Autonomous Vehicles and the Impact on Cities (Singularity University Global Summit)

Here’s a 20-minute talk I did at the Singularity University Global Summit last month. It’s a crash-course (no pun intended) on the different types of autonomous vehicles and use cases, the challenges that stand in the way of city-scale deployments, and ideas for how autonomous vehicles will transform cities, not just transportation systems.

Builds on ideas from these articles:

Advertisements

Policy Issues Facing Social Media Companies: The Case Study Of YouTube

One of the goals of www.techandpublicgood.com is to bridge the worlds of Government, tech and business, which often hold a degree of suspicion towards each other. This article dives deep into controversial policy issues surrounding social media companies.

As a case study, it elucidates the challenges, considerations and dilemmas behind YouTube’s policies. This is me, a Government policy-maker, putting myself in the shoes of a YouTube policy-maker. I figure our considerations are similar despite our different contexts. If you know better than me on any of these issues, feedback is much, much welcomed.

The Unexpected Responsibilities of Social Media Companies

We live in an increasingly divided world. The forces driving these divisions, for example, rising income inequality, geopolitical, racial and religious tensions, were in play long before the advent of social media.

However, social media has provided a channel for divisions to widen. Lowering the barriers for individuals to share and ‘viral’ their knowledge and opinions has brought tremendous benefits, such as spreading education and freedom of speech. On the other hand, it has given greater voice and reach to malicious or ‘fake’ content. Algorithms designed to push us to what we will most likely click create an echo chamber, reinforcing our beliefs and biases.

When a flurry of social media companies took to the scene in the 2000s, their intention was to create platforms for people to find what they wanted – friends, funny videos, relevant information, roommates or hobbyist items. Very few would have imagined that their platforms would completely change how everyday folks conversed and debated, shared and consumed information.

Policy issues facing social media companies

Today, social media companies are adjusting to the new responsibilities that this influence entails. Here is an overview of the issues at stake.

  1. Free speech and censorship

It is important to recognize the role of social media in democratizing how information is generated, shared and consumed. At the same time, not everything is appropriate to be shared online. Social media platforms recognize that they must have a moral view on harmful content that should be taken down, for example, content which aims to instigate violence or harm to others.

However, censorship cannot be overused. Social media platforms cannot become arbiters of morality because many issues are subjective, and it is not the platform’s role to make a judgment on who is right: The same LGBT content can be affirming for some, but offensive for others. When is it fake news, or merely a different interpretation? Here’s a real dilemma: let’s say someone reports an outbreak of disease on Facebook. The Government requests to take down the report until their investigations are completed because it will incite unnecessary fear in their population. Is Facebook best placed to assess who is right?

In general, a social media platform’s policy must identify and take down of content that is inherently harmful, while catering to subjectivity by providing choice – to users, on the content they receive, and to advertisers, on the content their brands are associated with. It is an intricate balance to strike, requiring nuanced, consistent policy backed up by a strong and coherent detection, enforcement and appeals regime.

  1. Copyright infringements

Another policy area surrounds copyright. Individuals sharing content online may inadvertently or intentionally infringe on others’ copyrights. On one level, better detection of copyright infringements is needed. YouTube invested $60m in a system called ContentID, which allows rights holders to give YouTube their content so that YouTube can identify where it is being used.

What to do about copyright infringements is another issue. Should they be taken down immediately, or should the platform provide choice to copyright owners? Paradigms have shifted over the years in recognition that copyright owners may have different preferences: to enforce a take down, seek royalties or take no action.

  1. Privacy

A third category of policy issues surrounds managing users’ privacy rights.

First, how can the platform generate advertising revenues and keep their user base engaged, while respecting different preferences for personal privacy? This typically pertains to the practice of combining personal information with search and click history to build up a profile of the user, which enables targeted advertising. Information is sometimes sold to third parties.

Second, what does it mean to give people true ‘choice’ when it comes to privacy? Many argue that long privacy agreements which do not give people a choice other than quit the app do not provide people a real choice in privacy.

Third, should individuals have the right to be forgotten online? The EU and Google have been in a lengthy court battle on the right of private citizens to make requests for search engines to delist incorrect, irrelevant or out of date information returned by an online search for their full name, not just in their country of residence but globally.

  1. Children

Children bring these policy issues into sharper focus based on notions of age-appropriateness, consent, manipulation and safety. Platforms like Facebook do not allow users below 13. YouTube introduced ‘Restricted Mode’ as well as YouTube Kids, which filter content more strictly than the regular platform.

Similarly, higher standards apply to children’s privacy. Should companies be allowed to build profiles on children, and potentially manipulate them at such a young age? Should people be allowed to remove posts they made or online information about them while they were children?

Safety for children is also a huge issue particularly on interactive platforms where children can be groomed by predators. Taking into account privacy considerations, how can we detect it before harm is inflicted, and what is the right course of action?

The YouTube Case Study

I have not scraped the bottom of the barrel on the range of policy issues that social media companies deal with, but the broad categories are in place. Now let’s get into specifics of how social media companies have answered these questions through policy, implementation and resource allocation.

To put some meat on this, here’s a quick case study of YouTube’s approach. There are at least four components:

  1. Product differentiation
  2. Enhancing user choice within existing products
  3. Closing the policy-implementation loop
  4. Strategic communications and advocacy

1. Product differentiation

Product differentiation is one way to cater to different appetites for content and privacy. In 2015, YouTube has launched ‘YouTube Kids’ which excludes violence, nudity, and vulgar language. It also provides higher privacy by default through features such as blocking children from posting content and viewing targeted ads, and enabling them to view content without having to sign up for an account. ‘YouTube Red’ offers advertisement-free viewing.

However, product differentiation has its limits because significant resources are required for customization. There is also a slippery slope to avoid: if YouTube rolled out “YouTube China” with far stricter content censorship, imagine the influx of country requests that would ensue!

2. Enhancing user choices within existing products

Providing users choice in their settings is another way to cater to varying preferences within a given product. For example, advertisers on YouTube may have varying appetites for types of videos their advertisements are shown against. Enabling choice, rather than banning more videos, is key: earlier this year, YouTube introduced features that enabled advertisers to exclude specific sites and channels from all of their AdWords for Video and Google Display Network campaigns, and manage brand safety settings across all their campaigns with a push of a button.

Concerning privacy, users who do not want their personal data and search/click history to be linked can go to the activity controls section of their account page on Google, and untick the box marked “Include Chrome browsing history and activity from websites and apps that use Google services”. For particular searches, you can also use “incognito mode”, which ensures that Chrome will not save your browsing history, cookies and site data, or information entered in forms. These are ways to provide real choices in privacy.

3. Closing the Policy-Implementation Loop

A robust policy defines clear principles which determine when content should be taken down or excluded from monetization opportunities and Restricted Mode. Implementation policy then becomes critical. With the large volume of content coming online every minute, it is impossible for YouTube employees to monitor everything. YouTube has to rely on user flagging and machine learning to identify copyright infringements or offensive content.

However, algorithms cannot be 100% accurate and often cannot explain why decisions are made. A robust appeals and re-evaluation process with humans in the loop is needed to ensure the integrity of the policy. More importantly, the human touch is needed to positively engage content producers (who hate to be censored).

In my previous jobs, we often quipped: “policy is ops”. It is no point having a perfect policy if enforcement and implementation simply cannot support it. Policy teams need a constant feedback loop with implementation teams, to bridge the ideal with the possible.

4. Strategic communications and advocacy

Finally, robust policy is necessary, but insufficient for social media companies. Strategic communications and advocacy are an absolute must.

  • Public criticism of a company’s policies can negatively impact business. Boycotts and greater Government regulation are examples. YouTube is swimming against a common but simplistic narrative that tech companies are simply trading of public interests in privacy and security for business interests such as the growth of advertising revenue.
  • Misperceptions about policies can also have dangerous impacts. A few years ago, Israel’s Deputy Foreign Minister met with YouTube executives, raising the issue of Palestinians leveraging YouTube videos to incite violence against Israel. She later released a statement which inaccurately suggested that Google would collaborate with Israel to take down this content. Google refuted this, but the nuance could have already been lost with segments of the public. YouTube’s policy of neutrality must come across clearly, even as lobby groups try to drag it into their agendas.

The purpose of Strategic Communications is to create a wide circle of advocates around YouTube’s policy stance so that negative press and misperceptions are less likely to take off. Elements of Strategic Communications include:

  • Going beyond the ‘what’ of policy, to the ‘why’. It is important to illuminate the consistent principles behind YouTube’s policy stances, as well as the considerations and trade-offs entailed. Channels such as blog posts enable this, since mainstream media is unlikely to provide the level of nuance needed.
  • Building strategic relationships and advocates. This includes entering into conversations and debates with your most strident critics, and building alliances with third parties who advocate your views.
  • Strong internal communications. Since social media companies themselves are run by an aggregation of people with different beliefs, it is essential that employees do not feel disenfranchised by the company’s policy stance.
  • Providing an alternative narrative. In addition, an important point for YouTube to make is that more is at stake than taking down offensive video content. Ultimately, we are all fighting against greater divisiveness and polarization in society. Although some elements of YouTube exacerbate this, YouTube can also make a huge dent in bridging divides.  Hence, I love what YouTube is doing with “Creators for Change”, a program that cultivates creators who aim to counter xenophobia, extremism and hate online. These creators are working on web series on controversial issues, as well as educational workshops for students. They are using the YouTube platform to close divides.

Conclusion

It is far too simplistic to say that companies only pursue business interests, leaving Governments to protect public interests. Every new product, including social media platforms, is a double-edged sword, with the potential to bring us closer to or further from where we want to be as a society.

Both Governments and Social Media companies are trying to push us towards the first scenario. However, Governments will tend to advocate for more conservative policies as their primary objective is to minimize downside on issues such as national security, privacy and Government legitimacy. On the other hand, private businesses are simultaneously managing downsides while pushing the boundaries on issues such as free speech and revenue generation models.

A natural tension between these two positions is healthy as we decide, as countries and global communities, where we collectively fall on issues. This is how democracy works, after all.

 

policyissuesyoutube
Source: http://www.thewindowsclub.com/ultrasurf-review-risk-blogging

Homelessness in the Silicon Valley: Is Inclusive Growth Impossible Here?

Last week, I was at the Singularity University Global Summit in downtown SF giving a presentation on Autonomous Vehicles. It was at the Hilton on Union Square. One lunch break, I hopped out for a bite. Turning the corner, I found a street lined with homeless people sleeping on cardboard. Waste filled streets and it smelled like a public toilet. It was jarring – the contrast between a high-energy, lavish tech conference solving “exponential problems”, and the poverty right at our doorstep.

Bringing it even closer to home, did you know that one-third of school children in East Palo Alto are homeless? They live in trailer parks and the back of cars with their families. This is happening just 10 minutes away from Palo Alto, Mountain View and Menlo Park, some of the richest districts in the world.

homelesschildren.jpg
Source: https://www.theguardian.com/society/2016/dec/28/silicon-valley-homeless-east-palo-alto-california-schools#img-1

The incredible wealth in the Valley has raised some troubling societal issues. Daniel Saver, senior staff attorney in charge of the housing program at Community Legal Services in East Palo Alto, has dealt with many cases of tenants receiving massive unanticipated rent increases — often of $400-$600 dollars a month, or even up to $1,000 or $1,200 a month. This is a functional eviction notice for many.

Even the highest paid tech workers are not spared, but the burden has disproportionately fallen on middle to low-income service workers – cooks, cleaners, security officers in tech companies – where minority races (African-American, Latino) are over-represented. Teachers, nurses and other service professionals are also affected because their salaries can’t keep pace with the housing prices.

“Inclusive” and “Growth”: Can we have both?

The problem I highlighted above isn’t particularly a “tech” or “Silicon Valley” problem, although it certainly is exaggerated here. Any region undergoing rapid growth experiences a surge in demand for services and infrastructure (such as homes, healthcare and roads). When infrastructure growth can’t catch up, people at the lower end of the income spectrum are priced out. In the case of the Silicon Valley, they move further out and commute to work, or live in trailer parks. Some leave the region altogether. In this way, the Silicon Valley has become an exclusive bubble of wealth.

Singapore’s earliest leaders understood the trade-off between growth and inclusiveness acutely. They knew that the problem I outlined above would be many orders worse in an island whose total land area is half the size of Los Angeles, with little room to expand: in large countries like America, people who are priced out of one region can move to a cheaper region. People make their money in one state and retire in another. A large land area is a natural buffer against economic upheaval.

This was not an option for Singapore: our little island needed rapid economic growth to stand a chance for survival. At the same time, we couldn’t afford to push people out when the cost of living increased. We had to remain a comfortable home for people at all life stages and all incomes across many cycles of economic change.

Four social policies served as bastions for inclusiveness as our economy grew:

  • First: our housing policy. 80% of all Singaporeans live in public housing built by the Government. Families earning below S$170K a year (about USD$115K) are eligible for public housing. Public housing in Singapore is very different from how Americans imagine: They are not gray, dingy rental facilities serving low-income neighborhoods. Apartment blocks are modern and undergo periodic regeneration. Urban planners design each public housing estate to include libraries, parks, common spaces, transportation networks and schools. Our public housing is highly subsidized, with lower-income families receiving higher subsidies. This policy keeps homes affordable for the large majority of the population.

 

  • Second, our healthcare policy. You can read about it comprehensively in this New York Times article, but I will point out one aspect: universal healthcare insurance. Instead of leaving insurance completely to free market providers – which potentially prices people out of this critical good – the Singapore Government provides a basic layer of healthcare insurance for all Singaporeans, called “Medishield”. Singaporeans are free to buy additional plans and riders from private insurers, but these are built on top of the basic, universal medical insurance.

 

  • Third, our education policy. We have a universal education system covering ages 7-16. Education is almost free. Schools are centrally resourced, not by the tax districts they are in.

 

Singapore’s social policies are not perfect. There are many issues we are reviewing, some of which I worked on prior to my current job. However, our approach demonstrates an active and systematic attempt to tackle the trade-off between economic growth and inclusiveness – I have not seen the equivalent in the United States.

How can the Valley achieve Inclusive Growth?

The United States has a very different context. The Government has not traditionally played a large role in social policy, and there is great political resistance to a change in this direction (for example, the attempt to repeal Obamacare).

Who will step in to fill this large gap in basic public services? I’ve always admired Americans’ ability to self-organize and provide for the needs of their community, which Daniel Saver is doing through his work in East Palo Alto. However, the magnitude of the problem – especially in the Silicon Valley – calls for someone to take more radical responsibility in ensuring basic services for the local community.

I believe technology companies can, and should take on greater responsibility to demonstrate that inclusive growth is possible. Much like how they might form a “Partnership in AI” to recommend rules and ethics in making socially-responsible AI, I believe they can come together to discuss how they may systematically contribute to inclusive development in their local backyard.

  • Could the technology companies provide subsidized housing in their own backyards? Facebook plans to build a new campus that will offer 1500 apartments at subsidized rent to the public. It’s a great step, but very small in the grand scheme. Perhaps they can commit to providing some subsidized housing for every X sqft of new development (the local governments should commit to opening new land for this too).
  • Can the large army of contract workers be offered better healthcare insurance and retirement benefits?
  • Can we help to invest in educational districts that are traditionally under-resourced?
  • Can we contribute to the thriving of the teaching, nursing, and social services community in the Bay Area?

Conclusion 

The problems in the Valley are certainly not caused by the technology companies alone. Failing infrastructure, outdated policies and politics are a huge part of the problem. However, tech companies are becoming more powerful and rich than many states today, and it is worth asking what new responsibilities come with that.

Tech companies have made exceptional contributions to worldwide causes – from education to hunger and healthcare. I would like to see them applying their tremendous intellect and resources to problems in their own backyard. Perhaps we can make the Silicon Valley an example of inclusive growth, rather than a picture of super fast growth plus ugly inequality. Now, isn’t that something we want to scale throughout the world? It would give many countries and people a greater hope as they seek to emulate us.

homelessinSF.jpg
Source: http://www.businessinsider.com/silicon-valleys-homelessness-problem-2014-3

Tackling Job Displacement at Scale: My Ideal Solution

Job displacement is not new, but the scale and speed will increase in the coming years – a 2013 study by Frey and Osborne suggested that 47% of workers in America held jobs with a high risk of potential automation.

A few months ago, I wrote “Tackling AI-Driven Job Displacement: A Primer”, which argued that current avenues for people to retrain and find new jobs appeal mainly to those who are already motivated. When changing jobs is a choice, we don’t have a problem. However, when changing jobs is a necessity – which it will be as the speed of job displacement increases – we need the whole population to be motivated and proactive about re-skilling and finding new jobs.

How can we achieve this? For countries, this will be one of the defining challenges of our generation. If we cannot get a large proportion of our population to continuously re-skill to fit emerging jobs, we will face economic slowdown, increasing unemployment and most probably political upheaval.

In this article, I give a big picture of my ideal future, some of the outstanding efforts by tech companies and Governments, and the two big challenges that require collaboration.

My Ideal Future: The Big Picture

The job market today is wrought with inefficiencies which create barriers for job-seekers and employers.

  • For individuals, the job search is intimidating because most of us do not have a good understanding of our skills, and how these map onto other jobs. There is also little incentive to obtain new skills if the link between these skills and future employment seems tenuous. It is a simple cost-benefit analysis.
  • Employers are similarly in the dark. They can pay exorbitantly for skills that seem to be in limited supply when they unaware of people with adjacent skill-sets who could easily fill those roles with some investment in training. According to the Manpower Group, 46% of US employers reported that they had difficulty filling jobs.
  • Education providers can profit greatly from the lack of transparency, when people are willing to pay for credentials that have no bearing on employment outcomes. On the other hand, they may struggle to reach a wider audience because only motivated people are seeking out their services. Adult education providers suffer here.

Imagine a different future with me for a few seconds.

If you are a regular person who wants to stay employed

Imagine a day when making plans for your next job is as much a part of your life as making plans for your next purchase: the barriers to your job search are so low.

  • Just as you have a bank account, you have a skills account which elucidates all the skills you have accumulated through your education and previous jobs. These include self-reported skills, as well as skills that are verified by an authority that has studied the relationship between job descriptions and skills across sectors.
  • Just as Facebook and Google push you advertisements based on your click history, you are consistently notified about emerging jobs with a close skills match to your account.Good morning, we noticed that these jobs, which match your skillset, are trending. Salaries in these jobs are 5% higher than your current salary”.
  • Suppose these jobs are sorted by degree of match to your existing skillset – 90%, 80%, 70%. You click on one and get an assessment of the best next steps – “Hi Karen, looks like you’re an 80% match to this job, and people with your profile have successfully entered this job by [taking this course] [doing a side project in this area] [taking a 1-month internship with X company]. Click to proceed”.
  • You are also notified if your job is at risk, and nudged to take action. “Based on trends, your ‘risk modelling’ skill is increasingly automated by companies. Job listings for this are are decreasing at a rate of 5% a year and this is expected to speed up. Here are some adjacent skills you should pick up ASAP.”

When it’s so easy, won’t more people start to inculcate this as part of their lifestyle?

If you are an employer

Now imagine you are an employer. You need the right talent to serve emerging needs and want to access this talent, fast. Imagine if you not only had access to candidates who applied for their jobs, but candidates who had a >80% match to needed skill-sets, and could be a perfect fit if they underwent prescribed training or certification. You could reach out to these individuals and nudge them towards this, or sponsor training where it makes economic sense.

Rather than firing staff when functions are automated, you may prefer to move them into emerging jobs within the company, as long as the cost is not prohibitive. What if you could assess the degree of match between a potential lay-off with all the emerging jobs in your organization? This could help you make a choice on whether to invest to train the potential lay-off for these roles, or to favour of a new hire.

The information and incentives are better aligned to help you invest in training, rather than to go through rapid cycles of hiring and firing, which could undermine the fabric of your company.

If you are an education or training provider

Now imagine you are an education or training provider. When your courses are not standalone units but part of a pathway that quantifiably increases someone’s chances at securing an emerging job, your user base will likely increase. With more employers investing in education and skills training, your revenue also increases.

If you are a country Government

Now imagine you are a country government who has realized that the traditional model of centralized labour force planning is far too slow for the rate of job displacement. You can breathe easy because there is no longer a need for this outdated method. Individuals are motivated to reskill by good information and clear outcomes. Employers are willing to invest in training that will bring them the skills they need. Training providers fill real gaps and less people are overspending on education that yields little outcomes. The market is finally working – not just for the motivated few, but for your whole population.

Existing Efforts by Tech Companies are Laudable, but Insufficient

What I have laid out is pretty ambitious. It requires:

  • A closed loop between individuals, training providers and employers (graph below). Having a common language to describe skills and jobs lies at the core – if we continue to describe the same skills and jobs in different words, employers do not know the full extent of their candidate pool, job-seekers do not know where they fit, and educational institutions do not know how their programs really help. It’s the wild, wild west.
  • User-centric design that caters for the full range of our population: from the highly-motivated time-rich youth, to time-strapped parents and people who have worked in the same industry for decades. Some may not even use the internet. Employers similarly have to come on board – from the large multinationals to our mom and pop shops.

skillsarticle

Several companies have already been working on parts of this puzzle. For example:

Google for Jobs, announced in June this year, leverages its powerful search engine to pull jobs which are most relevant to job searchers. To achieve this, Google’s powerful machine-learning model normalizes jobs and skills which are described very differently by employers and users. For example, if you search for “collections call centre job”, google will be able to flag out all the jobs that you will likely be interested in, even those that include none of your search terms, e.g. “Revenue Management and Care Representative.”

In my opinion, no one is better placed than Google to do this because of their experience with relational models, which group relevant search terms and results. Relational models form the fundamental basis for how Google yields relevant results even when you are not sure how to search for it. I wrote about how Google uses relational models in health search here, which has strong parallels to the job search.

Linkedin is well-positioned to create a closed loop between job-seekers, training providers and employers because all these players are in their platform. Linkedin helps individuals develop their skills portfolios through self-reporting and endorsements. It is also starting to map individual skills and jobs. If you have a premium subscription, you can see how your skills compare to other candidates and the percentile of applicants you fall into based on your Linkedin profile (example below). With the acquisition of Lynda.com, Linkedin is increasingly able to recommend courses to help people up-skill towards their desired job outcomes. To provide greater transparency to job seekers and employers, Linkedin’s Economic Graph team is also mapping the demand and supply of various skills across the world. However, Linkedin’s current business model is expensive for users, which could exclude a large part of the population and limit the data it collects.

linkedinpic
Linkedin: initial efforts at mapping skills to jobs

Start-ups are also playing important roles in this ecosystem. Just two examples:

  • Interviewed, an SF-based start-up, helps to close the loop between skills training and jobs. It started out by successfully developing a range of assessments to help employers assess the skills of candidates. It has since developed platforms such as Rightskill, where employers commit to hiring or at least interviewing people who obtain a certain score on hosted course. MooCs like Udacity have also made strides to closely link skills training to job placements – an essential move if they want to motivate a wider population.
  • JobKred, a Singapore-based start-up, uses predictive analytics and data science to connect job seekers to their best opportunities, recommend personalised learning plans, and help employers zero in on their top prospects. They provide a customised user experience in the job search, making the job search less frightening.

Two Big Challenges Ahead

The creativity and experience of these companies will go a long way to achieving my ideal future. However, there are two big challenges ahead that I hope companies, Governments and non-profits will tackle together.

First, aligning the way that everyone describes skills.

Google has made leaps in working out the objective relationship between skills and jobs on the back-end. Their relational model is based on proprietary occupational and skills ontologies that they built in-house.

  • Google’s occupational ontology gives a top-down understanding of 1,100 job families, a task typically undertaken by Governments.
  • Google’s skills ontology maps out 50,000 hard and soft skills and the relationships between these skills.
  • The skills and jobs ontologies are then mapped onto each other to paint an objective picture of the hard and soft skills required in each job, regardless of how they are described.

This is an amazing first step – let’s just take a moment to appreciate it. The fact that they make their relational models available through the Google Cloud Jobs API is even more amazing.

However, I believe these ontologies should not be kept on the back-end, used only when someone conducts an internet search for jobs. Many aspects of the job and education journey take place offline. Constraining the benefits to people who make proactive internet searches may miss out on a huge swathe of potential job-seekers.

Public and non-profit organizations should help with nudging individuals, employers and training institutions towards describing skills and jobs in the same language – perhaps Google can consider working with Linkedin, to combine Google’s job and skills ontologies with Linkedin’s data on self-reported skills, and make these a shared resource. However, the business model must make sense for the companies. Instead of users, Governments, charities or philanthropists should pay as this is a public good.

To reach the widest population, we also need to proactively help individuals populate and manage their skills accounts, just as they manage their bank accounts. UX designers need to work on interfaces that give people a bias to action. I covered some ideas in my “ideal future” section.

Second, bringing users on at scale to enable powerful Artificial Intelligence.

AI will play a huge role in enabling my ideal future: it underpins recommender systems and relational models, just to name a few areas. In turn, these AI-powered systems are enabled by data. Here we face a chicken-and-egg issue: unless we bring on the wider population to use the system, the software will not be able to serve the wider population. To collect data, we need to bring employers, individuals and educational institutions onto this system at scale.

This is an area where technology companies will benefit from the help of public and non-profit institutions. Singapore’s decision to give every citizen $500 for skills training and to create a unified skills and jobs database for individuals is a first step towards bringing a larger segment of the population on board. For segments of the population who are not digitally savvy, we need humans to come alongside and help them get onto the system.

Final thoughts

My ideal future is one where everyone – not just the highly motivated, time-rich or digital savvy – is empowered to continually update their skills and move to emerging jobs, rather than get left behind in the wave of job displacement. Finding your next job must be made as easy as finding the next place you want to eat at.

In all likelihood, this vision will not come through 100%. New safety nets must be put in place for people who simply cannot find new jobs. However, I believe that keeping people in jobs must be our utmost priority: not only are jobs the best safety net, there is also something dignifying about work that I do not believe can be replaced by a handout.

The goal of motivating a population at scale and overcoming inefficiencies in the job market is an area ripe for solutions by technology companies. I am encouraged that many – from conglomerates to start-ups – are working hard to achieve this vision. At the same time, I want see more collaborations that will enable large segments of our population to benefit. I would love to hear your thoughts – what would you like to see in this space?

 

 

Smart Cities Connect 2017 – Interview and Takeaways

I was in Austin, Texas, in June to represent Singapore at Smart Cities Connect 2017. I  participated in a panel on Data and Networks with the CIO/Chief Data Architects of San Jose, Orlando and Austin; served as a reviewer for eight Urban Mobility Start-up pitches, and had incredible side-meetings with CTO/CIOs across America. In an interview with Chelsea Collier, Smart City Connect’s Editor-at-Large, I shared some takeaways:

An Interview with Karen Tay, Smart Nation Director, Singapore

By: Chelsea Collier, Smart Cities Connect 

Chelsea Collier [CC]: I’m so happy to have you here at Smart Cities Connect.

Karen Tay [KT]: Thank you, I’m glad to be in your city after you visited Singapore a few months ago.

CC: I was so blown away not only by what you all are doing, but how you’re doing it, and how intentional and collaborative everyone in the government proper is. I was very very inspired by what I saw there.

KT: Thank you, yes it’s not without challenges. I think one of my takeaways from this conference is that we all face the same challenges, and part of it is organizational: how we are set up in a way that gets all the different domains to collaborate on Smart City Projects. That is not something that comes naturally because we are so used to working in silos. By setting up a smart city team (typically within the CIO or CTO’s office), actually many cities in the U.S. are doing similar things to Singapore. 

CC: Good, and I’m so excited that there’s so much progress being made just in the past year. This is the second time we’ve done this conference, the first time was in June here in Austin in 2016, and just in that span of one year I’ve seen so many cities go from intention, and more of an ethereal concept to really launching into strategic conversations and into pilots, and talking about scaling. So I’m impressed by how quickly it’s moving. It might not feel that way on the city side because you’re there day in and day out but from the outside world it’s really exciting.

KT: Definitely and I think it’s also driven by compelling use cases. One of the great people I met at this conference was Rosa Akhtarkhavari, the Orlando CIO. She talked about how the Orlando shootings really brought to the fore some of the technology needs that needed to be met and how they’re now going to build video analytics capabilities. I think it’s always driven by the use case. You cannot build too far ahead without the use case in mind. And I think that’s what driving the speed of progress.

[NB: during the Orlando nightclub shooting in 2016, Rosa’s ideal scenario was if she could pump the secondary shooter’s picture into the system, and analytics at the edge could flag out which locations the secondary shooter was seen. (Rather than to bring all the video feeds in and analyze in the cloud/data center, which would slow things down). However, she found that current capabilities did not allow for that.]

CC: Perfect. So any big lessons learned these past couple of days, or what you’ve learned that can benefit you back at work?

KT: I think one of the dominant themes of a smart city conference in the U.S. is how are you going to pay for this digital infrastructure.

In Singapore we are prudent with how we spend but I think we are fortunate that we have the resources to build some of these things. But what I really took away is that even if you have the resources, you have to have discipline of thinking about the economics of it.

I really appreciated the discussions like Chicago collaborating with the university, or San Jose working with a company that’s willing to sponsor a lot of these sensor deployments, or even companies like Civic Connect, which are saying “well, our funders are okay for us to pay for this free of charge to the city as long as we have a business model which will reap the benefits”. I think in Singapore we will benefit a lot from thinking about this economic discipline, just as the U.S. is forced to do. I think that was one of my main takeaways.

However I also think that the government cannot run away from paying for some of these services. It cannot completely be left to the private sector because of this idea of digital inclusion. Fundamentally, the government needs to be able to ensure that use cases which will not yield economic returns will still be accounted for. Who else could do that in society? 

[Another key takeaway that I did not mention in the interview concerned privacy in smart cities. All cities – especially American – want to change the narrative that lumps “surveillance” with “data collection”. CIOs recognize that if there is the capacity to identify people for serious crimes such as terrorism, there is the capacity to identify anyone else. Hence the issue is not about limiting our capacity to do identify people through video footage, but ensuring predictability, accountability and transparency in how the data is used.

Cities need to give citizens utmost assurance in this regard. I believe Seattle has a good model to learn from: Seattle put in place a privacy “self review” process, where every department seeking to launch a technology solution has to undergo a “self review” according to the 6 privacy principles (which were established a few years ago). Any technology project that collects data also has to pass through the City Council’s approval. “At-risk” cases are flagged up by the city’s Chief Privacy Officer in the CTO’s office. She advises on precautions and typically pushes them to conduct a public consultation.]

CC: I think as the public sector and the private sector get more comfortable working together they can have strategic and very honest conversations about that and everybody can own their piece of it. And there’s just no time to waste, the problems aren’t getting smaller. They’re only escalating and technology has the potential to really help make some headway there.

KT: I think so. I think there are problems like homelessness, inequality, access to healthcare – all these are big problems waiting to be solved and technology can. It’s a matter of having those conversations.

CC: Perfect. So glad you’re here. Thanks for joining us.

KT: Thank you.

====

 

 

scc2017

Three harmful ideas about leadership (and shifts you can make)

Leadership and coaching has been a one of my side interests for the longest time. I recently wrote 3 Tips for Middle Managers in “Day 2” Organizations. This article goes a little deeper – challenging us not just to take on tips, but to fundamentally reorient our (often sub-conscious) mindsets.

Mark+Zuckerberg+Jeff+Weiner+CEO+Corporate+2am7izVF6uvl
What makes these guys great leaders? Source: Source: http://www.zimbio.com/photos/Mark+Zuckerberg/Jeff+Weiner a caption

What makes a great leader? Is it inherent?

The Silicon Valley is known for some really great leaders (and some very terrible ones – but that is a story for another day). CEOs like Mark Zuckerberg and Jeff Weiner are famous for creating highly productive workplaces – where people feel empowered to solve problems in creative ways and teams are more than the sum of their parts.

How did they become great leaders? Is the ability for good leadership somehow inherent to these individuals, or inherent to a particular breed of (young) (engineering-minded) people?

I don’t think so. Rather, I believe leaders like Zuckerberg and Weiner simply grasp what it takes to lead successful teams in the new economy, which can be characterized as a rapid series of disruptions whose timing and nature are difficult to predict. The skills-sets needed to help a company be continually successful are evolving faster than before.

Hence, in the new economy, good leadership is less and less defined by subject-area expertise, and more and more defined by the ability to hire well and create the conditions for talented individuals to propel the business forward, such as trust and autonomy.

Unspoken assumptions about good leadership held me back

Changing a leadership culture in incumbent organizations is arguably more difficult than setting up new ones like Facebook or Linkedin. I believe that more than anything, it is the subversive assumptions about good leadership that hold us back from adapting.

In the Asian context (the roots may trace to patriarchy), much of the subconscious narrative around “good leaders” centers around three characteristics:

  • Teachers, who impart years of experience in subject matter or organizational navigation to team members
  • Protectors, who shield their teams from the vicissitudes of the workplace so that they can focus on their tasks
  • Lonely heroes, who personally soak up the stress and always present a calm front to superiors, peers and team members

The problem with this definition is that it assumes a certain hierarchy in knowledge and ability that is inconsistent with the dynamic and evolving needs of the new economy. It drives leaders to limit, rather than unlock their team’s potential.

My Turning Point: Three Big Mindset Shifts

For a good 2.5 years of my leadership journey, I was unaware of that I held these beliefs. It was only when I attended a 5-week leadership training programme in 2015 that these assumptions were unearthed. The training included a 360 degree feedback exercise which 15 staff, 15 peers and 2 superiors filled in anonymously, group and individual coaching, and leadership simulations.

Through the course, I realized that I needed understand my role as a leader differently in order to truly unlock the potential of my team. Here are the three mental shifts I had to make:

  1. From Teacher to Coach

The first shift was to see myself as a coach, rather than a teacher. Most leaders feel safe when they know better than their teams. It is a natural way to garner respect and confidence from the team. When I started my first managerial position at 26 in a team that was older and more experienced, I constantly asked myself what areas I “knew better”, so that I could establish value by imparting some sort of wisdom. That was not a good move. This mindset made me unnecessarily (and subconsciously) controlling.

When leading a team, the right starting point is not I, but them. A leader who primarily sees himself as a coach believes in the potential for each member to bring some magic to the team which he cannot. This is more in line with the reality of the new economy, where what we need to know is rapidly evolving.

A coaching leader also understands that every member has both personal and work objectives when they arrive at the office. He gets to know these objectives and is committed to helping them achieve it. He acts as a mirror, a challenger and supporter, as the individuals pursue their objectives.

Grasping the shift from teacher to coach invigorated me. I started to see team development not just as “making the team better at their work (in the narrow way I defined ‘better’)” but unlocking the potential of every member. This opened up whole new spaces of interaction, especially with team members who had gained mastery at their work. We probed into issues such as helping a highly intelligent but quiet woman contribute more during debates, helping a team member change an overly confrontational communication style, and working through an unhealthy competitive dynamic between two members. I believe we grew individually and became a more productive team.

A coaching approach also set me free. Instead of trying to solve their problems, I saw clearly that my responsibility was to help them understand themselves and take steps towards their objectives, thereby maximizing the potential of my team without bearing unnecessary burdens.

  1. From Protector to Challenger and Collaborator

The second shift is moving away from a “protector” mindset, which can really hold your team back.

At a management course, I was asked to draw a picture representing my relationship with my team. I drew a picture of a sheep pen and shepherd. Very noble, one would think. And why would it be the wrong solution? My team gave positive feedback about my protector role: I made things clean, structured and efficient so they could deliver the outcomes. Navigated the politics on their behalf. Reading the 360 degree feedback, I felt like I was doing a good job at leading.

I had not realized that delivering on ‘work outcomes’ and having a happy team did not mean I was succeeding in unlocking their potential. My role was to prepare each team member for the next bound of leadership, not to keep them happy within the current role.

I needed to be generous enough to allow them to experience frustration, ambiguity and conflict. To give them a safe environment to face some of this messiness down themselves; to decide what was the right thing to do, how firmly to stand, how much compromise they were willing to make; to stay confident when people disagreed and made things personal. This is what it means to move from the role of a “protector” to “challenger”.

It also struck me that leaders need to move from the role of “protectors” to “collaborators”. One leadership simluation drove the point hone. Each one in our cohort of fourty was assigned a role in an imaginary organization – either a “Top”, “Middle” or “Bottom”. We had to maximize some outcome in a tight timeline (I believe it was about accumulating shoes…). Instructions would be given to the “Tops”, who would then hold meetings with “Middles”, who would execute the tasks with their teams. Every 20 minutes, we would pause and give each other feedback.

One of my friends who was a “Middle” faced a huge uprising from his team. They fed back that they could not trust him because he was not being transparent with them; he seemed to be concealing some instructions. He said that the instructions were changing so quickly and he wanted to establish some clarity before communicating with him. It struck me that when we hire top talent, they want agency, they want to be collaborators – not sheep that are protected. “We want to discuss as a team how to address ever-changing instructions, rather than have you hide some instructions from us to protect us.”

  1. From Lonely Hero to Highly Networked with Peers

My final shift involved understanding the value of being tightly networked with my peers. If you subconsciously see yourself as a protector and teacher, you will start to become isolated because you perceive your role as constantly “helping” and “giving”, absorbing difficulties for others, being a hero.

However, if you shift your perception of what good leadership entails: moving from teacher to coach; from protector to challenger and collaborator, you will start to see yourself as needing a community of people who can serve as your coaches, challengers and collaborators.

There is no better group to do this than your peers. Yet many of us neglect our peers as we spend time managing upwards and downwards. I remember a piece of feedback from several of my peers, which said, in gist: “Karen is super effective at bringing us together and helping to get cross-team projects done, but we wish she would tell us more about herself – what she thinks, what she likes, what she feels about issues.” I reflected on this feedback with my coach, and realized that I resisted being vulnerable with my peers because I believed I should not be a burden. It was an unhealthy belief not just for me, but evidently for my wider organization.

This insight, along with the intensive group coaching I went through with 5 peers during the course, made me understand that good leaders cannot be lonely heroes. We must be tightly networked with peers for accountability, challenge, support and perspective. As we seek to navigate a far more volatile and ambiguous economy, this has never been more important.

Conclusion

I’ve argued that the new economy demands a new style of leadership, but subconscious, subversive beliefs about what makes a good leader can severely hold us back.

Which of the three shifts: teacher to coach, protector to challenger and collaborator, lonely hero to networked – do you feel is holding you back?

Realizing this is a great first step to working through it and becoming a leader who can truly unlock the valuable human potential that is in your team, just as Facebook, Linkedin and Google do in theirs.

 

================

Afternote 1:

These principles do not apply equally to all jobs. For example, professional skills still require superiors to play a strong teaching role, although seniors need to be open to learning from younger doctors who might have a stronger grounding in technology. Another example is when the team is new and needs to be taught basic skills.

Furthermore, a focus on bringing out the potential of your team and being a good coach does not mean that you cannot be firm, especially with people who are not motivated, or whose goals are wildly disaligned with that of the organization. I have examples from my own experience, but that is a story for another day.

Afternote 2:

I have a strong interest in coaching and will be undergoing training in the next year. If you’d like to experience coaching, I can point you towards resources and possibly be your coach within the next few months once I’ve received my training.

 

 

 

3 Ways Technology Can Help Keep People in Work (Genevieve Ding)

We’ve heard it before: robots and AI are eating our jobs. New jobs will emerge, but we may not be equipped to do them. I’d written an overview of the issue here.

This week, Genevieve Ding gives an overview of how technology can help KEEP people in work, by mitigating job displacement. Gen speaks from a position of experience: over the past five years, she headed economic strategy in the Ministry of Finance, where her team introduced the SkillsFuture initiative in 2015. She currently works at the National Trade Union Congress (NTUC), where she is interacts with employers and workers to understand the employment landscape across the spectrum: old and young workers, blue collar and white collar jobs. She was previously in the Foreign Service for 4 years, and was posted to Beijing, reporting on all aspects of the Chinese economy.

Enjoy!

===

OlderWorker+UP2+226
Photo from ntuc.org.sg 

Technology and Job Displacement: Not a Foregone Conclusion

Auntie Sally is a union member at an electronics factory in Singapore producing wire bonds, the tiny wires in your mobile phone, tablet or step tracker that connect the semiconductor chip to its housing. A toothy lady with boundless energy and a knack for making you feel like her daughter, she has been in this job for 15 years. With no more than a primary school education, she began by operating the machines which make aluminium wire bonds, and has risen through the ranks to become a supervisor in her department. Recently, she was transferred to a new department, where she has to use software she does not understand.  Frustrated and under immense pressure, she feels lost and fears that she may soon be let go. Once fiercely proud, her sense of self tied up so closely to the value she used to provide at work is now deflating. Worse still, her company’s prospects are bleak because the process of wire bonding risks being entirely replaced by advances in materials engineering.

There have been many involved government policy discussions on how rapid technological disruptions and the progress of artificial intelligence will inevitably displace workers like Auntie Sally along the entire value chain, hollowing out the lower and middle classes.  The pace of disruptive technologies makes it ever more difficult to train workers fast enough to transition to new jobs and sectors.

Underlying this is a deep, visceral and very real fear that the diligent, dutiful employee has of losing his job. The US elections and Brexit last year and the ongoing protests against Uber in part reflect this widespread, deep-rooted fear. The sheer potential and associated impact of disruptive technology could trigger the same fears that offshoring has, driving an even deeper divisive cleave between proponents who are able to extract benefit from it, and those who fear they will lose out.

There is plenty of literature on how we can partner technology to improve productivity that make jobs easier, but risk displacing workers, whether by hovercraft technology to reduce the need for painful manual work; collaborative robots to increase productivity, data mining programmes such as Verifi and Margin Matrix that perform time-consuming routine research and drafting so lawyers can focus on more complex and meaningful work; or smart manufacturing systems that use predictive data analytics to increase yield rates and optimise operations in manufacturing.

In a technologically-enabled nation, can technology be used to tackle some of the very problems arising from its development? So that workers are not mere spectators or recipients of help, but active players with a call to action, and who can see the reality of a better, more hopeful life?  Workers will be more invested in the adoption of emerging technology if they can see themselves jointly sharing in progress.

How can technology can play a role in mitigating job displacements and making workers more valuable? I suggest three ways.

1. Helping companies identify their skills needs

As technology evolves, the skills required in the workplace to enable companies to keep ahead are evolving more quickly than ever. The biggest challenge for the labour movement or employment agencies is how keep up with the rapid change in the skills profile of their workforce.

What was somewhat surprising to me when speaking with companies, is that they too are often feeling around in the dark for skills they will need in the future, before suddenly realising that they’re behind — leaving too short a runway to train workers. Data science for instance was a skill which was suddenly hot, prompting companies to scramble to develop expertise. But for some time many didn’t really understand how data science would add real value and what specific skills were required, so job descriptions for data scientists had grandiose expectations looking for “unicorns” that were almost impossible to fill.

Technology has a role to play in helping both government and companies, especially smaller ones, better anticipate the changing skillsets needed to remain competitive.  Data analysis has the potential to trawl through thousands of online job descriptions to fish out skills that are trending in particular sectors, or even identify emerging skills across sectors which may give rise to new niche areas of growth. Once identified, companies are better able to start training workers as soon as possible before their old skills become outmoded.

2. Matching skills, eliminating biases

Just as Tinder and Coffee Meets Bagel help people find their other halves based on users’ preferences and profiles for a better fit, finding good matches between skills and skills in demand is essential to helping workers.

Algorithms, not unlike those that help you find a compatible date, have the potential to match job seekers to jobs based on skills, interests, aspirations, and cultural fit. At the same time, algorithms can help workers identify skills gaps in their resumes based on the skills most in demand or trending in job descriptions, helping them to identify training opportunities.

Digital labour platforms like LinkedIn or CareerBuilder also create more transparent job markets and disrupt previously closed labour markets by increasing workers’ access to a wider variety of jobs and employers’ access to a wider pool of job seekers, reducing the advantage of “old boys clubs”, often driven by wealth and connections.

The playing field is levelled even further by technology platforms that attenuate hiring biases such as paper qualifications and gender, by enabling testing for the specific aptitudes required on the job. Platforms like Codility and GitHub help employers seek out and test for quality of coding and development skills, not certifications.  Catalyst DevWorks’ Catalyst Talent Platform uses machine learning on thousands of variables from hundreds of thousands of individual engineer and developer candidates to identify innate capabilities and predict whether someone will be exceptional talent in the job, whether or not they have a degree or a good resume.

3. Real-time, real-world training

Lifelong learning is much easier said than done. Massive open online courses (MOOCs) have already democratised learning, providing easy access to countless new courses and possibilities. However, much of this learning remains theoretical and does not train or test  “on-the-job”, so it is less useful for industries such as manufacturing.

Even more depressingly, upskilling won’t get you a job. I’ve had the unenviable position of speaking to a electronics engineer Mr. Lee who was retrenched. In tears, he related how he tried to take professional courses in the biomedical sector, with hopes of entering what was then one of Singapore’s growth sectors. Despite his burnished qualifications, all the companies he approached felt that he didn’t have the job experience commensurate with someone else his age in the industry.

Virtual and augmented reality (VR and AR) open up new possibilities of providing “on-site” ,  “hands on”  training for workers and might provide a solution to learning that accelerates job transition and meaningful skills acquisition throughout one’s life.

In manufacturing for instance, AR smart glasses that overlay computer-generated graphics and real-time instructions can improve productivity without prior training. This will shorten the time required for onboarding new workers and help close skills gaps.

Significantly, these upskilling technologies can also help companies “test” out potential employees during the hiring process in a simulated environment, assuring them that the job seeker – even if, like Mr Lee, did not have prior work experience – can perform to standard. Real-time, real-world training with AR will also workers help existing workers learn continuously and at an accelerated speed, increasing organisational learning agility.

Why is this so difficult? The challenge of scale

Using technology to help mitigate the impact of job displacements can only be really effective however if we can adopt them at scale. This can be challenging.  For instance, identifying in-demand skills across sectors or on a national level, or skills matching through data analytics will be most robust if there is open access to large volumes of  job offerings on the demand side. Markets are more transparent the larger the source data.

However, much of this information is fragmented across various platforms and job portals–with a significant proportion of hiring done through personal referrals or headhunters. National job portals where all employers are required to list job openings with job descriptions and skills needed–such as Singapore’s national online Jobs Bank–would go some way to address this. Google for Jobs, which was recently launched, will also contribute to this. 

Technology will also affect various constituents to differentiated degrees.  Eliminating biases through Codility or GitHub for example is limited to skills that are more quantifiable and thus demonstrable on a platform. Less quantifiable skills such as learning agility or strategic thinking may not be as easily evaluated through mediated platforms.

Last, technologies such as VR and AR for training are most impactful if they can both be customised and scaled up. Cost constraints and access to these technologies in the near-term will limit their scalability. Addressing these challenges  in-depth is certainly worth a separate discussion.

Conclusion: Technology as a force for social resilience and collective progress

Challenges notwithstanding, by deliberately harnessing technology in these ways, we are negotiating a new narrative: one that empowers workers and shows them that they too have a stake in our collective progress. Technology no longer divides, but instead buttresses society’s resilience. It provides Auntie Sally a vision of progress that she can once again take pride in contributing to.

 

3 Tips for Middle Managers in “Day 2” Organizations

with my team at MOE, who taught me so much about leading well – I’m still learning!

Everyone wants it to be “Day 1”
Jeff Bezos’s 2017 letter to Amazon Shareholders had some piercing insights about running a “Day 1” organization. He doesn’t exactly define Day 1, but the concept is clear when he describes a “Day 2” organization: “Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

There are many gems on running an innovative organization, but I’ll highlight two:

  • First, a Day 1 organization never lets itself be owned by process. Processes exist to serve an outcome – following the process should never be the outcome. “The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.”
  • Second, a Day 1 organization masters the cycle of rapid decision-making, prototyping, failing and trying again. Jeff urges his people to take action based on 70% of information they would ideally have, and to be willing to disagree and commit (rather than disagree and grudgingly assent): it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” 

I recently shared a part of Jeff’s letter on Linkedin, and the responses suggested two things:

  • Many look upon tech companies like Amazon with envy, because their organizations seem so “Day 2” in comparison
  • Most (if not all) of us want our organizations to be “Day 1”

What if my organization is already Day 2?

What do I do if my organization is already Day 2? This question is important to the area of “tech and public good”, the subject of my blog (www.techandpublicgood.com).

Why? Because the main reason organizations (Governments, civil society and private sector alike) fail to harness technology – or for that matter any type of innovative practice – that will clearly improve their customer experience and operations is internal resistance. In other words, it is because they are in “Day 2”.

It is often not deliberate resistance. It is a slow, painful death, precipitated by adherence to process and status-quo practices, and a lack of clear ownership for the outcome.

When it comes to building innovative organizations, “it’s about the people, it’s really not about technology” – I can’t agree more with Greg Godbout, co-founder of 18F, who said this in a recent Harvard Business School interview.

People matter, and in my experience, the level of innovation in an organization depends on the behaviours of middle managers. Middle managers are some of the most powerful influencers in an organization – they set the tone for their teams’ culture, and can almost singlehandedly dampen or stimulate innovative behaviour among a large majority of your workforce.

Forces that make middle managers susceptible to “Day 2” behaviours

Unfortunately, I have noticed that when people transition from “team member” to “middle manager”, gravity seems to pull them towards being “upholders” rather than “innovators”.

This could be the result of coping mechanisms to deal with increased responsibility. When promoted from member to manager, one takes on a multitude of new objectives. In addition to the original objective they were hired to achieve, they have to help their team navigate relationships with other departments, obtain resources and gain the confidence of their bosses, manage HR issues, and prioritize their team’s bandwidth. Email load triples; more time is spent managing upwards, downwards and sideways; the appetite or bandwidth for innovations gets lost.

I worked in 5 teams as a member and middle manager over the years, and have seen the following traps among leaders:

  • Falling into a “process-orientation” in their leadership style to cope with volume. Simply ensuring that process is being followed can give some comfort that the team is on the right track, without having to commit too much mental bandwidth. There’s limited upside, but also limited downside.
  • Defining success by whether everyone (bosses, team members, fellow managers) is happy, and hence failing to challenge the status quo.
  • Feeling de-motivated. I have had friends lament that when they became middle managers, they felt removed from the real groundwork, but not high enough to influence decisions. They lost their motivation to challenge the status quo.

Three Important Questions for Middle Managers (and their bosses)

It would be a natural point for me to go into how an organisation should select, train and reward middle managers. In the past, such articles have left me feeling validated but disempowered, as it leaves the action to the organisation – and who knows how long it will take.

As middle managers, we have agency. I’d hence like to address middle managers who are aware that they are in a Day 2 organization and are possibly perpetuating Day 2 practices. What questions can you ask yourself, and have conversations with your boss and team about? Three tips:

First, where exactly am I creating new value for my organisation?

This seems like an obvious question, but we need to be very ruthless in holding ourselves to an answer. “Keeping the peace”, or “making my boss and team happy” are bad answers that busy people can easily default to.

What are your objectives and how are you adding to them? As a middle manager, you have a wider scope than a team member, and your value could come in a variety of forms, including:

  • Taking ownership of an issue that falls between departmental lines. Take on the “start-up cost” until everyone is on-board. For example, put together a short think-piece that outlines the issue you want to tackle, and why it would be beneficial all around if different teams to got involved. Use it as a starting point to rally people across departments, and eventually bring it to upper management for endorsement and resourcing. When I became a middle manager, I realized I was uniquely positioned to take the lead on such issues, as I had greater ability to cross inter-departmental lines.
  • Examining a process that does not work, and proposing an alternative. I read this lovely article on Linkedin about the prevalence of bad systems, and why they are allowed to persist. The problem is that bad systems often end up in a kind of corporate Bermuda Triangle — no one really monitors them; worse, one is empowered to change them when the need arises.” When you see a stupid process, don’t let it pass you by. I once had a boss who had this mentality of fixing bad systems. I admired her deeply. Our department had to manage hundreds of event invitations to our Ministers. For a long time, one person oversaw all these events because it was “the only way to not drop the ball”. It clearly drained her. My boss, our “czar of bad systems”, developed a tracking list which could be rotated among 3-4 people on a weekly basis, spreading the task around and allowing us to monitor follow-through far better than before.
  • Work on a development plan for your team members. HR is not a “good to do”, but an essential objective that should take up a middle manager’s work time. Everyone does it differently, but I had monthly check-ins with each team member, where we discussed where they needed to be challenged, or supported. I kept a file on their work record, training and future job aspirations. It certainly came in handy when I had to argue for their promotions and opportunities.

“But I don’t have the time!” So true. Something has to give. Most of your bosses will have a vague idea of what is important in your job (they’re too busy thinking about their own jobs), so you have to tell him/her. Proactively engage your boss on the 2-3 pieces of value you want to deliver, and why you think it is important. Use it as an opportunity to talk about what you will not be dedicating much bandwidth to i.e. I will not be responding as fast on x, y, z issues, or for x, y, z issues, my team members will report directly to you. If you are working for a boss worth his/her salt, you are guaranteed a good conversation and probably a green light. You will definitely feel more motivated.

Second, am I creating a team in my own image, or does each individual feel empowered?

Usually, people are promoted to management positions because they did well as a team member. The result can be a narcissistic impulse to make your team members perform in the same way that you do. It is also a key source of overwork – vetting and thinking about everything each team member does. I encourage middle managers, especially new ones, to break down their management job into at least two roles, and define their value differently for each.

  1. Roles where there is a legitimate need to uphold standards. In my experience, issues that fall into this category have included managing external relationships (such as a foreign university pulling out of a partnership with a Singaporean university, with implications on our students), sensitive complaints (such as allegations of sexual harassment by a Professor or teacher), or a reply to the Minister on a key policy issue (to ensure we make the best use of the Minister’s time). These are areas where a middle manager’s experience must be applied to help the team member make the right proposal.
  2. Roles where you provide one perspective, but let the team member fly. However, there are also issues where the team member should be allowed deep ownership. Proposing an overhaul of the preschool sector? Revamping communications materials for Principals following a policy announcement? Please proceed. I’ll give you my perspective, but it is up to you what you propose to our boss. In one of my favorite jobs, my bosses were very conscious about giving us autonomy. Ministers and even the Deputy Prime Minister would call the “lowest level” staff directly, and we never felt hesitant to make our arguments.

Again, be clear to your own boss how you see your role in the different projects under you. It helps to be transparent with your team members about this too, so that they can hold you accountable if you become a micro-manager or if you’re not providing enough guidance. I’ve treasured the times my team members have said “hey Karen, we need to talk about your involvement in this project.”

If you get this right, you will also be better able to manage your time. Type 1 issues will command more of your bandwidth than Type 2. Ultimately, you want to train your team to the point where your role is largely Type 2. The more your team can fly, the more bandwidth you have. Both you and them will feel more motivated when you have that autonomy.

Third, how often do I disagree and commit, compared to having my team disagree and commit?

In his article, Jeff Bezos gave an example about how he strongly disagreed with a team’s proposal, and though their discussions did not change his mind, he committed strongly to their proposal. I was impressed – in typical organizations, the bulk of “disagreeing and committing” is by team members, not team leaders.

As middle managers or leaders of any form, we should ask ourselves how often we disagree and commit to our team members, compared to the other way around. It is a reflection of how empowered our teams are (and hence how well we are using the precious human resources we hire). It is also a proxy for how innovative our team’s culture is.

Conclusion

If you feel like you work in a “Day 2” organization, I believe you have the agency to bring about change, and I hope that by reflecting on these three questions, you will have some idea of how to push your team towards “Day 1”. I write about this as a fellow learner in the journey towards good leadership. Many of these lessons were the result of making painful mistakes, observing others’ strengths and weaknesses, getting feedback from my bosses and team and reflecting deeply on my own practice of leadership. I would love to hear your experiences as well.

Interview with Bert Kaufman, Head of Policy and Regulation @ Zoox (Self Driving Cars)

Today, I’d like to introduce you to my friend Bert Kaufman, Head of Corporate and Regulatory Affairs at Zoox, one of the hottest self-driving car start-ups in the Valley.  I’ve previously written about why tech companies need policy teams more than ever, and Bert’s work is at the forefront of this.

When I first met Bert, we had already heard of each other, and immediately hit it off. In addition to being extremely kind and generous, Bert is a killer combination of a big-picture, systems thinker – from his days in Washington – and an embodiment of the generous, action-oriented, and creative start-up culture in the Valley, where he currently works.

In this interview, I ask him about his transition from Washington to Silicon Valley, issues surrounding self-driving, what he wishes Government folks knew, and how else he thinks technology should be harnessed for public good.

This is my third profile piece on folks who work at the intersection of tech and public good, following Xinwei Ngiam and Kenneth Tay. Enjoy!

 

bk.headshot

  1. Tell us a little more about yourself. Why did you move to the Valley after almost 8 years in Washington?  

I spent most of my time growing up on the East Coast and down South, so from a cultural standpoint, I always thought Washington was a great mix of north and south—“The Northern most Southern city.” From a professional standpoint, I am a lawyer who loves policy issues, so I gravitated towards Washington after law school. But what I discovered about myself over the past decade is that I really love building organizations and organizing initiatives around good ideas. And there is no better place in the world to build these things than the capital of innovation and entrepreneurship. My move out to the Bay Area was prompted by my fiancée who was in graduate school at Stanford, and because my role as an appointee in the Obama Administration was winding down.

  1. What did you enjoy most about your job in Washington and how did it prepare you for your current role at an autonomous vehicle startup?

Before joining the Obama Administration in 2013, I spent five years growing an organization called Business Forward. We started Business Forward to help business leaders from across the country do a better job of advising Washington policymakers and, conversely, to make it more efficient and transparent for policymakers to listen to business leaders. Through that experience, I faced the challenges of building an organization from scratch and learned the importance of taking a long-term view. Our funding came from about 60 large companies—our members—and I traveled around the country, engaged with thousands of people, and learned about issues that businesses of all shapes, sizes and ages faced as the country emerged out of the 2008-09 financial crisis.

That experience prepared me for the chance to join Penny Pritzker’s team at the Commerce Department. Not only did I get to work for an incredibly brilliant, demanding, and hard-working leader in Secretary Pritzker, but I also had the opportunity to help build and manage an initiative we created called the Presidential Ambassadors for Global Entrepreneurship. This initiative worked across The White House, Commerce and State Departments, USAID, the Small Business Administration, NASA, and with some of America’s most successful entrepreneurs to mentor, motivate, and in some cases fund aspiring entrepreneurs from across the U.S. and around the world. I also worked on policy issues related to the digital economy on areas like data privacy and cybersecurity.

In my role now, as an in-house lawyer working on policy in a sea of engineers and computer scientists, it’s important to communicate clearly and to understand the policy implications of the technology that we’re developing. This is important both within my organization and externally. Transportation is a highly-regulated space, for many important reasons. As a society, we want people to move around freely, but we also want to ensure that they can do so safely. The advent of autonomous vehicles will lead to innovation in road safety. What we are doing is so new that we have the opportunity to create best practices that can set the bar for future policy.

Policy is really important to any technology business intersecting with regulated markets. Technology startups that fail to consider policy or regulatory implications do so at their own peril. Conversely, regulators need to understand that the regulations should be nimble, flexible, and fair and not cumbersome. These principles will allow technology to advance on a level playing field.

  1. What is one thing you’ve learned or experienced that you wish your colleagues in Washington had a chance to?

Meaningful innovation is hard and takes time, so it is important to take a long view. Government can and should catalyze and support innovation through funding basic and applied research and challenge grants. Government should set ambitious policy goals while at the same time leaving innovation to the private sector.

For example, between 2004 and 2007, DARPA (the Defense Advanced Research Projects Agency) set out some “Moonshot-like” challenges and put forth a modest amount of prize money for autonomous vehicle-related technology. Today, the payoffs are huge. The teams that competed in those challenges are the fathers and mothers of all the autonomous driving R&D now taking place across the entire automotive industry. In other words, a series of small government challenges have generated an enormous amount of private sector investment and job creation. Two lessons here: the first is that a little can go a very long way; the second is that government set a goal, got out of the way, and let academia and the private sector drive the evolution of the space.

  1. Self Driving Car technology is one of the hottest areas in the Valley. What are a few things the international community should know about Self Driving Cars?

Three points here:

  • First, autonomous technology will usher in a paradigm shift as large as when we transitioned from the age of the horse and carriage to the age of the automobile. Getting around will allow for increased productivity, and for people who live in areas with poor access to public transit, it could make it easier to access jobs and opportunities. We will also think about real estate differently. For example, much of real estate today is built for and around the automobile. Think parking lots and parking garages. In a world of shared autonomous vehicles, demand for parking decreases.
  • Second, the first rollouts will happen in cities in a ridesharing model, not in vehicles sold to end customers. Cities can benefit from shared electric, autonomous transportation because it will ease congestion and decrease pollution. As more people move into cities, the idea of individual car ownership becomes less tenable. In this model, liability shifts away form individuals towards fleet managers and manufacturers.
  • Finally, and most importantly, safety is paramount. In the U.S., more than 35,000 people are killed every year in automobile collisions. Most of those fatalities are caused by human choice or error. Autonomous vehicle systems will be designed to interact safely on the roads with other road users like human drivers, pedestrians, and cyclists.
  1. Moving away from Self Driving Cars, what is one problem in society today – perhaps one you encountered at the Department of Commerce – that you think we can solve more aggressively using technology?

I think that technology, correctly harnessed and understood, has the potential to improve the lives of many. Technology underpins most of our economy today, and it’s only going to compound over time, so we need to use technology to do a better job of training and educating people for the jobs of the future. Governments can identify important priorities and strategies and incentivize education and training so that people are prepared and trained for an evolving economy.

Finally, as I said earlier, if the private sector’s job is to drive innovation, government should work to ensure that there is an adequate social safety net in place for all people as the economy changes.

Thanks, Bert, for sharing your insights!

 

Bert and Zoe.jpg
Bert, Zoe (his super-woman fiancee) and Talia!

When a Nudge Becomes a Shove: Uber’s Guilt is All Of Ours

Uber recently made headlines with this feature by the New York Times, on how it uses behavioural insights to get drivers to work longer hours, and go to areas where there is high passenger demand. As Uber drivers are not employees, Uber has very little formal influence over their behavior – they can’t mandate how much drivers drive, or what area they cover. Behavioural nudges are a relatively costless way of getting drivers to do what Uber wants, instead of using monetary incentives. But is Uber particularly guilty?

nudge2

A Brief Overview of Behavioural Nudges

The use of behavioural nudges to shape customer, constituent and employee behavior is certainly not unique. Examples abound, including:

  • Businesses and customers. Positive reinforcement is the bedrock of modern advertising. Jeff Bezos from Amazon famously said “through our Selling Coach program, we generate a steady stream of automated machine-learned ‘nudges’ (more than 70 million in a typical week).” Games like candy crush turn us into addicts by providing mini rewards in our brains, releasing the neurochemical dopamine and tapping into the same neuro-circuitry involved in addiction.
  • Governments and constituents. The UK Government has its own Behavioral Insights team, which has helped sign up an extra 100,000 organ donors a year and doubled the number of army applicants by simply changing default options and how emails were written.
  • Employers and employees. One of the reason Google will fix your car, take care of your health and food needs all in one place is because they know it will get you to stay longer and work harder.

Now that you’ve seen some examples, what exactly are “behavioral nudges”? Definitions vary, but I’ll boil it down to two things:

  • Applying an insight about a person’s decision-making calculus (that the person might not even know about himself!) to get him to make the decision you want.
  • The person is likely unaware that this tool is being used (unlike a law or a policy, which he actively shapes his behavior to comply with).

For example, the principle of “loss aversion” suggests that humans are more likely to respond to a potential loss, than a potential gain. When you want drivers to drive for two more hours, tell them they’d lose out on $200 if they didn’t. Don’t tell them they’ll gain $200. We are also a lot more vulnerable to peer pressure than we think. The UK Government managed to nudge forward the payment of £30m a year in income tax by introducing new reminder letters that informed recipients that most of their neighbours had already paid. Never underestimate the power of inertia – which is why companies adopt “opt-out” rather than “opt-in” clauses.

An in-depth article on leading thinkers in the field of behavioural science (Kahneman, Tversky, Thaler, Lewis), can be found here.

The use of behavioural nudges is not new, but data has made it an increasingly powerful tool.

The potential for behavioural nudges is increasing with the proliferation of data about individuals. The more you understand how people make decisions – to work longer hours, to buy your product, to pay their taxes, to brush their teeth, to play a game, the more effectively you can nudge them towards your desired behaviour. Facebook knows more about me than I do. Uber knows more about their drivers than drivers themselves.

As a result, these companies can push buttons I didn’t even know existed. They have the potential to hack my operating system and change my behaviour.

Hence the ethical question of when a “nudge” becomes outright manipulation is more pertinent than ever.

Here are several ways to think about whether a “nudge” is being used ethically. <By the way, some people argue that it’s never OK to curb someone’s “moral freedom” through nudges, but I find that too idealistic – nudges have been used for time immemorial. It has to be a matter of degree.>

First, what is the inherent goodness of the outcome for the target population?

On the positive extreme, behaviours such as showing up at a doctors’ appointment, attending school or paying bills on time can be seen as actions that are positive for the individual. On the negative extreme, you could have outcomes such as an alcoholic purchasing more alcohol, or a suicidal person being nudged off the ledge.

There is huge scope for debate in between the extremes. Uber could argue that getting drivers to work longer hours during peak period is good for their earnings. Facebook would argue that repeatedly pushing advertisements that users are more likely to click helps them find what they need and like faster.

But here are two sub-questions to consider, in Uber’s case:

  • What is the distribution of benefits accruing to Uber vs the driver if the driver changes his behaviour? In this case, there seems to be a direct trade-off between Uber and drivers’ interests. As more drivers come onto the platform as a result of the nudges, drivers don’t benefit from surge pricing. On the other hand, Uber gets the benefit of more rides and hence more earnings.
  • Is there an intention to deceive? The author suggests that some of Uber’s methods nudged drivers towards geographical areas on the pretext of a surge, but when drivers got there, they found there was none. Even if this was not the intention, the asymmetry of information is unfair to drivers. More transparency is needed, perhaps by providing drivers a live feed of surge rates in various areas, including when surge is dropping.

Second, how easy is it to “opt-out”?

The ‘opt-out’ technique is one of the most commonly used “nudges”: always set your preferred option as the default, and count on human inertia (or ignorance) to keep people there. If you are a Netflix user, you’ve experienced this: once your episode ends, the next one comes on automatically in ten seconds. It is a nudge to keep you watching, but you can turn off this feature permanently. Google and Facebook will send you personalized ads, but you can opt-out and get those replaced by randomised advertisements instead.

If you are an Uber driver, you can also temporarily turn off the forward-dispatch feature, which dispatches a new ride to you before the current one ends (keeping you constantly driving, just as Netflix keeps you constantly watching). However, there is no permanent way to turn it off. It will keep popping back on when you take a new ride: you have to be constantly proactive about stopping it if you don’t want to overwork. Does the lack of a permanent opt-out feature make Uber more guilty? Perhaps. But I would like to find out more about the design considerations of both Uber and Lyft before giving a definitive view (hit me up if you have further insight!).

Generally, how proactively institutions educate their users/employees about the opt-out function matters, as does how easy it is to opt-out.

A More Important Question

So is Uber particularly guilty? On the surface it seems to. But want to hear my real answer? I have no idea, simply because much of the nudging that institutions do today is invisible, making it impossible to compare. We – as users, employees, constituents – do not even know that it is happening, and there is no legal obligation to tell us.

Hence, rather than ask whether Uber is guiltier than other institutions which deploy “nudges”, I believe the more important question should be: is self-regulation by these institutions sufficient? If not, does anyone have the moral high-ground to arbitrate? Should there be a system where institutions report their use of “nudges” and hold each other accountable? Would love to hear your thoughts.

nudge
Source: New York Times

Tech for Health – Too Little Too Late (Alam Kasenally)

This week, I feel lucky to have a veteran in healthcare technology and data science from the Silicon Valley, Alam Kasenally, give us an overview on how technology has already transformed healthcare, and the gaping hole which has yet to be filled: patient experience.

Alam recently moved to Mauritius with his wife, Min Xuan (one of Singapore’s brightest entrepreneurs), where they manage a hospital. They’re also busy inspiring youth toward entrepreneurship, and building an innovation hub in Mauritius. Prior to his relocation, Alam worked in Cancer Commons in the Bay Area, which provides patients and their physicians with the knowledge needed to select the best available therapies and trials, and to continuously update that knowledge based on each patient’s response. He also worked in Oracle, Yahoo and Crowdcast prior.

Alam and Min are two people who will inspire you with their commitment to using tech and innovation for public good, how deeply their invest in others, and their entrepreneurial experience. For our entrepreneur readers: If you are a founder trying to gain quick access to real users and customers to pilot quickly, their hospital in Mauritius provides an immediate incubator for medical technologies, while they also have trusted partners especially in the agriculture and tourism verticals that can move quickly. Let me know if you’d like to be in touch!

Alam

What’s the opportunity for Tech in Health?

Early Sunday morning, and that ridiculously healthy neighbor of mine is already lunging and squatting on my, I mean our lawn, ready for her half-marathon practice. My overwhelming instinct is to grab my fitbit and try and compete, but really, I should be (from an economist’s point of view) happy that I have an additional neighbor in my community that is healthy. For a start, the workforce is larger by one (and perhaps more than one: serious illnesses affect entire families of people who care for the patient). I enjoy a larger share of tax dollars deployed to Leslie Knope rather than, uh Gregory House. Finally, my neighbor is probably not lunging with a dripping nose. My neighborhood is safer.

So, now we’ve established that Health is a Public Good (as well as being “in the Public Good”), is there therefore a role for technology in Health? Well, there already is, and let’s take a tour of the landscape.

Technology, and I’ll focus on tech as the Valley knows it, minus traditional medical technology (prosthetics, diagnostic and treatment infrastructure, etc) has made a serious impact in the last 10 years. Smart entrepreneurs everywhere have caught on that tech can:

  1. Lower overall costs through automation and efficiency (Epic, the Goliath of the industry, now faces an impressive challenge led by lean startups)
  2. Lower overall costs through the finding of patterns in hospital big data
  3. Avoid adverse selection and moral hazard through finding of patterns in insurance data and monitoring patient behavior (though I have to yet to see these cost-savings trickle down to patients)
  4. Provide a variety of “quantified self” (steps, sleep, calorie, breath) in an effort to influence behavior change and lower their healthcare system’s cost
  5. Lower overall costs through remote monitoring of patients (FBS, SPO2, EKG) and we’re even seeing these devices cross into the consumer space.

So is there any scope for the use of technology left?

There’s scope for a combination of technology, process, regulation and people. Healthcare is not only an expensive good, but a remarkably complex one. Sleep, Dieting, Breathing are just fine (though they have attracted the most VC dollars, as they are the easiest to do). The patient experience, on the other hand, is broken, in a million pieces. The complexity of choosing the institution and doctor that will lead to the best (and most consistent) outcome is daunting. The complexity of referrals is mind-boggling and reimbursement is ludicrous. The simple knowledge of viable treatment options and associated outcomes is not available to patients, their families and even doctors.

Is this the limit of the use of technology for Health? No, it’s only the beginning. It’s time for a true Uber of Healthcare to emerge.

“Huber” re-invents the patient experience just like Uber successfully re-invented the taxi experience. This company (and maybe government) will successfully join different partners and datasets, to create an experience that is to the patient’s (and her family’s) satisfaction, safety and in her interests. Datasets that only get smarter, as Healthcare outcomes, treatment models and patient preference filter back into the system. From my experience, certain countries remain crippled in regulation that thwart such efforts, often with the reasonable but ironic pretext of patient privacy. But others (Singapore comes to mind) have an honest broker, trustworthy IT custodian of the data and could write regulation and create necessary conditions that could well be in the patient’s interest.

Soon, participants will begin to realize that it isn’t just Health that’s the Public Good. But Data. Now, excuse me while I grab my fitbit.

Featuring Xinwei Ngiam: Government Policymaker turned Start-up Business Strategist

I’m really excited to share this interview with Xinwei, Director of Strategy at Grab (formerly GrabTaxi), a ridesharing platform in Southeast Asia. She is also Regional Head of Grab’s social ridesharing service, GrabHitch, which beta-launched in Singapore in late 2015 and has since expanded to Kuala Lumpur, Jakarta and Bangkok. Prior to joining Grab, XW worked at the Boston Consulting Group and the Singapore Ministry of Finance.

In this wide-ranging interview, she shares her biggest lessons in her journey from policy-maker to consultant to start-up director, where she wants to see technology applied more aggressively, advice for companies looking to expand into Southeast Asia, and insights for both policy-makers and technologists from both sides of the fence. Besides being a good friend, Xinwei is someone I admire deeply for her work ethic, depth of thought and calm under pressure. Definitely someone to watch 🙂

xwngiam

1. How did you make the transition from Government to Tech? What’s it like working in a start up vs in a more traditional industry?


After I left Government, I joined consulting for about 2.5 years, and 
thereafter joined Grab, where I’ve been working now for almost 2 years.

I would recommend consulting for any generalist who is looking to learn at hyper-speed about the business world and about the region we live in. While at BCG, I spent at least half of my time in Indonesia (if not more), and it’s benefited me greatly now that I work in and manage teams in our Jakarta office.

Joining Grab opened my eyes to start-up life and culture. I’ve loved this way of working from the beginning – the juxtaposition between the casual team culture but incredibly intense pace of work; the tension between wanting to reach for the stars but having to ruthlessly prioritize based on your current resources and capabilities; the ever-present low-level existential crisis of not quite knowing whether you’re flying or falling. It’s a thrilling place to work, but with that thrill also comes stress and increasingly blurred lines between work and life (my husband will not hesitate to confirm this last point).

For those who are seeking to move from more traditional industries to start-ups, you have to be prepared to let go of some of what you know; but also have confidence that you’re bringing an expertise and knowledge base about how companies work that is very valuable to
start-ups. Some tips:

 

(a) Learn to embrace uncertainty.

Uncertainty will exist in all aspects of start-up life. The type that seems to affect people most is professional uncertainty. In a startup, it’s not uncommon to experience frequent reorganizations, to see the team you joined dismantled, or to undergo several title or portfolio changes in a few months. Then there’s business uncertainty – how do you know whether to invest in a new vertical/market/business or not? When choosing between two ideas that could 10X the business (or send it into a downward spiral) how do you choose? There is no playbook for what startups typically do, and that can cause a lot of anxiety.

There is no perfect remedy for this, but it helps to take a philosophical view that no matter what happens you’ll live to die another day. Channel all your nervous energy into obsessing about your business and outserving your customers, put aside your personal anxieties and just enjoy the ride.

 

(b) Execution is what makes good ideas great

There are two common pitfalls (that I have personally experienced many times now). The first is to overestimate your ability to execute, which results in jam-packed workplans where items are checked off the list, but not done in a truly excellent way. The second is to underestimate the need for excellent execution; this usually comes hot on the heels of a great idea where one is seduced into thinking that the awesomeness of the idea will carry the day.

The truth is that good ideas are everywhere, especially in fast-growing startups where everyone is obsessing over big questions such as how to win market share, how to serve customers better, or how to leapfrog the competition. What makes an idea truly great is elegant, flawless execution that delivers outsized results.

I don’t have any big secrets to share on how to execute well – I’m still very much a student in this journey – but I think a big part of it is about disavowing silver bullets and instead being very deliberate about tracking and measuring any intervention you make in your market. You want to get to a point where you know how best to deploy every dollar based on what channels you have at your disposal and what your objectives are. The tradeoff of course is that learning takes time (not to mention failure), and in a startup, time is often the one thing we don’t have. But our job is to walk that tightrope.

2. What is one problem in society today that you think we can solve more aggressively using technology?

I would really like to see how we can use technology to facilitate elderly lifestyles and caregiving. I think the amount of thinking and consumer research done in the field is simply not commensurate to the tremendous need and opportunity. In fact, elderly care has many similar themes with infant care (ranging from personal hygiene products to food to mobility solutions), but the two sectors are worlds apart in terms of customer-centricity, product variety and innovation. One reason is that elderly people aren’t as tech savvy as younger cohorts, nor are they constantly connected to the internet via smartphones – but that is changing very quickly.  I think there is another deeper reason, which is that elderly care fundamentally faces a brand image problem – we associate it with the end-of-life, the loss of dignity, and diminished versions of ourselves, rather than simply a challenging stage in life where we have different needs and require more support and help than we used to.

I would love to see innovations in areas that facilitate independent living (mobility solutions, health monitoring and remote caregiving of some sort, seamless chronic care), reduce the burden on caregivers, and that use the internet to create active communities or learning opportunities for the elderly.

3. What’s one thing you wish your friends in Government knew about the tech sector, and one thing you wish your friends in the tech sector knew about Government?

That no one is really in this only for the money. There’s a common misconception that everyone in the private sector (and especially in tech companies) is out to make a quick buck. Of course, there are always going to be companies that fit that stereotype. But in my experience, the most impressive and successful entrepreneurs never quite set out to make big bucks. Rather they became obsessed with some crazy idea that they thought could deliver huge impact, executed on it and managed to bring the world along with them.Making money is a necessity for businesses (at least once the growth capital runs out) and so it’s unrealistic to expect companies to behave like charities. But just like the humans who found and build them, companies have their own personalities, culture and DNA. Of course, there’s a limit to how nuanced our regulations and economic policies can be, but if governments see that many businesses come from the same starting point of wanting to make a positive impact on society, then it paves the way for more open and productive engagement.

Another misconception – which, like the first, isn’t restricted to people in Government – is that what makes a tech company great is solely dependent on how good their tech is, and nothing else. The companies that we consider great “tech companies” – Apple, Amazon, Netflix, Facebook, Google – certainly had and continue to build superior technology; but what sets them apart is clarity of focus, a winning business model, and the willingness to fail and pivot.


I recall a conversation with a friend who was trying to understand how Didi beat Uber in China, and a sticking point was whether Didi had any original tech or whether they simply copied ideas; or whether Didi had superior tech which allowed them to win. There are many versions of this story, but what’s fairly clear to me is that technology was merely table-stakes in the Didi-Uber fight; these were two giants at the top of their game and a more finely-tuned surge algorithm was not going to be decisive. What Didi had was incredibly efficient and locally rooted ground operations (back to execution and the ability to deploy every dollar more efficiently than the competition), excellent and often viral marketing, and deep integration with China’s all-pervasive mobile payments network.

In terms of what I wish the private sector understands about Government – I think it’s that the current system of rules and regulations was constructed for a reason and changing it does require time and deep consideration. There’s a general impatience among the private sector with governments, and especially so in the tech sector given that so much of what we do challenges status quo norms and systems. But just as we wish governments understood that we are just trying to serve our customers the best we can, they too need to do the required diligence to make sure that this is the right thing for society as a whole. So the approach shouldn’t be to try and disassociate ourselves from government or brazenly disregard regulations, but to build bridges and try to align our interests. If you’re in it for the long haul, then engagement and trust is the only sustainable way forward.

4.     You work extensively in Indonesia and Kuala Lumpur. What are they key differences in how you operate in these contexts? What advice to you have for companies looking to move into these regions?


One gradually exploding myth about Southeast Asia is that it is a coherent region; in fact, Southeast Asia is extremely fragmented with clusters of countries sharing some common cultural history while others are relatively unrelated. I’ve found that Singapore and KL feel 
culturally very similar, for obvious reasons. Indonesia, on the other hand, feels quite different, more so the further you travel from Jakarta. As my CEO likes to say, Indonesia is a continent, not a country. The energy and vibe is quite different from what you’ll feel in Singapore or KL. The war for talent is far more intense there. We’ve seen some really impressive tech companies come out of Indonesia in the past few years.

If you’re looking to expand to or start something in Indonesia (or really anywhere outside home ground), I think the most important thing to do is to spend time on the ground and learn the language. There’s only so much management you can do from afar, and most of these markets are intensely competitive. There is no substitute for being on the ground and experiencing your product and services in the local context. You’ll learn things that no management report could adequately describe.

 

5. Some of our readers are interested in entering the field of tech. What is your advice for them?


First, if you are currently in a non-technical role but would like to become a technical Product Manager, a software engineer or data scientist, then some formal training is required and there are tons of great options out there to acquire those skills. That aside, I believe that in every company will be a tech company in the future, in some shape or form. It will become increasingly meaningless to think about entering the “tech industry” because every company will have to adopt relevant technology to stay ahead, including how to use the internet to distribute services, understand their customers and facilitate payments and other transactions.

So I would encourage anyone keen on “tech” to first ask themselves what real-world problem they are trying to solve, or what business vertical they feel best fits their interest. Once you’ve figured that out, then go in search of a company that you think is harnessing tech in the right way to solve that problem. Otherwise you put yourself at risk of becoming an unknowing participant in “innovation theatre” in a company that’s just using tech as a marketing tool.

ngiam and tay
XW and I at CES2017, speaking about the potential and challenges of the sharing economy in transport

Why Do Tech Companies Need Policy Teams More Than Ever? (An Evening with X, Andressen Horowitz & 23AndMe)

The Stanford Business School just launched a new Policy Innovation Initiative. Earlier this week, I attended their launch event featuring Sarah Hunter, Policy Director of X (previously Google X), Kathy Hibbs, Chief Regulatory Officer, 23&Me, and Ted Ullyot, Partner of Policy and Regulatory Affairs, Andreessen Horowitz.

Why the need for policy and regulatory thinking within the tech world?

The motivation is simple. In the past decade or so, software innovations have dominated. We’ve seen how great software platforms – sometimes built by tiny crack teams – can scale rapidly in way that completely changes markets. Think Amazon and Ebay for commerce, Facebook, Snapchat, Instagram for social networking, Box, Salesforce, Workday and Slack for enterprise solutions (decode: software that helps us manage our HR, customer relations and intra-office discussions with much less pain). The market is increasingly saturated with software solutions for almost every area of life. Hence, while will continue to see gains in productivity and efficiency in these systems because of Artificial Intelligence, pure software will no longer be the area of rapid technological innovation.

Instead, technological innovation in the next decade will be dominated by technologies spanning hardware (things that are in our physical world) and software (the virtual world). Examples include self-driving cars and surgical robots, which are performing physical functions but controlled by algorithms in the virtual world. A term often used to describe this general area is “cyber-physical systems”.

Here comes the challenge: objects in the physical world are more directly risky to human life than software systems. Furthermore, these objects can harm people who don’t choose to use them – I choose to download Facebook if I can stomach the risks to my personal privacy. On the other hand, even if I never buy my own Self Driving Car, my life could be at risk if someone else owns a faulty one. There is a more acute need to manage risks to the general public.

Hence, the regulatory landscape is stacked against emerging tech. First, Legacy regulations abound to protect consumers from death or physical harm, such as long Food and Drug Administration (FDA) and vehicle/driver-licensing processes. Second, Because of potential harm to human life, regulators are likely to approach the emerging technology from the perspective of ‘mitigating every risk’ (read: adding even more new conditions and clauses). Third, regulations and legislation are typically based on precedent, and are hence biased towards incremental (as opposed to disruptive) improvements in incumbent tech and business models.

Regulatory risk will be the major Go-to-Market hindrance for most emerging tech companies in the next decade; if they fail to address regulations, a company could be dead in the water before they even begin. Policy teams within tech companies exist to minimize this regulatory risk. They advise companies on questions such as:

  1. Who do we need to influence so that regulations fall in our favour? Policy teams often go above regulators to paint visions for politicians: how the emerging technology will solve social problems and create new economic opportunities.
  2. Should we work with regulators to co-create new regulations, or break the regulations? The risk of breaking regulations varies – if you’re able to get widespread support from users (think Uber+AirBnB), you may be able to force regulators into certain positions. It’s more difficult to take this approach for hardware solutions.
  3. If we desire to co-create new regulations, what approach should we take? One company designed their own set of self-driving car regulations, which never came to pass because the technology was pivoting so quickly.
  4. How early should we engage regulators? Generally, it isn’t good to give regulators surprises, but sometimes engaging too quickly before there are good answers on how to mitigate the risks will scare them into coming down hard
  5. Is it even worth trying to enter this market, or should we start where regulations/Governments are more relaxed? For example, most successful drone companies tested outside the U.S.

The role of policy teams in tech companies can be likened to master chess players. They get to know the kings, queens, knights and pawns who influence the regulatory system, and appeal to a range of motivations to move the pieces in their company’s favour.

Each speaker pointed out that regulators aren’t technological dinosaurs who intentionally regulate technology to death (though they are often caricatured this way). They simply have a different bottom line, which is to minimize risks and externalities. Put this way, regulators and innovators can provide a healthy check and balance to each other.

Areas I Hope They Address

I’m excited about this initiative by Stanford Business School and would love to see it be a neutral place for tech and policy to folks to discuss the best approaches to regulating emerging tech. Here are some areas I hope they will address.

How do companies manage the competitive vs collaborative dynamic in lobbying for regulatory change?

On one hand, there are great advantages to be the first mover and defining the regulations in your favour. Kathy from 23&Me shared that if you are able to set precedent, all your competitors have to follow your standards. This locks is a certain competitive advantage. There are other circumstances where working collaboratively is more productive. For example, on the same issue, politicians and regulators might be far more willing to listen to a group of local start-up founders than large multinationals like Google. Smaller companies sometimes have to work through trade associations because they lack the scale needed for lobbying.

Will start-ups lose out as this policy/regulatory expertise becomes more critical to success, yet is dominated by large players?

Small companies are often unable to recruit for the policy/regulatory function because of their resource constraints. This is why VCs like Andreessen Horowitz have policy teams that advise their stable of start-ups. Will more of such advisory services become available to start-ups? Who will provide them?

We are also starting to see coalitions such as the “Partnership in AI” by Google, Facebook, Amazon, IBM and Microsoft – no doubt one of the objectives is to lobby Governments on AI-related policies. How do start-ups fit in? Is there a risk that the agenda is overly swayed by large companies?

An idea for policy teams in tech companies: Go beyond lobbying for regulation; work with Governments to support widespread adoption of emerging tech

One of the themes of the night was how tech companies need to paint a vision to politicians on the benefits of the emerging technology, so that they support favourable regulatory change.

I think we have to go further than persuading politicians to get to the point of favourable regulation. Widespread adoption of emerging technology especially in areas of healthcare, transport and education is hindered by more than regulation. For example, change can’t take place if you don’t inspire, resource, and manage the morale of teams on the ground who are accustomed to existing ways of work and will not change just because a new technology exists. I saw it first hand when I worked at the 40,000-strong Ministry of Education in Singapore.

Without this, politicians will find it difficult to move, even if they agree strongly with tech companies’ visions for the future. Singapore’s Prime Minister, who takes a personal interest in Smart Nation, recently lamented that the whole effort is moving too slowly. This, coming from one of the most efficient Governments in the world, suggests that there are deep-seated issues in achieving widespread adoption of technology.

Here are some things that are essential for widespread adoption of emerging tech, but that Governments/Politicians will not be able to tackle alone:

  • Painting a vision that shows implementers and constituents how emerging tech will exponentially improve their reality;
  • Tying concrete benefits to these emerging technologies such as creating new local jobs;
  • Actively advocating for programs that help people deal with the downsides disruptive technology, such as re-training for displaced workers.

These are areas that policy teams within tech companies can consider as they seek to move chess pieces in their favour: not just to achieve favourable regulations, but to see widespread adoption of their technologies in regulated sectors.

<if you work in a policy/reg team within a tech company and already work on these areas, I would love to hear from you>

In the coming posts, I will also feature folks who work in policy/regulatory teams within Silicon Valley and Singaporean companies. Look out for those!

Two (Game-Changing) Ways Cities can use Technology to Fight Inequality

The story of income inequality is not new – as lower and middle-class incomes stagnate while the highest income brackets race ahead, the wealthy have access to goods and services that are increasingly out of the average person’s reach.

But we now see its detrimental effects more clearly than ever. I live in the Silicon Valley, and when news of Donald Trump’s election broke, the overwhelming feeling was disbelief. It was unimaginable. Tears of anguish were shed, yet a large part of the country celebrated. To me, that moment captured the deeper impact of inequality – fragmentation of society. Our politics become polarized, we are unable to find middle ground in our interests, and we increasingly feel like a nation of enemies, not countrymen.

While the problem gets more serious, our typical approaches to tackling inequality are reaching their limits. Redistribution is a political hot potato that pits the interests of the “haves” and “have-nots” against each other. Investing heavily in educational opportunities has diminishing marginal returns on social mobility both in the absolute sense (because the future of jobs is increasingly uncertain) and in the relative sense (because wealthier parents give their children more and more advantages).

We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.

Inequality

Source: charterforcompassion.org http://bit.ly/1y8DPw1

First, cities should use technology to make life experiences in the city more and more independent of incomes.

 It would be impossible to close the income gap completely, short of communism. A society where incomes are totally equal is also undesirable, as it erodes the motivation to work.

However, I believe that technology can make life in the city increasingly independent of income, which can go a long way towards mitigating the daily experience of inequality.

Let me start with explaining the notion of an aspirational good – things that people wish they had money to buy. In transport, most people aspire towards owning a car. In housing, it is a condominium or a private home (American friends: as opposed to a publicly-built Housing Development Board apartment, which 80% of Singaporeans live in). In healthcare, it is a private doctor or hospital bed – at your choice and convenience. In education, it is getting into top schools and universities.

There is an unsustainable dynamic behind aspirational goods. Because these goods are limited in supply, the more people can afford it, the more expensive they get, and the further out of reach of the average citizen. Aspirational goods are the sources of a huge amount of angst in the middle class.

Technology has the potential to overturn the entire notion of an aspirational good. By creating new forms of value, it can make the alternatives so attractive that even those who have money choose not to buy the aspirational good. 

Take transportation for example. Owning a car is so attractive today because public transportation is an inferior option on many counts – the low cost cannot make up for its lack of time efficiency (it takes about twice the amount of time as a car ride), comfort (especially in humid weather), and customization (as a car owner, I know I can get a ride whenever I want).

What if public transport can be faster, more comfortable, more customized and cheaper than owning a car? With technology, this need not be a pipe dream. Imagine a day when you can wake up in the morning and your phone already knows where you need to be. It recommends the top three ways to get there. You select one, and within a minute, your ride shows up at your door – perhaps a shared car, or an electric bike if it’s sunny. It gets you to the train station just as your train pulls in. When you get out of the train, your minibus has just arrived to take you to the office. After work, you can summon a sleek designer vehicle for your dinner date. On the weekend, an autonomous jeep shows up at your door-step to take your family around for a day of fun.

You don’t need to buy multiple tickets – everything is paid through your phone. Or, you can even pay for transport just like a Netflix or Amazon Prime Subscription: a flat fee for unlimited rides. You never need to worry about parking again. With alternatives like this, how many people would still want to own a personal car? Even the wealthy may reconsider, especially if we simultaneously put in policies to make driving more inconvenient, such as no-drive zones in the city.

Just as technology brings about new forms of value (e.g. customization, flexibility) for those who don’t own a car, how can it do the same for other sectors?

  • How can technology help to transform Singapore’s public housing estates such that they offer new forms of value which private estates cannot provide? For example, how can we help HDB dwellers feel like the entire estate – with all its facilities and open spaces – is their home, one much bigger and diverse than any private estate? Digital communities and intra-town transportation may be aspects of this.
  • How can technology make a face-to-face doctors’ appointment something that people no longer seek as the “premium option”, for example, by making tele-health so attractive and pervasive?

I believe if domain experts and technologists put their minds to this, they will be able to come up with much better ideas than these! In short, technology can help catapult currently “inferior” options to equal status as “aspirational” options by creating new forms of value.

2. Second, cities should use technology to distribute scarce land and human resources more equitably.

In most countries, there is a healthy debate on how progressive and equitable the tax and redistribution regime is. However, not as much attention is paid to how other scarce city resources – land and manpower – are used. These too, must be used equitably, and technology can help cities achieve this.

Land

Reducing the land used on roads is a great example of how we can use land more equitably. Roads and parking lots tend to be utilized disproportionately by those who own cars, who – in Singapore – tend to be wealthier. Can we cut down on roads and parking, and reallocate this land to purposes such as community facilities and public housing, which benefit a wider proportion of the population?

Yes, and technology is critical to this. How much land we need for roads and parking is determined by the concept of “peak demand” – the maximum number of vehicles on the road, ever. We can cut down peak demand by encouraging people to use shared mobility options rather than drive a private car (I write about how tech enables this here), and by investing in autonomous freight and utility so that these activities can be done at night, when roads are far emptier.

Public Sector Manpower

Similarly, we can use public sector manpower more equitably by investing in technology. Technology can significantly reduce the manpower we commit to customer services. For example, Govtech rolled out MyInfo, which enables citizens to automatically fill in their administrative information for Government schemes with the click of a button. Chatbots on Government websites will increasingly be able to answer public queries; phone lines will no longer be needed. Public sector manpower can now be dedicated to functions which are in great need of resources. One such area is social work and education. Families in the bottom rung of society often face a cocktail of challenges – divorce, low-income, lack of stable employment, cycles of incarceration and so on. Giving them (or their children) a real chance of breaking out involves an extremely high level of hand-holding and investment by social workers and schools. Resources are sorely needed here.

Access to top quality healthcare

Let’s take another scarce resource – top surgeons. People who can pay for their services access better quality care, and stand a higher chance at recovery. Technology can change this dynamic. Companies like Verb Surgical are using machine learning to propagate top surgeons’ expertise more widely. This is how it works: every time the best surgeons perform a procedure, every single action is recorded in a common machine “brain”. The “brain” is trained to associate each action with the probability of a successful surgery. As the “brain” records more and more surgeries, it gets smarter and smarter. Now, the “brain” is made accessible to ALL surgeons. At each step of their surgery, they are told what successful surgeons did. Now, the best surgical expertise is within the reach of the average citizen.

Technology that enables our scarce resources (e.g. land, public sector manpower and top surgeons) to benefit the broad population and serve those in acute need are the types of technologies that cities should invest in, and quickly enable through regulations.

Conclusion

If you google the “Smart City” movement, you’ll find many broad and loose definitions. Generally, it refers to how cities deploy technology to improve city life and allocate resources more efficiently, whether it is helping transport systems run more efficiently, making interactions with various Government services easier, or to adding fun to the city experience.

Unfortunately, such broad and loose definitions give cities little guidance to on what to focus on in prioritising investments and regulatory reform, which is an incredibly important conversation given the limited resources at most cities’ disposal. It also does not paint a compelling vision for why being a Smart City matters, which disengages most of the population. Personally, before I worked in tech, I felt absolutely no connection to the idea of a ‘Smart City’. Tech was cool, but I never thought it was crucial.

I believe that using technology to tackle inequality and its effects should be a Smart City’s ambitious goal and guiding force, providing focus and rallying support from its constituents. This article spelled out two ways to do so.

“What I Wish They Knew”: 5 Answers from a Government Data Scientist

One of the goals of this blog is to bridge the worlds of tech and government: I believe we can do so much more by working together, yet we often don’t understand each other deeply enough to begin. I will be starting the “What I Wish They Knew” series, which features people who are familiar with both these communities.

The first person to kick off this series is none other than my husband, Kenneth, who worked in Singapore’s Government Data Science unit before pursuing a PhD in Statistics at Stanford. By the way, you can find out more about Singapore’s Government Data Science unit on their blog: https://blog.gds-gov.tech/ – highly recommended.

kenneth handsome.jpg
Kenneth at The Hive, where the Government Digital Services team is located.
  1. How did you get into Government Data Science? 

I’ve had a strong interest in mathematics and related quantitative fields like statistics and computer science for as long as I can remember, and studied math at Princeton as an undergrad. [NB: Kenneth’s mathematics blog can be found here.]

I began my career in the Singapore public service, hoping to give back to society in the small ways I could. While I was at the Ministries of Defence and Environment, most of my work did not involve any advanced math or data analysis.

I missed the intellectual challenge of quantitative thinking, and started taking online courses on the side. Andrew Ng’s “Machine Learning” course on Coursera first piqued my interest in data science. I learnt how we can use a small toolbox of algorithms to extract a whole lot of information from data, and I thought to myself, “How cool would it be if I could use some of these techniques in my work?”

Fortunately, a unit in Government was being set up to do just that: use state-of-the-art data analysis techniques to inform policy decision-making. I joined the Government Data Science unit in 2015 as a consultant. My work experience in policy and my quantitative undergraduate training put me in a rare position to understand the mindsets of both the policy officer and the data scientist. As such, I felt that I was an effective translator between the 2 parties.

  1. How about an example – what is one meaningful thing you did in Government Data Science?

An agency was very concerned about congestion during peak hours at the checkpoint between Singapore and Malaysia. Understanding cargo traffic patterns could help them design policies to reduce congestion. All they had was tens of millions of “permit data” entries, which captured the time that the cargo truck carrying the permit passed through a checkpoint, the industry code and value of the goods carried. I worked with the agency to define useful problem statements to shape the direction of analysis. One example: what are the top 5 industries moving cargo during peak hours? (Since policy interventions would be done at an industry-level).

Next, since each truck could hold multiple permits and the number of trucks was what we cared about, we went through a (non-trivial) process of turning “permit data” into “truck data”. We were able to identify the top industries moving cargo during peak hours, and further narrowed this group to those who were moving cargo on the busiest roads. We were also able to develop hypotheses on what influenced industry behaviour.

After completing the analysis, I shaped the narrative of our presentation in a way that delivered impactful policy insights, ruthlessly cutting down on unnecessary details. This is often the most painful part of the process for a data scientist – it’s so tempting to want to show ALL the great analysis we did.

After dozens of hours of work, there was nothing more satisfying in seeing the audience’s facial expressions saying, in effect, “before I was blind, now I see”! They never had a picture of the cargo traffic patterns until our analysis was done, and could now act upon it to improve congestion.

  1. What is one thing you wish non-data scientists knew about working with data scientists? 

That good data analysis requires significant collaboration between the data scientist and you, the domain expert.

Some people view the relationship between the domain expert and the data scientist as follows:

  1. Domain expert gives data scientist a bunch of Excel files.
  2. Data scientist crunches the numbers and churns out a report or presentation 3 months later. After all, the data scientist knows everything about data and that’s what we are paying them to do, right?

Nothing could be further from the truth! Domain expertise can speed up the data analysis process tremendously and direct it meaningfully, resulting in greater value from the project. Let me give two examples of this.

First, explaining the data to the data scientist, down to what each column means and how the data was collected, will save him/her much second-guessing angst. For example, if the patient check-out time was 18:00:00, does it mean that the person checked out at 6pm, or does it mean that the clinic closed at 6pm, and so everyone who hadn’t checked out yet was given a standard check-out time? Explaining the data will also give the data scientist a better sense of which variables are of greater importance and deserve more attention. In the example above, what does “checking-out” mean anyway, and is it significant?

Second, domain experts can pick up on insights that would escape data scientists. For example, a data scientist finds that Chromosome 21 seems to have an impact on a health outcome. Is that expected? Does it confirm some of the other hunches that we have? Or is it something completely unexpected, that suggests that the model is wrong? These are questions that a data scientist is unlikely to have any intuition about. However, with feedback from the domain expert, the data scientist can quickly decide to pursue or drop lines of inquiry.

  1. As a policy-maker, what is one thing you wish more data scientists paid attention to?

That data analysis is not for the data scientist, but for the policy-maker (or client). As such, good data analysis always puts findings and insights in the proper context.

Consider the sentence: “Our prediction model for which patients will be re-admitted over the next 6 months is 34.56% more accurate than the existing model.” Upon seeing this sentence, several questions come to mind:

  • What do you mean by accuracy? Is the measure for accuracy that you are using appropriate? (See this https://en.wikipedia.org/wiki/Confusion_matrix for a whole zoo of accuracy measures.)
  • 56% seems overly precise: can we really compare performance down to 2 decimal places? (35% might be better.)
  • Does 35% more accuracy translate to a meaningful difference? For example, will this allow us to tailor our services better to 100,000 patients, or 10 patients? (If possible, relate the finding to something the policy-maker cares about, like dollars or man-hours saved.)
  • Is this even a meaningful thing to predict?? (Hopefully, the domain expert would have said so. See answer to question 2.)

Good data analysis also provides enough detail to illuminate, but not too much till it confuses. For example, saying nothing about the modeling process could make me wonder whether you did your homework in choosing the most appropriate model (and whether I should have spent all that money hiring you). At the other extreme, I will not appreciate going through slide after slide of raw SAS/R output.

At this point I cannot overstate the importance of appropriate data visualisation. These visualisations have to be thought through: good charts clarify, poor ones confuse. Unfortunately, it’s a lot easier to make the latter. (See this for examples not to follow.)

kenneth-graph
Truly, a (well-designed) picture is worth a thousand words

 

5. What is your hope for the field of data science?

The economist Ronald Coase famously said “Torture the data, and it will confess to anything.” In an era of subjective reporting and “fake news”, this concern is more pertinent than ever.

My hope is that the general population will have enough statistical knowledge so that they can call a bluff when they see one, and demand quantitative evidence for decisions their leaders make. To this end, I hope to see reforms in statistics education at the high-school level so that it becomes a subject that people feel is relevant and interesting, rather than abstract and theoretical (which is often how it is taught today).

Thanks for reading! Know anyone who should be featured in this series? Do let me know at karentay@gmail.com.

Tackling AI-driven job displacement: A Primer

I’m starting a series of posts on solutions to AI-driven job displacement.

I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?

I use the word “hope” because it is the prospect of a better future within reach which will inspire people to continually re-invent themselves when they face setbacks like job loss, rather than to give up. It’s not enough to give them concessions.

Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:

  • In the long run, there will be a net positive in jobs, but in the short run there will be pain: especially among the middle class. The White House published a useful overview on the impact of AI on society last December and Gary Bolles has great resources to understand the hollowing out of the middle class. One very concrete example of middle-class job loss: as shared mobility becomes an increasingly attractive option for city-dwellers, less people will feel the need to own a car. The GMs, Fords, Toyotas and Tier-1 suppliers of the world will scale down their manufacturing business and move into new lines such as mobility services. New jobs will most definitely be created – for example, operators who manage fleets of autonomous vehicles and software engineers whose algorithms will continually improve the cost and energy efficiency of fleets. It’s likely these jobs will hire new people, not those who were displaced.
  • Progress is at stake if we can’t give people the confidence that the new system will work in their favor. We see this in widespread support for anti-trade platforms, even though no economist doubts the overall benefits of trade. America has taken a few steps back, and while some of this may be attributed to the ignorance of one man, it is also the collective anxiety of people speaking through their votes – and this anxiety is not unfounded. This is not a particularly American problem. Brexit is the other most popularly cited example, but you see it everywhere, both in the developed and developing world: Egypt, the Philippines, Singapore: a push back among those who feel that the system no longer works in their favor.

So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.

Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?

A Framework to Understand our Target Group

To start off, it’s useful to have a framework to understand the universe of people we need to reach. I offer two dimensions that differentiate people in our target group.

Slide2.jpg

*these are just illustrative examples! Obviously, the reality is a lot more nuanced.

Proactive vs Reactive

On one end, we have people who are “proactive” about keeping themselves relevant to the job market. Geeks like my husband, who completed a dozen Coursera/Udacity courses, taught himself new coding languages, mastered Tableau and ran a successful math blog, all while managing his day job.

On the other end we have people who are “reactive” – they are hardly thinking about the possibility of losing their job; even if they are, they aren’t taking action. This could be the 45-year old who worked a 9-5 job for the past 20 years or a new parent who has no bandwidth to think about anything but the present.

Inherent personality traits are one determinant of where a person falls on this spectrum. Life stage is another – it affects how much time we have to think about alternative plans. The prevailing culture of family and community also shapes what we value such adventure or stability, ambition or work-life balance. Much of this is determined by geography.

Where someone falls on this spectrum is never static. People can shift left and right, and motivation is key.

 New Entrant vs Old Timer

The “new entrant” vs “old timer” distinction is useful in framing solutions. To illustrate this, let’s imagine Uber can fully automate all its fleets by 2025, and will no longer need human drivers.

“Old timers” are existing Uber drivers. The challenge is to give them sufficient fore-warning of impending job loss, and opportunities to pivot into new industries. The difficulty is in encouraging them to take action before it’s too late, and to ensure there are good alternatives.

“New entrants” are those who will be entering the job market in 2025. The challenge is to equip them with skills that will be relevant to the job market in 2025, including skills required to operate Uber’s fleet of autonomous vehicles in localities throughout the world. The difficulty is that no one knows exactly what skills will be needed in 2025, and how many jobs will require these skills.

Here are some topics I’d like to address based on this framework.

First, most solutions today address those who are “proactive” – it’s the easiest group to address because it’s sufficient to make learning resources available; they’ll find them and take them up. How can we motivate or guide those who fall into the “reactive” half of the framework? What can tech companies do? What can Governments and employers do? How do these efforts tie together?

Second, how can tech work with higher education institutions to prepare new entrants (our children today) for the future job market? About five years ago, some predicted the death of higher education institutions as Massive Open Online Courses took off. Now, most of us acknowledge that this is not going to happen at a large scale any time soon. Most people aren’t as confident enough to ditch traditional paths, even if these don’t make sense anymore. How can tech companies help to reform higher education and make it far more responsive to the job market than today? I will draw on my experience working in higher education to address this question.

Third, what social security policies do we need in an era of accelerating job displacement? In the Silicon Valley, the idea of a Universal Basic Income – where everyone receives a basic monthly stipend from the state – has gained much traction as a policy solution to job-displacement. How will a Universal Basic Income impact the different groups in this framework? What are the alternatives or complements to a Universal Basic Income? I will draw on my experience working in and studying social policy to address this.

A summary here: Stay tuned!

slide3

The Dark Side of the Sharing Economy in Transport (and Three Solutions)

102519988-protest-uber-1910x1000

photosource: cnbc

The shared economy is a net positive for society…

In my previous posts, I’ve talked about how the shared economy – expedited by technology – can have tremendous benefits for society. For example, it can mitigate the feeling the inequality by closing gaps in the transportation experience. The benefits are even greater when private companies work with Governments to reach the elderly, poor and underserved.

I’m pretty sure that the shared economy in transport has a net positive effect on the economy too, though evidence is nascent. Last March, Lawrence Katz and Alan Krueger conducted the RAND-Princeton Contingent Worker Survey, which showed a substantial rise in the incidence of alternative work arrangements[1] for workers in the US, from 10.1% in 2005 to 15.8% in late 2015. Strikingly, this implies that all of the net employment growth in the US from 2005–15 appears to have occurred in alternative work arrangements.

One reason is that the shared economy provides part-time or intermittent work for people who otherwise cannot find a suitable job. All these Uber drivers I’ve met before – the young man who lost his job and needs time to search for new employment. The father fighting a costly custody battle, who needs a flexible job so he can show up in court. The lower-income mother who needs to supplement her day job to pay for her mortgage. Uber driving is a particularly attractive part-time job – typical part-timers get paid disproportionately less than full-timers. In contrast, Alan Krueger and Jonathan Hall found that no matter how many (or few) hours Uber drivers work, their hourly earnings were the same.

…BUT the distribution of benefits matters, and here’s how cities should think about it

Even though the shared economy creates tremendous new value, the distribution of value favours some groups over others. The tensions that arise can undermine companies operating in the shared economy, as we’ve seen in several cities worldwide. Cities need to consider how they may ensure a fair distribution of the shared economy’s benefits along three dimensions:

  • Workers vs companies
  • Incumbents vs new entrants
  • Now vs 10 years’ time, especially with the advent of autonomous vehicles

Companies would be wise to work collaboratively with cities to resolve these issues early on, rather than lock horns in costly legislative battles, or get blocked from new markets.

  1. Fair Distribution of Benefits Between Drivers and Companies

The shared economy benefits both ride-sharing companies and their drivers, but arguably companies benefit a lot more. The business model of ride-sharing companies like Uber, Lyft and Grab is to provide a technology platform which enables matching of riders to drivers. These drivers are essentially self-employed contractors who log on to the platform whenever they wish. Many are able to find work and supplement their income through this platform.

However, these drivers are not employed by ride-sharing companies and hence do not receive certain benefits. In Singapore, employers are required to contribute up to 17% of their employee’s salary to a savings account for housing, retirement and healthcare. In the U.S., many receive healthcare insurance through their employers, who are often able to get better rates than individuals. In the UK, employees are protected by the minimum wage legislation. Drivers for Uber and Grab do not receive such benefits because they are not considered employees.

As companies rely more on these self-employed workers to fuel their business, risks are passed from companies to workers. As Singapore’s Deputy Prime Minister Tharman Shanmugaratnam has said “….it serves the interests of the company because they’re really pushing risk to the contract worker…who actually can’t take much risk – the risk of instability in wages and the risk of not being prepared for retirement because of a lack of social security contributions.”

Cities need to start shifting their social policies to accommodate the rising proportion of self-employed workers. At the same time, companies need to discuss reasonable ways to spread benefits and share risks with workers who are self-employed. It need not be all or nothing. There has been talk about creating a “third classification” of workers who have some benefits of employees, while retaining the independence of a contractor. This gives both the business and worker flexibility while providing some social protection.

Working with cities on some “in-between” solutions will help business avoid lengthy legal battles down the road. For example, Uber is appealing a ruling by London’s employment tribunal recently that it should treat its drivers like employees, including paying the national minimum wage. I would go so far as to say that it is the responsibility of ride-sharing companies to start engaging in these discussions, because many of their workers face an uncertain future when autonomy arrives (third point).

  1. Fair Distribution of Benefits Between Incumbents vs New Entrants

The arrival of ride-sharing companies like Uber very rapidly redistributed benefits in the transportation system, creating new winners (e.g. consumers, new drivers) and losers (e.g. taxi drivers). Yes, there were probably more winners than losers, but the losers suffered a huge impact on their livelihoods. For exampe, many taxi drivers in the U.S. invested large sums in their license – in Chicago, the median cost of a taxi medalllion in late 2013 was USD$357,000. Having the value of your medallion plummet is like losing your home!

Cities dealing with disruptive innovation need to quickly level the playing field between incumbents and new entrants, to ensure that the distribution of benefits is not overly skewed in the direction of new entrants. 

In the case of ride-sharing, issues like driver training requirements take the forefront. For example, Singapore placed 10 hours of training requirements on Uber/Grab drivers, while significantly cutting down the training hours required for taxi drivers (now, they only need to spend 16 hours on in-class training, compared to about 60 hours previously). We also cut down course fees for taxi drivers, and scrapped the daily minimum mileage – a move which helps taxi drivers minimise empty cruising just to meet their quota.

It is in the interest of disruptors to avoid a total regulatory lockdown by avoiding a zero sum mentality in these negotiations.

  1. Fair Distribution of Benefits Between Present and Future

Finally, while we reap many benefits now, there are two important long-term considerations for cities working with ride-sharing companies.

First, many ride-sharing companies are at the stage where they are flush with investment, and can afford keep their ride prices artificially low. What happens if cities “outsource” their transportation to ride- companies, which eventually raise the prices beyond what regular citizens can afford? How can cities set up their transport systems such that competition can easily arise – keeping prices in check – or that public options can bounce back quickly? A city needs to ensure that even as people reap the benefits of the shared economy today, these benefits can be sustained over time.

Second, the big elephant in the room is autonomy. Full autonomy = no more need for drivers.

Autonomy will further redistribute the benefits away from drivers towards companies, and for all we say about new jobs being created, I’m pretty sure many of these drivers won’t be the ones to do it.

Because many drivers are self-employed workers not covered by social protections, they will be in particularly difficult situations.

It will be some time before full autonomy at scale is realised, so it is not too late to start conversations on how to ensure that drivers don’t get the short end of the stick when their jobs are replaced. One immediate action companies need to take is to give drivers information. Drivers, while not employees, are stakeholders in the company’s business and should be informed about the timeframes and implications of autonomy as the field evolves. In addition, much more can be done to help them with skills and future employment, a topic I will cover soon.

Conclusion

Over a series of posts, I’ve argued that the shared economy is a net positive for society and economy. This post, I posit that we need to work together to ensure that these benefits are distributed fairly between drivers and companies, incumbents and new entrants, present and future.

This is not the ambit of cities or Governments alone; companies seeking a sustainable business model in essential public services like transportation would be wise to work closely with cities rather than to be caught in costly legislative battles, be locked out of markets, or worse still – to be exploitative in their practices.

[1] “temporary help agency workers, on-call workers, contract company workers, independent contractors or freelances)

 

Disrupting Elderly Caregiving (and why Uberisation won’t work)

I came across a really encouraging article about tech start-ups that are trying to fix the elderly caregiving sector. This is incredibly important work.

The double whammy is here 

The graphic below, from the UN, shows how our global population is aging. It will happen faster in developed countries. In Japan people above 65 already constitute more than a quarter of the population. Singapore will get there in the 2030s, and the U.S. in the 2040s.

Poppyramids.png

This will be a double whammy on societies. Countries will have a shrinking tax base to support the growing number of elderly who need care. There won’t be abundant resources to build new nursing homes and hospital beds. At the same time, it will be more difficult for the elderly to continue living at home – with shrinking family sizes, the responsibility for caregiving will fall on one or two children, instead of three or four.

Advances in biotechnology, personalised medicine and genomics will go a long way towards mitigating these challenges. For example, if companies like Calico and Unity Biotechnology manage to reverse aging, people can have longer, healthier, independent lives. The periods of time when they need caregiving will plummet. This is referred to as a longer “healthspan” (as opposed to just “lifespan”).

Unfortunately, many developed countries are already starting to face the double whammy and need a more immediate solution. From a policy perspective, it does not make sense to build new infrastructure for the elderly because these will become redundant when subsequent elderly cohorts are smaller.

The best solution is to help people receive care in their own homes as they age. It is also what a large majority (some statistics suggest 90%) of people prefer.

Why is Home Caregiving such a tough nut to crack? 

A fragmented home care sector with low standards and revolving door caregivers is the norm in many cities. I don’t see a level of innovation and investment flowing into this sector that is commensurate to its importance. Three likely reasons why this is such a tough nut to crack.

  • Home caregiving is highly personal. When someone takes an Uber ride, they don’t have to talk to the driver if they don’t want to. In contrast, a caregiver gets deep into your personal space if they bathe, feed and change your diaper. You’re obviously going to be very picky about who you choose. Pickiness is not a one-way street – caregivers have their preferences too!
  •  The job is just not a lot of fun. In fact, that’s why many people out-source care to paid contractors, even care for those they love most. It’s not glamorous, it’s repetitive, it can be highly physical, and in some cases, you interact with suffering on a daily basis. For such a difficult job, you mostly work alone without a network of support and accountability. Benefits, training and wages are patchy. Motivation is low. It is more difficult to recruit a good part-time Caregiver than a good part-time Uber driver.

What are the implications for tech companies and smart cities?

Disrupting the flailing home care sector is a classic problem that the tech, business, Government and non-profit sectors need to go at together. Here are three things that anyone working in this space needs to consider.

  1. Tackle the root problem – motivation.  

The FT article features Josh Bruno,who left Bain Capital to set up Hometeam. He started with a tech solution in mind – perhaps the equivalent of Uber for in-home care, he thought – a platform that would match caregivers to elderly and take a cut.

After volunteering in about 40 elderly care organisations, he quickly realised that the deepest issue in the caregiving sector was not matching, but motivation – “People blame them as lazy, but they are the worst working conditions — low pay, no training.” Defying all industry standards, he decided to hire the caregivers, give them good benefits  of paid holidays, maternity leave, training and retirement.

I think Josh is getting to the heart of the issue – how can you expect people to deliver quality care if you don’t care for them? The first step is turn caregiving into a worthy career. Business can’t tackle this alone, which leads me to point two.

2. Partner to make home care an economically viable business 

Josh’s company was turned away by many VCs because hiring the caregivers was not deemed to be maximally profitable.

I believe many  have not yet realized the economic benefits of a strong home care sector. When elderly are able to continue living in their own homes, a whole ecosystem of services such as tele-health, food delivery and transport has a larger market. For large healthcare providers like Kaiser Permanente, a strong home care sector combined with tele-health can help manage its elderly clients outside the expensive hospital setting. This optimizes the use of hospital facilities and doctors’ time, making the business more profitable. For the same reasons, countries where healthcare systems are operated by the Government should consider investing public funds in the home care space, for example, by extending subsidies in nursing homes to the home care sector.

Furthermore, to help the home care sector grow, the public and non-profit sectors should play a role in helping to overcome inherent”diseconomies of scale”. It does not make sense for small home care companies to have their own training programmes and qualifications system. One idea is for Governments or non-profits to offer basic, modularised caregiver training as a “shared resource” for home care companies to utilise.

3. Use technology to build a care ecosystem around the elderly 

Although it may not currently feel like it, home caregivers are really part of a team of family members, healthcare professionals and community members who help make living at home comfortable and enjoyable.

Technology can enable better communication and support between different members of this team. The home caregiver should be able to ask the healthcare professional whether a situation requires medical attention, and share information that would help the healthcare professional with diagnosis and prescription. Community members should be able to volunteer in bite-sized chunks of time, a topic I wrote about in my article “Three ways to build a transportation system that services the vulnerable”. Ultimately, when members of the team communicate, they can collectively give better care and receive support from each other. This can help to tackle the motivation problem for caregivers.

Technology can be part of the care team too! It can enable an elderly person to live independently as long as possible, only activating their caregiving team when necessary. One of the greatest fears of an elderly person is that something happens (a fall, a stroke) when they are alone. The solution, 24-hour care, is expensive and intrusive.

The Singapore Management University has been piloting sensor-enabled homes in Singapore through their SHINESeniors initiative. These sensors observe and analyze the elderly’s living patterns and immediately activate his care team when a change in pattern suggests a deterioration. Besides enabling independent living, this also allows companies and family members to allocate their time and resources efficiently.

Conclusion

Disrupting the flailing home care sector is essential to the quality of life of older folk, and the sustainability of healthcare systems when populations age. It’s a tough nut to crack, but I can’t think of a more important issue to work on right now, because our parents – and then us – are going to be the beneficiaries of positive disruption!

 

 

Three ways to build a transportation system that serves the most vulnerable

So far, I’ve talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city.

But transportation systems, even the very best, will never serve everyone equally. Where the transportation system is not inclusive, the cost is borne by some of the most vulnerable in society.

stock-disabled-01

  • People who do not just need first and last mile transport – they need first and last meter transport. This includes the growing number of elderly (>65 years old), whose population will triple by 2030 in Singapore. It also includes those who have physical disabilities through accidents, illness or congenital conditions. Where the transportation system fails to provide first and last meter support, their caregivers bear the cost. When someone, (especially in a low-income household) leaves the workforce to be a full-time caregiver, there is a huge impact on the financial wellbeing of the family. In a survey by AgingCare.com 62% of caregivers said the cost of caring for a parent had impacted their ability to plan for their own financial future.
  • People who cannot afford rides. In my college years, I volunteered with Homefront, an organization that serves the homeless in New Jersey. I vividly remember talking to a mother whose children only ever ate canned food and hot dogs because they stayed in a Motel on Route 1, and it was too expensive to get to soup kitchens for a proper meal. A once-a-week supermarket trip was all they could afford. In this case, the cost is borne by the children, in the form of health and wellness.
  • People who live or work in inaccessible areas, where it does not make economic sense to deploy a public bus or even a ride-sharing car because there is so little demand. Where the transportation system does not provide, the cost is borne by the individuals or companies who have to cater private transport.

 

None of these groups are mutually exclusive. In fact, I hazard a guess that the number of families who fulfill at least two of these three conditions is not small, and will grow with the forces of aging and inequality.

For cities to provide a truly inclusive transportation experience, we need to explore three ideas:

  1. Closing the first-and-last meter transport gap through community participation

Currently, caregivers are responsible for the first-and-last meter transport gap. If 85-year old Jim needs to go to the clinic, his caregiver helps him onto the wheelchair at home, takes the elevator to the ground floor, and helps him board either the bus or the taxi. When he arrives at the clinic, his caregiver helps him out of the bus, and into the clinic for registration.

Can volunteers fill the first-and-last meter transport gap instead? For example, when Jim orders his ride to the clinic, can a request be blasted to volunteers who are in the 200-yard radius of his home or destination? It can be a simple five to ten minute volunteering stint – helping Jim out of his home and onto the bus, or out of the bus and into the clinic for registration.

With one click of a button, Jim should be able to pick and pay for his transport menu to the clinic, and get first-and-last meter support from the community. Volunteers who choose to be in the network can do good in bite-sized chunks. They don’t need to go out of their way – they receive alerts as they go about their daily lives. Perhaps we can lighten the load of caregivers.

This idea has taken off with apps like GoodGym, where runners can sign up to visit an elderly person or help with one-off tasks while on their running route. It would be important to integrate these efforts with our transportation networks so that people like Jim can enjoy seamless transportation experiences and live independently in the community, as many elderly desire.

 

  1. Closing the affordability and accessibility gap through public-private partnerships

Take Zara, the mother of four living in a Motel on Route 1 in New Jersey. Stranded because there are no public transportation options along Route 1, while ride sharing and taxis are too expensive.

It may not make sense for the Government to provide a public bus that passes by her Motel, simply because there is too little demand. I’ve personally experienced this. I used to live in a relatively inaccessible area in Singapore. Our municipality constantly lobbied the Government to provide a new bus line to serve us. We finally got it after 2 years, but every time I boarded that bus I counted no more than 5 people on it. Great for me, but it just wasn’t a great use of public funds to deploy a $100,000 bus way below capacity. Not to mention the additional congestion we created.

Here’s one idea for Governments: instead of buying a new bus to provide a bus line in inaccessible areas, use the money to subsidize rides by private providers such that it matches the cost of public transport. Furthermore, if a family like Zara’s is eligible for subsidies on public transportation, these should be applicable when they take rides by private providers.

This will require close collaboration between the Government and private providers (yes, operational issues will not be easy!), but is the most cost-effective way of closing the accessibility and affordability gap.

Some cities in the U.S. are working on this concept. For example, The Southeastern Philadelphia Transportation Authority (SEPTA) had insufficient parking lots at their train stations to accommodate commuters who drove to the station and dropped off their cars for the day. It did not make sense to make a huge investment in building new parking lots. Last year, they partnered with uber to provide a 40% discount on Uber rides to and from rail stations, encouraging people to share rides instead of drive.

  1. Deploying autonomous vehicles

Autonomous vehicles hold tremendous promise for our objectives of inclusive transport because they will likely reduce the cost of rides. First, a bulk of a ride’s cost today is the salary of the driver. Second, companies are moving towards deploying autonomous vehicles in fleets. When vehicles are constantly utilized, companies can afford to charge less per ride. Finally, with technological advances, we can expect the hardware of autonomous vehicles, such as Lidars, to decrease in cost.

When this occurs, it will make more economic sense for companies to deploy vehicles to inaccessible areas, even if there is no promise of a return trip. Reduced prices also means that transport will be more affordable to families like Zara.

A city that plans ahead will ensure that autonomous vehicles are deployed in a way that benefits the broader population. For example, road space should not be dominated by privately-owned autonomous vehicles; Autonomous vehicle fleets should be embraced. Helping city-dwellers accept autonomous vehicles as part of their daily transportation experience is also an important part of the equation.

 

Conclusion

In my first post, I talked about how a seamless and enjoyable commute, sans car ownership, can go a long way towards mitigating the experience of inequality in a city. In my second post, I explored the ways Governments must work with private transport providers to ensure a truly seamless commute in the sharing economy – one that mimics the comfort of car ownership.

This third post covered three ways to ensure that our transportation system caters to some of the most vulnerable members of our society: community participation, public-private partnerships, and embracing autonomous vehicles. Inclusivity is an objective that is particularly close to my heart.

My final post in this series will be about the darker side of the shared economy, and how cities and business must work together to manage disruptions to our transportation system, including the rise of ride-sharing technology companies, as well as the advent of autonomy.

The sharing economy tackles one of the biggest issues every modern city faces – inequality.

Last week I spoke on a CES panel “Powering the Shared Economy to Improve the Lives of City Dwellers”. My co-panellists were Zipcar, Lyft and Grab, so our discussion naturally focused on the sharing economy in transport. Our full session was recorded here.

As the only Government representative on the panel, the inevitable question to me was – how does the sharing economy impact a city? How does it fit into our plans? How does it change the way we operate? I’ll touch on the first question for now.

I believe the impact of the sharing economy goes beyond improving transport.

It has the potential to address one of the biggest issues every modern city faces – inequality.

Companies working on ride-sharing, car-sharing and autonomous vehicle fleets have the potential to make a much more fundamental impact on society than some might think.  

1. One of a city dweller’s most acute experiences of inequality is the daily commute.

Very few of us have the rising Gini coefficient at the top of our minds, but we feel its impact when we go about our daily lives. For example, in Singapore, the daily commute is a constant reminder of luxuries we may never afford. Just five years ago, there were three ways to get around the city:

  • I buy a car. It costs $100-$150k to buy a car[1], but I get the ultimate customisation in my commute. I can leave my house whenever I want, I don’t have to wait, I sit in air-conditioned comfort. I get to my destination in half the time of the equivalent journey on public transport.
  • I take public transport, which is cheap but the experience is quite the opposite of customisation. If I’m lucky, I get to the bus stop just as my bus is pulling in. If not, I wait 10 minutes, which has a knock-on effect on catching my next bus or train. I squeeze with strangers and hardly have room to move. I walk from my bus stop to work and am drenched in sweat from the 98% humidity.
  • I take a taxi, but only if I’m desperate and/or feeling rich, and it’s not always easy to catch one. At some point, taxis were waiting outside the Central Business District during peak hours so they could make an extra buck from being called, rather than hailed.

Five years ago, the trade-off between cost and comfort in the transport experience was extremely stark. A city dweller experiences inequality when he knows he will never be able to afford the comfort of a $100-$150k car, and feels like he doesn’t have a good alternative.

2.  By providing good travel experiences without the cost of car ownership, the sharing economy reduces the experience of inequality in the daily commute. 

The sharing economy has always played a central role in moving people around the city – in the form of public transport. Too bad public transport in most places gives the sharing economy a bad name.

Fortunately, technology and business innovations have given the sharing economy a much needed boost. For example, technology has enabled people to find a ride in real-time, with the click of an iPhone button. Business innovations such as Uberpool have brought down the cost of rides – in many places, below the traditional taxi fare.[1]

As a commuter, I now have a wide range of options sandwiched between owning a car and taking public transport. On the spectrum closest to car ownership, I can get an Uber or short-term rental car (e.g. Zipcar) on demand. For a slight decrease in cost, I can share my ride with others in a LyftLine/Uberpool. If I want to trade off some flexibility for an even cheaper fare, I can submit a bid on crowd-sourced bus services like Beeline or SWAT. Even public transport has improved significantly with LTA providing real-time information on bus arrival times and crowdedness.

Importantly, this expansion of good options means that commuters don’t need to make such a stark choice between cost and comfort when deciding whether or not to buy a car. This reduces the experience of inequality in the daily commute.

3. The best has yet to come – with the promise of autonomous vehicles, participating in the sharing economy will not just be a concession, but a superior option to car ownership.

Some people are already beginning to see shared transport as a superior option to owning a personal car because of the flexibility it brings. I can choose the option which fits my lifestyle – sometimes public transport works just fine, but if I’m in a hurry or on a date, I may pay more for a more comfortable experience. Importantly, I never have to worry about where to park.

In contrast, car owners can feel compelled to use their cars even if there are better options. Behavioural economists refer to car owners in Singapore as having a “sunk cost mentality”. Put simply, once you pay a bomb to own the car, nothing – not road taxes, expensive parking, the prospect of circling the block for an hour to find an empty lot, or for some, being caught drunk-driving – will stop you from using your car, because in your mind you’ve already sunk such a huge investment and you should use it as much as you can. It can be a psychological trap.

I believe that when autonomous vehicles are ready to be deployed in fleets (imagine Uber without drivers), shared transport will become even more attractive compared to car ownership. Commuting in the shared economy can become an experience, not just a necessary evil. When cars do not need to be driven by humans, new design possibilities open up. A steering wheel and front-facing seats are no longer necessary, and a car can be configured like a meeting room, for example. A car ride can be a place to meditate, focus on work or even have wine with your friends on the way to a party.

When many different designs of vehicles are deployed in a fleet, you will be able to summon precisely the vehicle (and accompanying service) you want. In the morning you could use a minivan to ferry your family to school and work, in the evening you could summon a sleek, designer vehicle to bring you to your company’s dinner function. On the weekend, a jeep could take your family around the island for some R&R.

Today, owning a private car is the standard for luxury transportation. People make a large financial outlay upfront in exchange for on-demand, customised transportation. With fleets of autonomous vehicles deployed round-the-clock, providing the ultimate customisation in travel experience, more efficiently and without the pains of parking, this paradigm will be overturned. Shared transport will be the more affordable and customised and comfortable experience. Fewer and fewer people will aspire to own a car.

4. A transportation system dominated by the sharing economy frees up precious city space for community, housing, and commercial activities

So far, I’ve talked about how technological developments may make many of us prefer shared transport over car ownership, and how that could help mitigate our experience of inequality in the city.

If more people choose shared transport instead of car ownership, this will also enable us to use our land more equitably and progressively: think about how roads and parking spaces are disproportionately used by those who have the resources to own cars. If we can reduce the number of cars on the road, this land can be used for purposes that benefit a more diverse population such as homes, community facilities and commerce.

In cities like Singapore, where land is a constrained resource, it is even more important to make sure we use it to benefit everyone, not just those who can afford it.

5. The vision of a more equal transportation experience and society can only be realised if Governments and businesses work together. Stay tuned for more.

I’m deliberately painting an ideal picture here.

Many things can detract from this vision of a less unequal transportation experience. For example, if the business models for autonomous vehicles target only the rich, or if we fail to make multi-modal transportation seamless for commuters in the shared economy (commuters really dislike the process of transferring from a bus to a train, and vice versa).

Furthermore, I’ve mainly spoke about issues pertaining to the “middle class” Some groups have not been addressed, such as the elderly and disabled. How can we ensure that the system benefits those with limited mobility?

In my next series of posts, I will explore these issues in greater detail, and talk about how partnerships between Governments and businesses can ensure that the forces of talent and technology powering the shared economy will be used towards maximum societal and business benefit. Stay tuned!

[1] Though the extent to which fare decreases are structural versus artificially depressed by Venture Capital investment is yet to be seen, a topic I discuss at https://techandpublicgood.com/2017/02/07/the-dark-side-of-the-shared-economy-in-transport-and-three-solutions/

[1] For an explanation on why cars in Singapore are so expensive, see this link. At a macro level, it’s about restricting the supply of cars to manage traffic and road space. http://dollarsandsense.sg/no-nonsense-explanation-on-why-cars-in-singapore-are-so-expensive/

[2] If the “sharing economy” is defined as a having access to an asset that you do not own. I find this to be the most compelling definition.