The paper investigates the rise of Big Data in contemporary society. It examines the most prominent epistemological claims made by Big Data proponents, calls attention to the potential socio-political consequences of blind data trust, and proposes a possible way forward. The paper’s main focus is on the interplay between an emerging new empiricism and an increasingly opaque algorithmic environment that challenges democratic demands for transparency and accountability. It concludes that a responsible culture of quantification requires epistemic vigilance as well as a greater awareness of the potential dangers and pitfalls of an ever more data-driven society….(More)”.
Who serves the poor ? surveying civil servants in the developing world
Worldbank working paper by Daniel Oliver Rogger: “Who are the civil servants that serve poor people in the developing world? This paper uses direct surveys of civil servants — the professional body of administrators who manage government policy — and their organizations from Ethiopia, Ghana, Indonesia, Nigeria, Pakistan and the Philippines, to highlight key aspects of their characteristics and experience of civil service life. Civil servants in the developing world face myriad challenges to serving the world’s poor, from limited facilities to significant political interference in their work. There are a number of commonalities across service environments, and the paper summarizes these in a series of ‘stylized facts’ of the civil service in the developing world. At the same time, the particular challenges faced by a public official vary substantially across and within countries and regions. For example, measured management practices differ widely across local governments of a single state in Nigeria. Surveys of civil servants allow us to document these differences, build better models of the public sector, and make more informed policy choices….(More)”.
Crowdsourcing Accountability: ICT for Service Delivery
Paper by Guy Grossman, Melina Platas and Jonathan Rodden: “We examine the effect on service delivery outcomes of a new information communication technology (ICT) platform that allows citizens to send free and anonymous messages to local government officials, thus reducing the cost and increasing the efficiency of communication about public services. In particular, we use a field experiment to assess the extent to which the introduction of this ICT platform improved monitoring by the district, effort by service providers, and inputs at service points in health, education and water in Arua District, Uganda. Despite relatively high levels of system uptake, enthusiasm of district officials, and anecdotal success stories, we find evidence of only marginal and uneven short-term improvements in health and water services, and no discernible long-term effects. Relatively few messages from citizens provided specific, actionable information about service provision within the purview and resource constraints of district officials, and users were often discouraged by officials’ responses. Our findings suggest that for crowd-sourced ICT programs to move from isolated success stories to long-term accountability enhancement, the quality and specific content of reports and responses provided by users and officials is centrally important….(More)”.
Artificial Intelligence and Public Policy
Paper by Adam D. Thierer, Andrea Castillo and Raymond Russell: “There is growing interest in the market potential of artificial intelligence (AI) technologies and applications as well as in the potential risks that these technologies might pose. As a result, questions are being raised about the legal and regulatory governance of AI, machine learning, “autonomous” systems, and related robotic and data technologies. Fearing concerns about labor market effects, social inequality, and even physical harm, some have called for precautionary regulations that could have the effect of limiting AI development and deployment. In this paper, we recommend a different policy framework for AI technologies. At this nascent stage of AI technology development, we think a better case can be made for prudence, patience, and a continuing embrace of “permissionless innovation” as it pertains to modern digital technologies. Unless a compelling case can be made that a new invention will bring serious harm to society, innovation should be allowed to continue unabated, and problems, if they develop at all, can be addressed later…(More)”.
Unnatural Surveillance: How Online Data Is Putting Species at Risk
Adam Welz at YaleEnvironment360: “…The burgeoning pools of digital data from electronic tags, online scientific publications, “citizen science” databases and the like – which have been an extraordinary boon to researchers and conservationists – can easily be misused by poachers and illegal collectors. Although a handful of scientists have recently raised concerns about it, the problem is so far poorly understood.
Today, researchers are surveilling everything from blue whales to honeybees with remote cameras and electronic tags. While this has had real benefits for conservation, some attempts to use real-time location data in order to harm animals have become known: Hunters have shared tips on how to use VHF radio signals from Yellowstone National Park wolves’ research collars to locate the animals. (Although many collared wolves that roamed outside the park have been killed, no hunter has actually been caught tracking tag signals.) In 2013, hackers in India apparently successfully accessed tiger satellite-tag data, but wildlife authorities quickly increased security and no tigers seem to have been harmed as a result. Western Australian government agents used a boat-mounted acoustic tag detector to hunt tagged white sharks in 2015. (At least one shark was killed, but it was not confirmed whether it was tagged). Canada’s Banff National Park last year banned VHF radio receivers after photographers were suspected of harassing tagged animals.
While there is no proof yet of a widespread problem, experts say it is often in researchers’ and equipment manufacturers’ interests to underreport abuse. Biologist Steven Cooke of Carleton University in Canada lead-authored a paper this year cautioning that the “failure to adopt more proactive thinking about the unintended consequences of electronic tagging could lead to malicious exploitation and disturbance of the very organisms researchers hope to understand and conserve.” The paper warned that non-scientists could easily buy tags and receivers to poach animals and disrupt scientific studies, noting that “although telemetry terrorism may seem far-fetched, some fringe groups and industry players may have incentives for doing so.”…(More)”.
Data-Driven Policy Making: The Policy Lab Approach
Societal challenges such as migration, poverty, and climate change can be considered ‘wicked problems’ for which no optimal solution exists. To address such problems, public administrations increasingly aim for data–driven policy making. Data-driven policy making aims to make optimal use of sensor data, and collaborate with citizens to co-create policy. However, few public administrations have realized this so far. Therefore, in this paper an approach for data-driven policy making is developed that can be used in the setting of a Policy Lab. A Policy Lab is an experimental environment in which stakeholders collaborate to develop and test policy. Based on literature, we first identify innovations in data-driven policy making. Subsequently, we map these innovations to the stages of the policy cycle. We found that most innovations are concerned with using new data sources in traditional statistics and that methodologies capturing the benefits of data-driven policy making are still under development. Further research should focus on policy experimentation while developing new methodologies for data-driven policy making at the same time….(More)”.
Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing
Priscilla Guo, Danielle Kehl, and Sam Kessler at Responsive Communities (Harvard): “In the summer of 2016, some unusual headlines began appearing in news outlets across the United States. “Secret Algorithms That Predict Future Criminals Get a Thumbs Up From the Wisconsin Supreme Court,” read one. Another declared: “There’s software used across the country to predict future criminals. And it’s biased against blacks.” These news stories (and others like them) drew attention to a previously obscure but fast-growing area in the field of criminal justice: the use of risk assessment software, powered by sophisticated and sometimes proprietary algorithms, to predict whether individual criminals are likely candidates for recidivism. In recent years, these programs have spread like wildfire throughout the American judicial system. They are now being used in a broad capacity, in areas ranging from pre-trial risk assessment to sentencing and probation hearings. This paper focuses on the latest—and perhaps most concerning—use of these risk assessment tools: their incorporation into the criminal sentencing process, a development which raises fundamental legal and ethical questions about fairness, accountability, and transparency. The goal is to provide an overview of these issues and offer a set of key considerations and questions for further research that can help local policymakers who are currently implementing or considering implementing similar systems. We start by putting this trend in context: the history of actuarial risk in the American legal system and the evolution of algorithmic risk assessments as the latest incarnation of a much broader trend. We go on to discuss how these tools are used in sentencing specifically and how that differs from other contexts like pre-trial risk assessment. We then delve into the legal and policy questions raised by the use of risk assessment software in sentencing decisions, including the potential for constitutional challenges under the Due Process and Equal Protection clauses of the Fourteenth Amendment. Finally, we summarize the challenges that these systems create for law and policymakers in the United States, and outline a series of possible best practices to ensure that these systems are deployed in a manner that promotes fairness, transparency, and accountability in the criminal justice system….(More)”.
Who Falls for Fake News? The Roles of Analytic Thinking, Motivated Reasoning, Political Ideology, and Bullshit Receptivity
Paper by Gordon Pennycook and David G. Rand: “Inaccurate beliefs pose a threat to democracy and fake news represents a particularly egregious and direct avenue by which inaccurate beliefs have been propagated via social media. Here we investigate the cognitive psychological profile of individuals who fall prey to fake news. We find a consistent positive correlation between the propensity to think analytically – as measured by the Cognitive Reflection Test (CRT) – and the ability to differentiate fake news from real news (“media truth discernment”). This was true regardless of whether the article’s source was indicated (which, surprisingly, also had no main effect on accuracy judgments). Contrary to the motivated reasoning account, CRT was just as positively correlated with media truth discernment, if not more so, for headlines that aligned with individuals’ political ideology relative to those that were politically discordant. The link between analytic thinking and media truth discernment was driven both by a negative correlation between CRT and perceptions of fake news accuracy (particularly among Hillary Clinton supporters), and a positive correlation between CRT and perceptions of real news accuracy (particularly among Donald Trump supporters). This suggests that factors that undermine the legitimacy of traditional news media may exacerbate the problem of inaccurate political beliefs among Trump supporters, who engaged in less analytic thinking and were overall less able to discern fake from real news (regardless of the news’ political valence). We also found consistent evidence that pseudo-profound bullshit receptivity negatively correlates with perceptions of fake news accuracy; a correlation that is mediated by analytic thinking. Finally, analytic thinking was associated with an unwillingness to share both fake and real news on social media. Our results indicate that the propensity to think analytically plays an important role in the recognition of misinformation, regardless of political valence – a finding that opens up potential avenues for fighting fake news….(More)”.
What does it mean to be differentially private?
Paul Francis at IAPP: “Back in June 2016, Apple announced it will use differential privacy to protect individual privacy for certain data that it collects. Though already a hot research topic for over a decade, this announcement introduced differential privacy to the broader public. Before that announcement, Google had already been using differential privacy for collecting Chrome usage statistics. And within the last month, Uber announced that they too are using differential privacy.
If you’ve done a little homework on differential privacy, you may have learned that it provides provable guarantees of privacy and concluded that a database that is differentially private is, well, private — in other words, that it protects individual privacy. But that isn’t necessarily the case. When someone says, “a database is differentially private,” they don’t mean that the database is private. Rather, they mean, “the privacy of the database can be measured.”
Really, it is like saying that “a bridge is weight limited.” If you know the weight limit of a bridge, then yes, you can use the bridge safely. But the bridge isn’t safe under all conditions. You can exceed the weight limit and hurt yourself.
The weight limit of bridges is expressed in tons, kilograms or number of people. Simplifying here a bit, the amount of privacy afforded by a differentially private database is expressed as a number, by convention labeled ε (epsilon). Lower ε means more private.
All bridges have a weight limit. Everybody knows this, so it sounds dumb to say, “a bridge is weight limited.” And guess what? All databases are differentially private. Or, more precisely, all databases have an ε. A database with no privacy protections at all has an ε of infinity. It is pretty misleading to call such a database differentially private, but mathematically speaking, it is not incorrect to do so. A database that can’t be queried at all has an ε of zero. Private, but useless.
In their paper on differential privacy for statistics, Cynthia Dwork and Adam Smith write, “The choice of ε is essentially a social question. We tend to think of ε as, say, 0.01, 0.1, or in some cases, ln 2 or ln 3.” The natural logarithm of 3 (ln 3) is around 1.1….(More)”.
Crowdsourcing citizen science: exploring the tensions between paid professionals and users
Jamie Woodcock et al in the Journal of Peer Production: “This paper explores the relationship between paid labour and unpaid users within the Zooniverse, a crowdsourced citizen science platform. The platform brings together a crowd of users to categorise data for use in scientific projects. It was initially established by a small group of academics for a single astronomy project, but has now grown into a multi-project platform that has engaged over 1.3 million users so far. The growth has introduced different dynamics to the platform as it has incorporated a greater number of scientists, developers, links with organisations, and funding arrangements—each bringing additional pressures and complications. The relationships between paid/professional and unpaid/citizen labour have become increasingly complicated with the rapid expansion of the Zooniverse. The paper draws on empirical data from an ongoing research project that has access to both users and paid professionals on the platform. There is the potential through growing peer-to-peer capacity that the boundaries between professional and citizen scientists can become significantly blurred. The findings of the paper, therefore, address important questions about the combinations of paid and unpaid labour, the involvement of a crowd in citizen science, and the contradictions this entails for an online platform. These are considered specifically from the viewpoint of the users and, therefore, form a new contribution to the theoretical understanding of crowdsourcing in practice….(More)”.