Paper by Mario L. Small: “The social sciences are in the midst of a revolution in access to data, as governments and private companies have accumulated vast digital records of rapidly multiplying aspects of our lives and made those records available to researchers. The accessibility and comprehensiveness of the data are unprecedented. How will the data revolution affect the study of social inequality? I argue that the speed, breadth, and low cost with which large-scale data can be acquired promise a dramatic transformation in the questions we can answer, but this promise can be undercut by size-induced blindness, the tendency to ignore important limitations amidst a source with billions of data points. The likely consequences for what we know about the social world remain unclear…(More)”.
Privacy and the City: How Data Shapes City Identities
Article by Bilyana Petkova: “This article bridges comparative constitutional law to research inspired by city leadership and the opportunities that technology brings to the urban environment. It looks first to some of the causes of rapid urbanization and finds them in the pitfalls of antidiscrimination law in federations and quasi-federations such as the United States and the European Union. Short of achieving antidiscrimination based on nationality, the EU has experimented with data privacy as an identity clause that could bring social cohesion the same way purportedly freedom of speech has done in the US. In the City however, diversity replaces antidiscrimination, making cities attractive to migrants across various walks of life. The consequence for federalism is the obvious decline of top-down or vertical, state-based federalism and the rise of legal urbanism whereby cities establish loose networks of cooperation between themselves. These types of arrangements are not yet a threat to the State or the EU but might become such if cities are increasingly isolated from the political process (e.g. at the EU level) and lack legal means to assert themselves in court. City diversity and openness to different cultures in turn invites a connection to new technologies since unlike antidiscrimination that is usually strictly examined on a case-by-case level, diversity can be more readily computed. Finally, the article focuses on NYC and London initiatives to suggest a futuristic vision of city networks that instead of using social credit score like in China, deploy data trusts to populate their urban environments, shape city identities and exchange ideas for urban development…(More)”.
The 2010 Census Confidentiality Protections Failed, Here’s How and Why
Paper by John M. Abowd, et al: “Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstructed records can differ from their confidential source on even a single value for these five variables. Using only published data, an attacker can verify that all records in 70% of all census blocks (97 million people) are perfectly reconstructed. The tabular publications in Summary File 1 thus have prohibited disclosure risk similar to the unreleased confidential microdata. Reidentification studies confirm that an attacker can, within blocks with perfect reconstruction accuracy, correctly infer the actual census response on race and ethnicity for 3.4 million vulnerable population uniques (persons with nonmodal characteristics) with 95% accuracy, the same precision as the confidential data achieve and far greater than statistical baselines. The flaw in the 2010 Census framework was the assumption that aggregation prevented accurate microdata reconstruction, justifying weaker disclosure limitation methods than were applied to 2010 Census public microdata. The framework used for 2020 Census publications defends against attacks that are based on reconstruction, as we also demonstrate here. Finally, we show that alternatives to the 2020 Census Disclosure Avoidance System with similar accuracy (enhanced swapping) also fail to protect confidentiality, and those that partially defend against reconstruction attacks (incomplete suppression implementations) destroy the primary statutory use case: data for redistricting all legislatures in the country in compliance with the 1965 Voting Rights Act…(More)”.
Knightian Uncertainty
Paper by Cass R. Sunstein: “In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which “there is no scientific basis on which to form any calculable probability whatever.” Knight claimed that “Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated.” Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D, and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real. Dogs face Knightian uncertainty; horses and elephants face it; human beings face it; in particular, human beings who make policy, or develop regulations, sometimes face it. Knightian uncertainty poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario (and thus to adopt the maximin rule), but serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential “miracles,” or (3) create uncertain risks…(More)”.
A synthesis of evidence for policy from behavioral science during COVID-19
Paper by Kai Ruggeri et al: “Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization…(More)”
Digital Epidemiology after COVID-19: impact and prospects
Paper by Sara Mesquita, Lília Perfeito, Daniela Paolotti, and Joana Gonçalves-Sá: “Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious disease, we review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic, in both nature and breadth. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized. Therefore, we expand on the current views and present a new definition of DE that, by highlighting the statistical nature of the datasets, helps in identifying possible biases. We offer some recommendations to reduce inequity and threats to privacy and argue in favour of complex multidisciplinary approaches to tackling infectious diseases…(More)”
Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices
Paper by Timon Elmer: “Puberty is a phase in which individuals often test the boundaries of themselves and surrounding others and further define their identity – and thus their uniqueness compared to other individuals. Similarly, as Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve scientific rigor and refine its identity. However, there are certain areas within CSS that are reluctant to adopt rigorous scientific practices from other fields, which can be observed through an overreliance on passively collected data (e.g., through digital traces, wearables) without questioning the validity of such data. This paper argues that CSS should embrace the potential of combining both passive and active measurement practices to capitalize on the strengths of each approach, including objectivity and psychological quality. Additionally, the paper suggests that CSS would benefit from integrating practices and knowledge from other established disciplines, such as measurement validation, theoretical embedding, and open science practices. Based on this argument, the paper provides ten recommendations for CSS to mature as an interdisciplinary field of research…(More)”.
Open Data Commons Licences (ODCL): Licensing personal and non personal data supporting the commons and privacy
Paper by Yaniv Benhamou and Melanie Dulong de Rosnay: “Data are often subject to a multitude of rights (e.g. original works or personal data posted on social media, or collected through captcha, subject to copyright, database and data protection) and voluntary shared through non standardized, non interoperable contractual terms. This leads to fragmented legal regimes and has become an even major challenge in the AI-era, for example when online platforms set their own Terms of Services (ToS), in business-to-consumer relationship (B2C).
This article proposes standard terms that may apply to all kind of data (including personal and mixed datasets subject to different legal regimes) based on the open data philosophy initially developed for Free and Open Source software and Creative Commons licenses for artistic and other copyrighted works. In a first part, we analyse how to extend open standard terms to all kinds of data (II). In a second part, we suggest to combine these open standard terms with collective governance instruments, in particular data trust, inspired by commons-based projects and by the centennial collective management of copyright (III). In a last part, after few concluding remarks (IV), we propose a template “Open Data Commons Licenses“ (ODCL) combining compulsory and optional elements to be selected by licensors, illustrated by pictograms and icons inspired by the bricks of Creative Commons licences and legal design techniques (V).
This proposal addresses the bargaining power imbalance and information asymmetry (by offering the licensor the ability to decide the terms), and conceptualises contract law differently. It reverses the current logic of contract: instead of letting companies (licensees) impose their own ToS to the users (licensors, being the copyright owner, data subject, data producer), licensors will reclaim the ability to set their own terms for access and use of data, by selecting standard terms. This should also allow the management of complex datasets, increase data sharing, and improve trust and control over the data. Like previous open licencing standards, the model is expected to lower the transaction costs by reducing the need to develop and read new complicated contractual terms. It can also spread the virality of open data to all data in an AI-era, if any input data under such terms used for AI training purposes propagates its conditions to all aggregated and output data. In other words, any data distributed under our ODCL template will turn all outcome into more or less open data and foster a data common ecosystem. Finally, instead of full openness, our model allows for restrictions outside of certain boundaries (e.g. authorized users and uses), in order to protect the commons and certain values. The model would require to be governed and monitored by a collective data trust…(More)”.
Populist Leaders and the Economy
Paper by Manuel Funke, Moritz Schularick and Christoph Trebesch: “Populism at the country level is at an all-time high, with more than 25 percent of nations currently governed by populists. How do economies perform under populist leaders? We build a new long-run cross-country database to study the macroeconomic history of populism. We identify 51 populist presidents and prime ministers from 1900 to 2020 and show that the economic cost of populism is high. After 15 years, GDP per capita is 10 percent lower compared to a plausible nonpopulist counterfactual. Economic disintegration, decreasing macroeconomic stability, and the erosion of institutions typically go hand in hand with populist rule…(More)”.
Steering Responsible AI: A Case for Algorithmic Pluralism
Paper by Stefaan G. Verhulst: “In this paper, I examine questions surrounding AI neutrality through the prism of existing literature and scholarship about mediation and media pluralism. Such traditions, I argue, provide a valuable theoretical framework for how we should approach the (likely) impending era of AI mediation. In particular, I suggest examining further the notion of algorithmic pluralism. Contrasting this notion to the dominant idea of algorithmic transparency, I seek to describe what algorithmic pluralism may be, and present both its opportunities and challenges. Implemented thoughtfully and responsibly, I argue, Algorithmic or AI pluralism has the potential to sustain the diversity, multiplicity, and inclusiveness that are so vital to democracy…(More)”.