Making Open Data more evidence-based

Curated on Posted on by Stefaan Verhulst

Essay by Stefaan G. Verhulst and Danny Lämmerhirt: “…To realize its potential there is a need for more evidence on the full life cycle of open data – within and across settings and sectors….

In particular, three substantive areas were identified that could benefit from interdisciplinary and comparative research:

Demand and use: First, many expressed a need to become smarter about the demand and use-side of open data. Much of the focus, given the nascent nature of many initiatives around the world, has been on the supply-side of open data. Yet to be more responsive and sustainable more insight needs to be gained to the demand and/or user needs.

Conversations repeatedly emphasized that we should differentiate between open data demand and use. Open data demand and use can be analyzed from multiple directions: 1) top-down, starting from a data provider, to intermediaries, to the end users and/or audiences; or 2) bottom-up, studying the data demands articulated by individuals (for instance, through FOIA requests), and how these demands can be taken up by intermediaries and open data providers to change what is being provided as open data.

Research should scrutinize each stage (provision, intermediation, use and demand) on its own, but also examine the interactions between stages (for instance, how may open data demand inform data supply, and how does data supply influence intermediation and use?)….

Informing data supply and infrastructure: Second, we heard on numerous occasions, a call upon researchers and domain experts to help in identifying “key data” and inform the government data infrastructure needed to provide them. Principle 1 of the International Open Data Charter states that governments should provide key data “open by default”, yet the questions remains in how to identify “key” data (e.g., would that mean data relevant to society at large?).

Which governments (and other public institutions) should be expected to provide key data and which information do we need to better understand government’s role in providing key data? How can we evaluate progress around publishing these data coherently if countries organize the capture, collection, and publication of this data differently?…

Impact: In addition to those two focus areas – covering the supply and demand side –  there was also a call to become more sophisticated about impact. Too often impact gets confused with outputs, or even activities. Given the embryonic and iterative nature of many open data efforts, signals of impact are limited and often preliminary. In addition, different types of impact (such as enhancing transparency versus generating innovation and economic growth) require different indicators and methods. At the same time, to allow for regular evaluations of what works and why there is a need for common assessment methods that can generate comparative and directional insights….

Research Networking: Several researchers identified a need for better exchange and collaboration among the research community. This would allow to tackle the research questions and challenges listed above, as well as to identify gaps in existing knowledge, to develop common research methods and frameworks and to learn from each other. Key questions posed involved: how to nurture and facilitate networking among researchers and (topical) experts from different disciplines, focusing on different issues or using different methods? How are different sub-networks related or disconnected with each other (for instance how connected are the data4development; freedom of information or civic tech research communities)? In addition, an interesting discussion emerged around how researchers can also network more with those part of the respective universe of analysis – potentially generating some kind of participatory research design….(More)”

The power of prediction markets

Curated on Posted on by Stefaan Verhulst

Adam Mann in Nature: “It was a great way to mix science with gambling, says Anna Dreber. The year was 2012, and an international group of psychologists had just launched the ‘Reproducibility Project’ — an effort to repeat dozens of psychology experiments to see which held up1. “So we thought it would be fantastic to bet on the outcome,” says Dreber, who leads a team of behavioural economists at the Stockholm School of Economics.

In particular, her team wanted to see whether scientists could make good use of prediction markets: mini Wall Streets in which participants buy and sell ‘shares’ in a future event at a price that reflects their collective wisdom about the chance of the event happening. As a control, Dreber and her colleagues first asked a group of psychologists to estimate the odds of replication for each study on the project’s list. Then the researchers set up a prediction market for each study, and gave the same psychologists US$100 apiece to invest.

When the Reproducibility Project revealed last year that it had been able to replicate fewer than half of the studies examined2, Dreber found that her experts hadn’t done much better than chance with their individual predictions. But working collectively through the markets, they had correctly guessed the outcome 71% of the time3.

Experiments such as this are a testament to the power of prediction markets to turn individuals’ guesses into forecasts of sometimes startling accuracy. That uncanny ability ensures that during every US presidential election, voters avidly follow the standings for their favoured candidates on exchanges such as Betfair and the Iowa Electronic Markets (IEM). But prediction markets are increasingly being used to make forecasts of all kinds, on everything from the outcomes of sporting events to the results of business decisions. Advocates maintain that they allow people to aggregate information without the biases that plague traditional forecasting methods, such as polls or expert analysis….

Prediction markets have also had some high-profile misfires, however — such as giving the odds of a Brexit ‘stay’ vote as 85% on the day of the referendum, 23 June. (UK citizens in fact narrowly voted to leave the European Union.) And prediction markets lagged well behind conventional polls in predicting that Donald Trump would become the 2016 Republican nominee for US president.

Such examples have inspired academics to probe prediction markets. Why do they work as well as they do? What are their limits, and why do their predictions sometimes fail?…(More)”

 

Crowdsourcing Gun Violence Research

Curated on Posted on by Stefaan Verhulst

Penn Engineering: “Gun violence is often described as an epidemic, but as visible and shocking as shooting incidents are, epidemiologists who study that particular source of mortality have a hard time tracking them. The Centers for Disease Control is prohibited by federal law from conducting gun violence research, so there is little in the way of centralized infrastructure to monitor where, how,when, why and to whom shootings occur.

Chris Callison-Burch, Aravind K.Joshi Term Assistant Professor in Computer and InformationScience, and graduate studentEllie Pavlick are working to solve this problem.

They have developed the GunViolence Database, which combines machine learning and crowdsourcing techniques to produce a national registry of shooting incidents. Callison-Burch and Pavlick’s algorithm scans thousands of articles from local newspaper and television stations,determines which are about gun violence, then asks everyday people to pullout vital statistics from those articles, compiling that information into a unified, open database.

For natural language processing experts like Callison-Burch and Pavlick, the most exciting prospect of this effort is that it is training computer systems to do this kind of analysis automatically. They recently presented their work on that front at Bloomberg’s Data for Good Exchange conference.

The Gun Violence Database project started in 2014, when it became the centerpiece of Callison-Burch’s “Crowdsourcing and Human Computation”class. There, Pavlick developed a series of homework assignments that challenged undergraduates to develop a classifier that could tell whether a given news article was about a shooting incident.

“It allowed us to teach the things we want students to learn about datascience and natural language processing, while giving them the motivation to do a project that could contribute to the greater good,” says Callison-Burch.

The articles students used to train their classifiers were sourced from “TheGun Report,” a daily blog from New York Times reporters that attempted to catalog shootings from around the country in the wake of the Sandy Hook massacre. Realizing that their algorithmic approach could be scaled up to automate what the Times’ reporters were attempting, the researchers began exploring how such a database could work. They consulted with DouglasWiebe, a Associate Professor of Epidemiology in Biostatistics andEpidemiology in the Perelman School of Medicine, to learn more about what kind of information public health researchers needed to better study gun violence on a societal scale.

From there, the researchers enlisted people to annotate the articles their classifier found, connecting with them through Mechanical Turk, Amazon’scrowdsourcing platform, and their own website, http://gun-violence.org/…(More)”

Reframing Data Transparency

Curated on Posted on by Stefaan Verhulst

“Recently, the Centre for Information Policy Leadership (“CIPL”) at Hunton & Williams LLP, a privacy and information policy think tank based in Brussels, London and Washington, D.C., and Telefónica, one of the largest telecommunications company in the world, issued a joint white paper on Reframing Data Transparency (the “white paper”). The white paper was the outcome of a June 2016 roundtable held by the two organizations in London, in which senior business leaders, Data Privacy Officers, lawyers and academics discussed the importance of user-centric transparency to the data driven economy….The issues explored during the roundtable and in the white paper include the following:

  • The transparency deficit in the digital age. There is a growing gap between traditional, legal privacy notices and user-centric transparency that is capable of delivering understandable and actionable information concerning an organization’s data use policies and practices, including why it processes data, what the benefits are to individuals and society, how it protects the data and how users can manage and control the use of their data.
  • The impact of the transparency deficit. The transparency deficit undermines customer trust and customers’ ability to participate more effectively in the digital economy.
  • Challenges of delivering user-centric transparency. In a connected world where there may be no direct relationship between companies and their end users, both transparency and consent as a basis for processing are particularly challenging.
  • Transparency as a multistakeholder challenge. Transparency is not solely a legal issue, but a multistakeholder challenge, which requires engagement of regulators, companies, individuals, behavioral economists, social scientists, psychologists and user experience specialists.
  • The role of data protection authorities (“DPAs”). DPAs play a key role in promoting and incentivizing effective data transparency approaches and tools.
  • The role of companies. Data transparency is a critical business issue because transparency drives digital trust as well as business opportunities. Organizations must innovate on how to deliver user-centric transparency. Data driven companies must research and develop new approaches to transparency that explain the value exchange between customers and companies and the companies’ data practices, and create tools that enable their customers to exercise effective engagement and control.
  • The importance of empowering individuals. It is crucial to support and enhance individuals’ digital literacy, which includes an understanding of the uses of personal data and the benefits of data processing, as well as knowledge of relevant privacy rights and the data management tools that are available to them. Government bodies, regulators and industry should be involved in educating the public regarding digital literacy. Such education should take place in schools and universities, and through consumer education campaigns. Transparency is the foundation and sine qua non of individual empowerment.
  • The role of behavioral economists, social scientists, psychologists and user experience specialists. Experts from these disciplines will be crucial in developing user-centric transparency and controls….(More)”.

Empowering cities

Curated on Posted on by Stefaan Verhulst

“The real story on how citizens and businesses are driving smart cities” by the Economist Intelligence Unit: “Digital technologies are the lifeblood of today’s cities. They are applied widely in industry and society, from information and communications technology (ICT) to the Internet of Things (IoT), in which objects are connected to the Internet. As sensors turn any object into part of an intelligent urban network, and as computing power facilitates analysis of the data these sensors collect, elected officials and city administrators can gain an unparalleled understanding of the infrastructure and services of their city. However, to make the most of this intelligence, another ingredient is essential: citizen engagement. Thanks to digital technologies, citizens can provide a steady flow of feedback and ideas to city officials.

This study by The Economist Intelligence Unit (EIU), supported by Philips Lighting, investigates how citizens and businesses in 12 diverse cities around the world—Barcelona, Berlin, Buenos Aires, Chicago, London, Los Angeles, Mexico City, New York City, Rio de Janeiro, Shanghai, Singapore and Toronto—envision the benefits of smart cities. The choices of the respondents to the survey reflect the diverse nature of the challenges and opportunities facing different cities, from older cities in mature markets, where technology is at work with infrastructure that may be centuries old, to new cities in emerging markets, which have the opportunity to incorporate digital technologies as they grow.

Coupled with expert perspectives, these insights paint a fresh picture of how digital technologies can empower people to contribute-giving city officials a roadmap to smart city life in the 21st century….(More)”

Nudging Health

Curated on Posted on by Stefaan Verhulst

Book edited by I. Glenn Cohen, Holly Fernandez Lynch, and Christopher T. Robertson: “Behavioral nudges are everywhere: calorie counts on menus, automated text reminders to encourage medication adherence, a reminder bell when a driver’s seatbelt isn’t fastened. Designed to help people make better health choices, these reminders have become so commonplace that they often go unnoticed. In Nudging Health, forty-five experts in behavioral science and health policy from across academia, government, and private industry come together to explore whether and how these tools are effective in improving health outcomes.

Behavioral science has swept the fields of economics and law through the study of nudges, cognitive biases, and decisional heuristics—but it has only recently begun to impact the conversation on health care.Nudging Health wrestles with some of the thorny philosophical issues, legal limits, and conceptual questions raised by behavioral science as applied to health law and policy. The volume frames the fundamental issues surrounding health nudges by addressing ethical questions. Does cost-sharing for health expenditures cause patients to make poor decisions? Is it right to make it difficult for people to opt out of having their organs harvested for donation when they die? Are behavioral nudges paternalistic? The contributors examine specific applications of behavioral science, including efforts to address health care costs, improve vaccination rates, and encourage better decision-making by physicians. They wrestle with questions regarding the doctor-patient relationship and defaults in healthcare while engaging with larger, timely questions of healthcare reform.

Nudging Health is the first multi-voiced assessment of behavioral economics and health law to span such a wide array of issues—from the Affordable Care Act to prescription drugs….(More)”

The Fix: How Nations Survive and Thrive in a World in Decline

Curated on Posted on by Stefaan Verhulst

Budgeting for Equity: How Can Participatory Budgeting Advance Equity in the United States?

Curated on Posted on by Stefaan Verhulst

Josh Lerner and Madeleine Pape in the Journal of Public Deliberation: “Participatory budgeting (PB) has expanded dramatically in the United States (US) from a pilot process in Chicago’s 49th ward in 2009 to over 50 processes in a dozen cities in 2015. Over this period, scholars, practitioners, and advocates have made two distinct but related claims about its impacts: that it can revitalize democracy and advance equity. In practice, however, achieving the latter has often proven challenging. Based on interviews with PB practitioners from across the US, we argue that an equitydriven model of PB is not simply about improving the quality of deliberation or reducing barriers to participation. While both of these factors are critically important, we identify three additional challenges: 1) Unclear Goals: how to clearly define and operationalize equity, 2) Participant Motivations: how to overcome the agendas of individual budget delegates, and 3) Limiting Structures: how to reconfigure the overarching budgetary and bureaucratic constraints that limit PB’s contribution to broader change. We suggest practical interventions for each of these challenges, including stronger political leadership, extending idea collection beyond the initial brainstorming phase, increasing opportunities for interaction between PB participants and their non-participating neighbors, expanding the scope of PB processes, and building stronger linkages between PB and other forms of political action….(More)”

One Crucial Thing Can Help End Violence Against Girls

Curated on Posted on by Stefaan Verhulst

Eleanor Goldberg at The Huffington Post: “…There are statistics that demonstrate how many girls are in school, for example. But there’s a glaring lack of information on how many of them have dropped out ― and why ― concluded a new study, “Counting the Invisible Girls,” published this month by Plan International.

Why Data On Women And Girls Is Crucial

Without accurate information about the struggles girls face, such as abuse, child marriage, and dropout rates, governments and nonprofit groups can’t develop programs that cater to the specific needs of underserved girls. As a result, struggling girls across the globe, have little chance of escaping the problems that prevent them from pursuing an education and becoming economically independent.

“If data used for policy-making is incomplete, we have a real challenge. Current data is not telling the full story,” Emily Courey Pryor, senior director of Data2X, said at the Social Good Summit in New York City last month. Data2X is a U.N.-led group that works with data collectors and policymakers to identify gender data issues and to help bring about solutions.

Plan International released its report to coincide with a number of major recent events….

How Data Helps Improve The Lives Of Women And Girls 

While data isn’t a panacea, it has proven in a number of instances to help marginalized groups.

Until last year, it was legal in Guatemala for a girl to marry at age 14 ― despite the numerous health risks associated with the practice. Young brides are more vulnerable to sexual abuse and more likely to face fatal complications related to pregnancy and childbirth than those who marry later.

To urge lawmakers to raise the minimum age of marriage, Plan International partnered with advocates and civil society groups to launch its “Because I am a Girl” initiative. It analyzed traditional Mayan laws and gathered evidence about the prevalence of child marriage and its impact on children’s lives. The group presented the information before Guatemala’s Congress and in August of last year, the minimum age for marriage was raised to 18.

A number of groups are heeding the call to continue to amass better data.

In May, the Bill and Melinda Gates Foundation pledged $80 million over the next three years to gather robust and reliable data.

In September, the U.N. women announced “Making Every Woman and Girl Count,”a public-private partnership that’s working to tackle the data issue. The program was unveiled at the U.N. General Assembly, and is working with the Gates Foundation, Data2X and a number of world leaders…(More)”

The Promise of Artificial Intelligence: 70 Real-World Examples

Curated on Posted on by Stefaan Verhulst

Report by the Information Technology & Innovation Foundation: “Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the Defense Advanced Research Projects Agency to spur development of autonomous vehicles. In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances.

Moreover, AI is already having a major positive impact in many different sectors of the global economy and society.  For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress.

To showcase the overwhelmingly positive impact of AI, this report describes the major uses of AI and highlights 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI….(More)”