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.

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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.

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

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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.

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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?

 

 

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!

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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.

 

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!

 

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  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.

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Source: New York Times

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.

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

Better Consumer Access AND System-level Sustainability: Can Cities Have Both?

 

This week I read three parallel articles: one on healthcare, two on transport, all with the same theme: how the introduction of disruptive technology in traditional ‘public services’ led to a flood of new demand, calling sustainability into question.

I’ve thus far painted a positive picture of how new technologies can democratize access to services: Riding in the comfort of a private vehicle is no longer restricted to those who have money to own a car. Tele-health, where patients can consult their doctors online rather than face-to-face, is cheaper and more accessible than a traditional doctor’s visit, cutting down unnecessary waiting and travelling time (issues that disproportionately affect the poor and elderly!).

But improving access often leads to a surge in demand, creating new problems for society. These articles point towards an important trade-off between consumer access and system-level health that I haven’t quite addressed. [Spoiler alert: we should care about both because they are ultimately about the consumer!]

Transport

“The Downside of Ride-Hailing: New York City Gridlock” empirically shows how ride sharing has worsened congestion in NYC because many have replaced their subway rides with an Uber or Lyft. “Average travel speeds in the heart of Manhattan dropped to about 8.1 miles per hour last year, down about 12 percent from 2010”. New Yorkers have famously pushed back against their Mayor’s attempts to restrict the number of Uber cars.

“Autonomous Vehicles: Hype and Potential” shows how autonomous vehicles can also exacerbate traffic congestion, slowing down the movement of people and goods around the city.

  • One of the promises of autonomy is that the car can be re-imagined. IDEO imagined how cars might become work-spaces in the picture below. Once the car becomes a comfortable place to work or relax, many of us might not mind spending more time on the roads. I might opt for an Uberpool even if takes twice the time of a train journey because it’s such a comfortable, productive ride.
  • If these autonomous vehicles are privately owned, people might send their cars on trips they would normally take. For example, sending their car to the McDonald’s drive-through, or far out of the city center to find cheap parking.
  • We will also take some time to get to roads where vehicles are 100% autonomous. In the interim, human drivers are likely to “bully” autonomous vehicles because they know that these autonomous vehicles are programmed to be risk-averse (an autonomous vehicle killing a person is perceived as a greater travesty than a distracted driver killing a person). In such a scenario, we will see autonomous vehicles driving at slower-than optimal speeds, creating more congestion.

Source: IDEO

Autonomous work spaces

Healthcare

The parallel in the healthcare system is a study by RAND Corporation, showing how only 12% of tele-health visits have replaced visits to the doctor, while 88% represented new use of medical services. Unsurprisingly, this finding suggests that doctors’ visits are highly price-elastic – by halving the cost, we see a surge in new demand. Net annual spending on healthcare among patients with respiratory illnesses increased by US$45 per tele-health user.

This is a bigger problem if the new users actually didn’t need to see a doctor and a smaller one if they would have deteriorated if not for the medical treatment. The answer is likely somewhere in between – I believe closer to the former – 88% is huge (But a more in-depth study correlating the new use of medical services with health outcomes is needed). There is potential for tremendous waste in our already-stretched healthcare systems if we massively lower the cost of healthcare services without creating disincentives for unnecessary usage.

How can we get the best of both worlds: access and sustainability?

Technologies have amazing potential to help us use scarce resources like doctors’ time and road space more efficiently, creating greater supply. By lowering cost, they also ensure that this greater supply is spread out more evenly across the population, regardless of income.

However, doctors’ time and road space are ultimately still scarce resources that need to be rationed somehow. Capitalist countries are happy to ration these services by income. Countries on the socialist end of the spectrum (think the UK National Health System) tend to ration by waiting time. Neither fully takes into account the most important consideration: need and urgency.

How can we incentivize people to only use these new, accessible services only when they really need it? Here are some ideas.

In transportation

In transportation, cities need to make mass people-mover systems (trains, buses) the core service used by most commuters: ride-sharing must complement, not replace trains and buses. The bulk of commuters should spend most of their journey in trains and buses where the road space per commuter is significantly lower. Ride-sharing can be a first-mile and last-mile solution (e.g. home to train station), but certainly not the default for the whole journey.

To achieve this, cities need to up their game in public transportation. It has to at least be reliable and predictable (which many, many aren’t). Examples of how Singapore has done this here and here. Taking a step further, payments and arrival/departure times should be integrated with ride-sharing platforms so that people can minimize waiting and inconvenience when transiting between ride-sharing and public transportation. Work-friendly design in public transportation (think flip-out work tables in public buses) will also help make these options less unattractive compared to IDEO’s self-driving pods.

When it comes to autonomy, cities also need to think about moving to 100% autonomous vehicles as quickly as possible, since the dynamics between human drivers and autonomous cars will likely increase congestion. A 100% autonomous vehicle scenario also creates the most gains in efficiency and safety – vehicles can travel bumper to bumper (more efficient use of roads) and provide information to each other about road and traffic conditions (safety and efficiency are both enhanced). I cover some strategies in this article though this is a topic worth exploring in greater depth.

Finally, slightly more “interventionist” policies may be needed, such as limiting private-use autonomous vehicles and rationing the total number of cars dedicated to ride sharing so that people are prodded towards mass people-mover systems like trains and buses.

Tech companies sometimes paint these suggestions as the Government acting against the consumer interest. I disagree: it is in the commuter and patients’ interest if we can manage the demands on our roads and doctors such that those who need it most can get the services in an affordable and timely manner.

In healthcare

In healthcare, raising co-payments is a commonly-used tool which helps people think twice before using a service. “Triaging” patients is another way – for example, having them first speak to a nurse practitioner and only passing them to the doctors if it is needed.

But let’s take the patient’s perspective for a minute. What’s motivating them to use a service they may not need? Anxiety that their condition may be more serious than they think, and lack of a place to clarify (short of calling up a doctor). Any new parent empathizes with this. I probably went to the doctor every week in the first month of my daughter’s birth for no good reason at all.

We need solutions that assuage a patients’ anxiety. I believe equipping home caregivers is going to be a big part of this. Home caregiving is currently an informal sector with minimal training, which is an incredible waste. Imagine if home caregivers could be the first line of defence – giving the patient assurance when they do not need a doctor, and quickly helping them access a doctors’ time when it is urgent.

If healthcare systems and healthcare insurance providers want to use tele-health to optimise their use of resources, the technology has to be complemented by human-centred solutions that assuage patients’ anxiety. If not, the technology won’t save them any money at all!

Conclusion

I hope that with the addition of this article, I’ve now painted a fuller picture of the impact of disruptive technologies on public services like transportation and healthcare. Indeed, they will make resources more abundant and accessible to people with lower-incomes. However, complementary policies and services are absolutely necessary to ensure that the system is not over-used – ultimately, so that those who really need the services can get it in both a timely and affordable manner.

 

“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.

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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.

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.