Stefaan Verhulst

World Bank Report: “Robust, inclusive, and responsible identification systems can increase access to finance, healthcare, education, and other critical services and benefits. Identification systems are also key to improving efficiency and enabling innovation for public- and private-sector services, such as greater efficiency in the delivery of social safety nets and facilitating the development of digital economies. However, the World Bank estimates that more than 1.1 billion individuals do not have official proof of their identity.10 New technologies provide countries with the opportunity to leapfrog paper-based systems and rapidly establish a robust identification infrastructure. As a result, the countries are increasingly adopting nationwide digital identification (ID) programs and leveraging them in other sectors.

Whether a country is enhancing existing ID systems or implementing new systems from the ground up, technology choices are critical to the success of digital identification systems. A number of new technologies are emerging to enable various aspects of ID lifecycle. For some of these technologies, no large-scale studies have been done; for others, current speculation makes objective evaluations difficult.

This report is a first attempt to develop a comprehensive overview of the current technology landscape for digital identification. It is intended to serve as a framework for understanding the myriad options and considerations of technology in this rapidly advancing agenda and in no way is intended to provide advice on specific technologies, particularly given there are a number of other considerations and country contexts which need to be considered. This report also does not advocate the use of a certain technology from a particular vendor for any particular application.

While some technologies are relatively easy to use and affordable, others are costly or so complex that using them on a large scale presents daunting challenges. This report provides practitioners with an overview of various technologies and advancements that are especially relevant for digital identification systems. It highlights key benefits and challenges associated with each technology. It also provides a framework for assessing each technology on multiple criteria, including length of time it has been in use, its ease of integration with legacy and future systems, and its interoperability with other technologies. The practitioners and stakeholders who read this are reminded to bear in mind that the technologies associated with ID systems are rapidly evolving, and that this report, prepared in early 2018, is a snapshot in time. Therefore, technology limitations and challenges highlighted in this report today may not be applicable in the years to come….(More)”

Tina Rosenberg at the New York Times: “The first social impact bond began in 2010 in Peterborough, England. Investors funded a program aimed at keeping newly released short-term inmates out of prison. It reduced reoffending by 9 percent compared to a control group, exceeding its target. So investors got their money back, plus interest.

Seldom has a policy idea gone viral so fast. There are now 108 such bonds, in 24 countries. The United States has 20, leveraging $211 million in investment capital, and at least 50 more are on the way. These bonds fund programs to reduce Oklahoma’s population of women in prison, help low-income mothers to have healthy pregnancies in South Carolina, teach refugees and immigrants English and job skills in Boston, house the homeless in Denver, and reduce storm water runoff in the District of Columbia. There’s a Forest Resilience Bond underway that seeks to finance desperately needed wildfire prevention.

Here’s how social impact bonds differ from standard social programs:

They raise upfront money to do prevention. Everyone knows most prevention is a great investment. But politicians don’t do “think ahead” very well. They hate to spend money now to create savings their successors will reap. Issuing a social impact bond means they don’t have to.

They concentrate resources on what works. Bonds build market discipline, since investors demand evidence of success.

They focus attention on outcomes rather than outputs. “Take work-force training,” said David Wilkinson, commissioner of Connecticut’s Office of Early Childhood. “We tend to pay for how many people receive training. We’re less likely to pay for — or even look at — how many people get good jobs.” Providers, he said, were best recognized for their work “when we reward them for outcomes they want to see and families they are serving want to achieve.”

They improve incentives.Focusing on outcomes changes the way social service providers think. In Connecticut, said Duryea, they now have a financial incentive to keep children out of foster care, rather than bring more in.

They force decision makers to look at data. Programs start with great fanfare, but often nobody then examines how they are doing. But with a bond, evaluation is essential.

They build in flexibility.“It’s a big advantage that they don’t prescribe what needs to be done,” said Cohen. The people on the ground choose the strategy, and can change it if necessary. “Innovators can think outside the box and tackle health or education in revolutionary ways,” he said.

…In the United States, social impact bonds have become synonymous with “pay for success” programs. But there are other ways to pay for success. For example, Wilkinson, the Connecticut official, has just started an Outcomes Rate Card — a way for a government to pay for home visits for vulnerable families. The social service agencies get base pay, but also bonuses. If a client has a full-term birth, the agency gets an extra $135 for a low-risk family, $170 for a hard-to-help one. A client who finds stable housing brings $150 or $220 to the agency, depending on the family’s situation….(More)”.

Joseph D Tucker at the Journal of  Medical Internet Research: “Crowdsourcing contests (also called innovation challenges, innovation contests, and inducement prize contests) can be used to solicit multisectoral feedback on health programs and design public health campaigns. They consist of organizing a steering committee, soliciting contributions, engaging the community, judging contributions, recognizing a subset of contributors, and sharing with the community.

Objective: This scoping review describes crowdsourcing contests by stage, examines ethical problems at each stage, and proposes potential ways of mitigating risk.

Methods: Our analysis was anchored in the specific example of a crowdsourcing contest that our team organized to solicit videos promoting condom use in China. The purpose of this contest was to create compelling 1-min videos to promote condom use. We used a scoping review to examine the existing ethical literature on crowdsourcing to help identify and frame ethical concerns at each stage.

Results: Crowdsourcing has a group of individuals solve a problem and then share the solution with the public. Crowdsourcing contests provide an opportunity for community engagement at each stage: organizing, soliciting, promoting, judging, recognizing, and sharing. Crowdsourcing poses several ethical concerns: organizing—potential for excluding community voices; soliciting—potential for overly narrow participation; promoting—potential for divulging confidential information; judging—potential for biased evaluation; recognizing—potential for insufficient recognition of the finalist; and sharing—potential for the solution to not be implemented or widely disseminated.

Conclusions: Crowdsourcing contests can be effective and engaging public health tools but also introduce potential ethical problems. We present methods for the responsible conduct of crowdsourcing contests… (More)”.

Open Access Book by Justin Parkhurst: “There has been an enormous increase in interest in the use of evidence for public policymaking, but the vast majority of work on the subject has failed to engage with the political nature of decision making and how this influences the ways in which evidence will be used (or misused) within political areas. This book provides new insights into the nature of political bias with regards to evidence and critically considers what an ‘improved’ use of evidence would look like from a policymaking perspective.

Part I describes the great potential for evidence to help achieve social goals, as well as the challenges raised by the political nature of policymaking. It explores the concern of evidence advocates that political interests drive the misuse or manipulation of evidence, as well as counter-concerns of critical policy scholars about how appeals to ‘evidence-based policy’ can depoliticise political debates. Both concerns reflect forms of bias – the first representing technical bias, whereby evidence use violates principles of scientific best practice, and the second representing issue bias in how appeals to evidence can shift political debates to particular questions or marginalise policy-relevant social concerns.

Part II then draws on the fields of policy studies and cognitive psychology to understand the origins and mechanisms of both forms of bias in relation to political interests and values. It illustrates how such biases are not only common, but can be much more predictable once we recognise their origins and manifestations in policy arenas.

Finally, Part III discusses ways to move forward for those seeking to improve the use of evidence in public policymaking. It explores what constitutes ‘good evidence for policy’, as well as the ‘good use of evidence’ within policy processes, and considers how to build evidence-advisory institutions that embed key principles of both scientific good practice and democratic representation. Taken as a whole, the approach promoted is termed the ‘good governance of evidence’ – a concept that represents the use of rigorous, systematic and technically valid pieces of evidence within decision-making processes that are representative of, and accountable to, populations served….(More)”.

Paper by Michael McGann, Emma Blomkamp and Jenny M. Lewis in Policy Sciences: “Governments are increasingly turning to public sector innovation (PSI) labs to take new approaches to policy and service design. This turn towards PSI labs, which has accelerated in more recent years, has been linked to a number of trends. These include growing interest in evidence-based policymaking and the application of ‘design thinking’ to policymaking, although these trends sit uncomfortably together. According to their proponents, PSI labs are helping to create a new era of experimental government and rapid experimentation in policy design.

But what do these PSI labs do? How do they differ from other public sector change agents and policy actors? What approaches do they bring to addressing contemporary policymaking? And how do they relate to other developments in policy design such as the growing interest in evidence-based policy and design experiments? The rise of PSI labs has thus far received little attention from policy scientists. Focusing on the problems associated with conceptualising PSI labs and clearly situating them in the policy process, this paper provides an analysis of some of the most prominent PSI labs. It examines whether labs can be classified into distinct types, their relationship to government and other policy actors and the principal methodological practices and commitments underpinning their approach to policymaking. Throughout, the paper considers how the rise of PSI labs may challenge positivist framings of policymaking as an empirically driven decision process….(More)”.

Paper by Rachel Baker, Thomas Dee, Brent Evans and June John: “While online learning environments are increasingly common, relatively little is known about issues of equity in these settings. We test for the presence of race and gender biases among postsecondary students and instructors in online classes by measuring student and instructor responses to discussion comments we posted in the discussion forums of 124 different online courses. Each comment was randomly assigned a student name connoting a specific race and gender. We find that instructors are 94% more likely to respond to forum posts by White male students. In contrast, we do not find general evidence of biases in student responses. However, we do find that comments placed by White females are more likely to receive a response from White female peers. We discuss the implications of our findings for our understanding of social identity dynamics in classrooms and the design of equitable online learning environments….(More)”.

Benson S. Hsu, MD and Emily Griese in Harvard Business Review: “At Sanford Health, a $4.5 billion rural integrated health care system, we deliver care to over 2.5 million people in 300 communities across 250,000 square miles. In the process, we collect and store vast quantities of patient data – everything from admission, diagnostic, treatment and discharge data to online interactions between patients and providers, as well as data on providers themselves. All this data clearly represents a rich resource with the potential to improve care, but until recently was underutilized. The question was, how best to leverage it.

While we have a mature data infrastructure including a centralized data and analytics team, a standalone virtual data warehouse linking all data silos, and strict enterprise-wide data governance, we reasoned that the best way forward would be to collaborate with other institutions that had additional and complementary data capabilities and expertise.

We reached out to potential academic partners who were leading the way in data science, from university departments of math, science, and computer informatics to business and medical schools and invited them to collaborate with us on projects that could improve health care quality and lower costs. In exchange, Sanford created contracts that gave these partners access to data whose use had previously been constrained by concerns about data privacy and competitive-use agreements. With this access, academic partners are advancing their own research while providing real-world insights into care delivery.

The resulting Sanford Data Collaborative, now in its second year, has attracted regional and national partners and is already beginning to deliver data-driven innovations that are improving care delivery, patient engagement, and care access. Here we describe three that hold particular promise.

• Developing Prescriptive Algorithms…
• Augmenting Patient Engagement…
• Improving Access to Care…(More)”.

Yomi Kazeem in Quartz: “On Mar. 7, elections in Sierra Leone marked a global landmark: the world’s first ever blockchain-powered presidential elections….

In Sierra Leone’s Western District, the most populous in the country, votes cast were manually recorded by Agora, a Swiss foundation offering digital voting solutions, using a permissioned blockchain. The idea was simple: just like blockchain technology helps ensure transparency with crytpocurrency transactions using public ledgers, by recording each vote on blockchain, Agora ensured transparency with votes cast in the district. While entries on permissioned blockchains can be viewed by everyone, entries can only be validated by authorized persons.

A lack of transparency has plagued many elections around the world, but particularly in some African countries where large sections of the electorate are often suspicions incumbent parties or ethnic loyalties have been responsible for the manipulation of the results in favor of one candidate or another. These suspicions remain even when there is little evidence of manipulation. A more transparent system could help restore trust.

Leonardo Gammar, CEO of Agora, says Sierra Leone’s NEC was “open minded” about the potential of blockchain in its elections after talks began late last year. “I also thought that if we can do it in Sierra Leone, we can do it everywhere else,” he says. That thinking is rooted in Sierra Leone’s developmental challenges which make electoral transparency difficult: poor network connectivity, low literacy levels and frequent electoral violence.

The big picture for Agora is to deploy solutions to automate the entire electoral process with citizens voting electronically using biometric data and personalized cryptographic keys and the votes in turn validated by blockchain. Gammar hopes Agora can replicate its work in other African elections on a larger scale but admits that doing so will require understanding the differing challenges each country faces.

Gammar says blockchain-powered electronic voting will be cheaper for African countries by cutting out the printing cost of paper-based elections but perhaps, more importantly, vastly reduce electoral violence…(More)”.

Fei-Fei Li in the New York Times: “For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.

It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: A.I. has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.

I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.

I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines.

First, A.I. needs to reflect more of the depth that characterizes our own intelligence….

No technology is more reflective of its creators than A.I. It has been said that there are no “machine” values at all, in fact; machine values arehuman values. A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility….(More).