Stefaan Verhulst

Biometric Mirror

Curated on August 29, 2018July 18, 2019 by Stefaan Verhulst

University of Melbourne: “Biometric Mirror exposes the possibilities of artificial intelligence and facial analysis in public space. The aim is to investigate the attitudes that emerge as people are presented with different perspectives on their own, anonymised biometric data distinguished from a single photograph of their face. It sheds light on the specific data that people oppose and approve, the sentiments it evokes, and the underlying reasoning. Biometric Mirror also presents an opportunity to reflect on whether the plausible future of artificial intelligence is a future we want to see take shape.

Big data and artificial intelligence are some of today’s most popular buzzwords. Both are promised to help deliver insights that were previously too complex for computer systems to calculate. With examples ranging from personalised recommendation systems to automatic facial analyses, user-generated data is now analysed by algorithms to identify patterns and predict outcomes. And the common view is that these developments will have a positive impact on society.

Within the realm of artificial intelligence (AI), facial analysis gains popularity. Today, CCTV cameras and advertising screens increasingly link with analysis systems that are able to detect emotions, age, gender and demographic information of people passing by. It has proven to increase advertising effectiveness in retail environments, since campaigns can now be tailored to specific audience profiles and situations. But facial analysis models are also being developed to predict your aggression level, sexual preference, life expectancy and likeliness of being a terrorist (or an academic) by simply monitoring surveillance camera footage or analysing a single photograph. Some of these developments have gained widespread media coverage for their innovative nature, but often the ethical and social impact is only a side thought.

Current technological developments approach ethical boundaries of the artificial intelligence age. Facial recognition and analysis in public space raise concerns as people are photographed without prior consent, and their photos disappear into a commercial operator’s infrastructure. It remains unclear how the data is processed, how the data is tailored for specific purposes and how the data is retained or disposed of. People also do not have the opportunity to review or amend their facial recognition data. Perhaps most worryingly, artificial intelligence systems may make decisions or deliver feedback based on the data, regardless of its accuracy or completeness. While facial recognition and analysis may be harmless for tailored advertising in retail environments or to unlock your phone, it quickly pushes ethical boundaries when the general purpose is to more closely monitor society… (More).

Online Bettors Can Sniff Out Weak Psychology Studies

Curated on August 29, 2018October 9, 2018 by Stefaan Verhulst

Ed Yong at the Atlantic: “Psychologists are in the midst of an ongoing, difficult reckoning. Many believe that their field is experiencing a “reproducibility crisis,” because they’ve tried and failed to repeat experiments done by their peers. Even classic results—the stuff of textbooks and TED talks—have proven surprisingly hard to replicate, perhaps because they’re the results of poor methods and statistical tomfoolery. These problems have spawned a community of researchers dedicated to improving the practices of their field and forging a more reliable way of doing science.

These attempts at reform have met resistance. Critics have argued that the so-called crisis is nothing of the sort, and that researchers who have failed to repeat past experiments were variously incompetent, prejudiced, or acting in bad faith.

But if those critiques are correct, then why is it that scientists seem to be remarkably good at predicting which studies in psychology and other social sciences will replicate, and which will not?

Consider the new results from the Social Sciences Replication Project, in which 24 researchers attempted to replicate social-science studies published between 2010 and 2015 in Nature and Science—the world’s top two scientific journals. The replicators ran much bigger versions of the original studies, recruiting around five times as many volunteers as before. They did all their work in the open, and ran their plans past the teams behind the original experiments. And ultimately, they could only reproduce the results of 13 out of 21 studies—62 percent.

As it turned out, that finding was entirely predictable. While the SSRP team was doing their experimental re-runs, they also ran a “prediction market”—a stock exchange in which volunteers could buy or sell “shares” in the 21 studies, based on how reproducible they seemed. They recruited 206 volunteers—a mix of psychologists and economists, students and professors, none of whom were involved in the SSRP itself. Each started with $100 and could earn more by correctly betting on studies that eventually panned out.

At the start of the market, shares for every study cost $0.50 each. As trading continued, those prices soared and dipped depending on the traders’ activities. And after two weeks, the final price reflected the traders’ collective view on the odds that each study would successfully replicate. So, for example, a stock price of $0.87 would mean a study had an 87 percent chance of replicating. Overall, the traders thought that studies in the market would replicate 63 percent of the time—a figure that was uncannily close to the actual 62-percent success rate.

The traders’ instincts were also unfailingly sound when it came to individual studies. Look at the graph below. The market assigned higher odds of success for the 13 studies that were successfully replicated than the eight that weren’t—compare the blue diamonds to the yellow diamonds….(More)”.

The rise of policy innovation labs: A catalog of policy innovation labs across Canada

Curated on August 28, 2018 by Stefaan Verhulst

Report by the Centre for Policy Innovation and Public Engagement (CPIPE): “In recent years, governments all over the world have been embracing new and innovative ways to develop public policies and design public services, from crowdsourcing to human-centred design thinking. This trend in government innovation has led to the rise of the Policy Innovation Lab (PIL): individual units, both inside and outside of government, that apply the traditional principles of scientific laboratories – experimentation, testing, and measurement – to social problems.

PILs are an increasingly important development in public policy making, with a variety of methods and approaches to building relationships between governments, organizations, and citizens, and generating ideas and designing policy. Yet, these labs are under-researched: many are established without a full understanding of their role and value to the policy community. We aim to address this knowledge gap, and create opportunities where policy innovators can make connections with their peers and learn about the current practices and applications of policy innovation from one another.

This report identifies the innovation labs in Canada, profiling their methodologies, projects, and partners, mapping the policy innovation landscape across the country. Each one-page summary provides a profile for each lab, and highlights the existing innovation practices and networks in the public, academic, non-profit, and private sectors, and identifies methodological and ideological trends across the different labs and networks.

This report is the first of its kind in North America. In this highly dynamic space, new labs are emerging and disappearing all the time. The purpose of this report is to put a spotlight on policy innovations and their successes, and to build and strengthen connections between researchers, policymakers, and policy innovators. Through a strengthened and sustained community of practice, we hope to see governments continue to embrace new approaches for effective policymaking…(More)”.

Why Proven Solutions Struggle to Scale Up

Curated on August 28, 2018 by Stefaan Verhulst

Kriss Deiglmeier & Amanda Greco at Stanford Social Innovation Review: “…As we applied the innovation continuum to the cases we studied, we identified barriers to scale that often trap social innovations in a stagnation chasm before they achieve diffusion and scaling.

Three barriers in particular repeatedly block social innovations from reaching their broadest impact: inadequate funds for growth, the fragmented nature of the social innovation ecosystem, and talent gaps. If we are serious about propelling proven social innovations to achieve widespread impact, we must find solutions that overcome each of these barriers. The rest of the article will explore in more detail each of these three barriers in turn.

1. Inadequate Funding

Social innovators face a convoluted and often elusive path to mobilize the resources needed to amplify the impact of their work. Of the strategies for scale in Mulgan’s typology, some are very capital intensive, others less so. Yet even the advocacy and network approaches to scaling social impact require resources. It takes time, funding, and expertise to navigate the relationships and complex interdependencies that are critical to success. Some ventures may benefit from earned revenue streams that provide funds for growth, but earned revenue isn’t guaranteed in the social innovation space, especially for innovations that operate where markets fail to meet needs and serve people with no ability to pay. Thus, external funding is usually needed in order to scale impact, whether from donors or from investors depending on the legal structure and financial prospects of the venture….

2. A Fragmented Ecosystem

One sector toiling in isolation or digging into an adversarial approach cannot achieve breakthrough scale on its own. Instead, engaging and coordinating actions across various actors from the private, nonprofit, and public sectors is critical. In the case of microfinance, for example, the innovation garnered interest from government and business when nonprofits like Grameen Bank had demonstrated success in providing financial services to formerly unbanked people.

Following the pioneering role of nonprofits to establish proof of concept, commercial banks entered the market, with mixed social outcomes, given the pressure they faced for profitability. As the microfinance industry matured, governments created a legal and regulatory environment that encouraged transparency, market entry, and competition. The cumulative efforts and engagement across the nonprofit, private, and public sectors were critical to scaling microfinance as we know it today and will continue to refine the approach for better social outcomes in the future…

3. The Talent Gap

To drive social innovations in a world of rapid change, organizations need talented leaders supported by effective teams. The insufficient funding and fragmented ecosystem require highly adept people to shepherd social innovations through the long journey to widespread social impact. Unfortunately, attracting and retaining people to navigate these complexities is a challenge…(More)”.

One of New York City’s most urgent design challenges is invisible

Curated on August 28, 2018 by Stefaan Verhulst

Diana Budds at Curbed: “Algorithms are invisible, but they already play a large role in shaping New York City’s built environment, schooling, public resources, and criminal justice system. Earlier this year, the City Council and Mayor Bill de Blasio formed the Automated Decision Systems Task Force, the first of its kind in the country, to analyze how NYC deploys automated systems to ensure fairness, equity, and accountability are upheld.

This week, 20 experts in the field of civil rights and artificial intelligence co-signed a letter to the task force to help influence its official report, which is scheduled to be published in December 2019.

The letter’s recommendations include creating a publicly accessible list of all the automated decision systems in use; consulting with experts before adopting an automated decision system; creating a permanent government body to oversee the procurement and regulation of automated decision systems; and upholding civil liberties in all matters related to automation. This could lay the groundwork for future legislation around automation in the city….Read the full letter here.”

Winners Take All

Curated on August 28, 2018 by Stefaan Verhulst

Book by Anand Giridharadas: “… takes us into the inner sanctums of a new gilded age, where the rich and powerful fight for equality and justice any way they can–except ways that threaten the social order and their position atop it. We see how they rebrand themselves as saviors of the poor; how they lavishly reward “thought leaders” who redefine “change” in winner-friendly ways; and how they constantly seek to do more good, but never less harm. We hear the limousine confessions of a celebrated foundation boss; witness an American president hem and haw about his plutocratic benefactors; and attend a cruise-ship conference where entrepreneurs celebrate their own self-interested magnanimity.

Giridharadas asks hard questions: Why, for example, should our gravest problems be solved by the unelected upper crust instead of the public institutions it erodes by lobbying and dodging taxes? He also points toward an answer: Rather than rely on scraps from the winners, we must take on the grueling democratic work of building more robust, egalitarian institutions and truly changing the world. A call to action for elites and everyday citizens alike….(More)”.

The Smart Transition: An Opportunity for a Sensor-Based Public-Health Risk Governance?

Curated on August 28, 2018 by Stefaan Verhulst

Anna Berti Suman in the International Review of Law, Computers & Technology: “This contribution analyses the promises and challenges of using bottom-up produced sensors data to manage public-health risks in the (smart) city. The article criticizes traditional ways of governing public-health risks with the aim to inspect the contribution that a sensor-based risk governance may bring to the fore. The failures of the top-down model serve to illustrate that the smart transformation of the city’s living environments may stimulate a better public-health risk governance and a new city’s utopia.

The central question this contribution addresses is: How could the potential of a city’s network of sensors and of datainfrastructures contribute to smartly realizing healthier cities, free from environmental risk? The central aim of the article is to reflect on the opportunity to combine top-down and bottom-up sensing approaches. In view of this aim, the complementary potential of top and bottom sensing is inspected. Citizen sensing practices are discussed as manifestation of the new public sphere and a taxonomy for a sensor-based risk governance is developed. The challenges hidden behind this arguably inclusive transition are dismantled….(More)”.

With real-time decisions, Citi Bike breaks the cycle of empty stations

Curated on August 28, 2018 by Stefaan Verhulst

Melanie Lefkowitz at Cornell Chronicle: “Cornell research has improved bike sharing in New York and other cities, providing tools to ensure bikes are available when and where they’re needed through a crowdsourcing system that uses real-time information to make decisions.

Citi Bike redistributes its bicycles around New York City using a program called Bike Angels, based on research by David Shmoys, the Laibe/Acheson Professor of Business Management and Leadership Studies in the School of Operations Research and Information Engineering.

Through Bike Angels, which Shmoys helped Citi Bike develop three years ago, cyclists earn points adding up to free rides and other prizes by using or returning bikes at certain high-need stations. Originally, Bike Angels awarded points for the same pattern of stations every morning, and a different fixed pattern each afternoon rush; now the program uses an algorithm that continually updates the pattern of stations for which users earn points.

“The ability to make decisions that are sensitive to exactly what are today’s conditions enables us to be much more effective in assigning those points,” said Shmoys, who is also associate director of Cornell’s Institute for Computational Sustainability.

With co-authors Hangil Chung ’18 and Daniel Freund, Ph.D. ’18, Shmoys wrote “Bike Angels: An Analysis of Citi Bike’s Incentive Program,” a detailed report showing the effectiveness of this approach. …(More)”.