Paper by Alessandro Acquisti & Catherine Tucker: “Open government holds promise of both a more efficient but more accountable and transparent government. It is not clear, however, how transparent information about citizens and their interaction with government, however, affects the welfare of those citizens, and if so in what direction. We investigate this by using as a natural experiment the effect of the online publication of the names and addresses of holders of handgun carry permits on criminals’ propensity to commit burglaries. In December 2008, a Memphis, TN newspaper published a searchable online database of names, zip codes, and ages of Tennessee handgun carry permit holders. We use detailed crime and handgun carry permit data for the city of Memphis to estimate the impact of publicity about the database on burglaries. We find that burglaries increased in zip codes with fewer gun permits, and decreased in those with more gun permits, after the database was publicized….(More)”
The Limitations of Privacy Rights
Paper by Daniel J. Solove: “Individual privacy rights are often at the heart of information privacy and data protection laws. The most comprehensive set of rights, from the European Union’s General Data Protection Regulation (GDPR), includes the right to access, right to rectification (correction), right to erasure, right to restriction, right to data portability, right to object, and right to not be subject to automated decisions. Privacy laws around the world include many of these rights in various forms.
In this article, I contend that although rights are an important component of privacy regulation, rights are often asked to do far more work than they are capable of doing. Rights can only give individuals a small amount of power. Ultimately, rights are at most capable of being a supporting actor, a small component of a much larger architecture. I advance three reasons why rights cannot serve as the bulwark of privacy protection. First, rights put too much onus on individuals when many privacy problems are systematic. Second, individuals lack the time and expertise to make difficult decisions about privacy, and rights cannot practically be exercised at scale with the number of organizations than process people’s data. Third, privacy cannot be protected by focusing solely on the atomistic individual. The personal data of many people is interrelated, and people’s decisions about their own data have implications for the privacy of other people.
The main goal of providing privacy rights aims to provide individuals with control over their personal data. However, effective privacy protection involves not just facilitating individual control, but also bringing the collection, processing, and transfer of personal data under control. Privacy rights are not designed to achieve the latter goal; and they fail at the former goal.
After discussing these overarching reasons why rights are insufficient for the oversized role they currently play in privacy regulation, I discuss the common privacy rights and why each falls short of providing significant privacy protection. For each right, I propose broader structural measures that can achieve its underlying goals in a more systematic, rigorous, and less haphazard way…(More)”.
Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities
Paper by Martin Lnenicka et al: “This paper focuses on the issue of the transparency maturity of open data ecosystems seen as the key for the development and maintenance of sustainable, citizen-centered, and socially resilient smart cities. This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data. The expert assessment of 34 portals representing 22 smart cities, with 36 features, allowed us to rank them and determine their level of transparency maturity according to four predefined levels of maturity – developing, defined, managed, and integrated. In addition, recommendations for identifying and improving the current maturity level and specific features have been provided. An open data ecosystem in the smart city context has been conceptualized, and its key components were determined. Our definition considers the components of the data-centric and data-driven infrastructure using the systems theory approach. We have defined five predominant types of current open data ecosystems based on prevailing data infrastructure components. The results of this study should contribute to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities…(More)”.
Governing AI to Advance Shared Prosperity
Chapter by Ekaterina Klinova: “This chapter describes a governance approach to promoting AI research and development that creates jobs and advances shared prosperity. Concerns over the labor-saving focus of AI advancement are shared by a growing number of economists, technologists, and policymakers around the world. They warn about the risk of AI entrenching poverty and inequality globally. Yet, translating those concerns into proactive governance interventions that would steer AI away from generating excessive levels of automation remains difficult and largely unattempted. Key causes of this difficulty arise from two types of sources: (1) insufficiently deep understanding of the full composition of factors giving AI R&D its present emphasis on labor-saving applications; and (2) lack of tools and processes that would enable AI practitioners and policymakers to anticipate and assess the impact of AI technologies on employment, wages and job quality. This chapter argues that addressing (2) will require creating worker-participatory means of differentiating between genuinely worker-benefiting AI and worker-displacing or worker-exploiting AI. To contribute to tackling (1), this chapter reviews AI practitioners’ motivations and constraints, such as relevant laws, market incentives, as well as less tangible but still highly influential constraining and motivating factors, including explicit and implicit norms in the AI field, visions of future societal order popular among the field’s members and ways that AI practitioners define goals worth pursuing and measure success. I highlight how each of these factors contributes meaningfully to giving AI advancement its excessive labor-saving emphasis and describe opportunities for governance interventions that could correct that over emphasis….(More)”.
Using ANPR data to create an anonymized linked open dataset on urban bustle
Paper by Brecht Van de Vyvere & Pieter Colpaert: “ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article’s key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making…(More)”.
AI & Society
Special Issue of Daedalus edited by James Manyika: “AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its impacts are varied. Concerns about its unintended effects and misuses have become paramount in conversations about the successful integration of AI in society. This volume explores the many facets of artificial intelligence: its technology, its potential futures, its effects on labor and the economy, its relationship with inequalities, its role in law and governance, its challenges to national security, and what it says about us as humans…(More)” See also https://aiethicscourse.org/
Rethinking gamified democracy as frictional: a comparative examination of the Decide Madrid and vTaiwan platforms
Paper by Yu-Shan Tseng: “Gamification in digital design harnesses game-like elements to create rewarding and competitive systems that encourage desirable user behaviour by influencing users’ bodily actions and emotions. Recently, gamification has been integrated into platforms built to fix democratic problems such as boredom and disengagement in political participation. This paper draws on an ethnographic study of two such platforms – Decide Madrid and vTaiwan – to problematise the universal, techno-deterministic account of digital democracy. I argue that gamified democracy is frictional by nature, a concept borrowed from cultural and social geographies. Incorporating gamification into interface design does not inherently enhance the user’s enjoyment, motivation and engagement through controlling their behaviours. ‘Friction’ in the user experience includes various emotional predicaments and tactical exploitation by more advanced users. Frictional systems in the sphere of digital democracy are neither positive nor negative per se. While they may threaten systemic inclusivity or hinder users’ abilities to organise and implement policy changes, friction can also provide new impetus to advance democratic practices…(More)”.
From “democratic erosion” to “a conversation among equals”
Paper by Roberto Gargarella: “In recent years, legal and political doctrinaires have been confusing the democratic crisis that is affecting most of our countries with a mere crisis of constitutionalism (i.e., a crisis in the way our system of “checks and balances” works). Expectedly, the result of this “diagnostic error” is that legal and political doctrinaires began to propose the wrong remedies for the democratic crisis. Usually, they began advocating for the “restoration” of the old system of “internal controls” or “checks and balances”, without paying attention to the democratic aspects of the crisis that would require, instead, the strengthening of “popular” controls and participatory mechanisms that favored the gradual emergence of a “conversation among equals”. In this work, I focus my attention on certain institutional alternatives – citizens’ assemblies and the like- that may help us overcome the present democratic crisis. In particular, I examine the recent practice of citizens’ assemblies and evaluate their functioning…(More)”.
Decoding human behavior with big data? Critical, constructive input from the decision sciences
Paper by Konstantinos V. Katsikopoulos and Marc C. Canellas: “Big data analytics employs algorithms to uncover people’s preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory….(More)”.
Making forest data fair and open
Paper by Renato A. F. de Lima : “It is a truth universally acknowledged that those in possession of time and good fortune must be in want of information. Nowhere is this more so than for tropical forests, which include the richest and most productive ecosystems on Earth. Information on tropical forest carbon and biodiversity, and how these are changing, is immensely valuable, and many different stakeholders wish to use data on tropical and subtropical forests. These include scientists, governments, nongovernmental organizations and commercial interests, such as those extracting timber or selling carbon credits. Another crucial, often-ignored group are the local communities for whom forest information may help to assert their rights and conserve or restore their forests.
A widespread view is that to lead to better public outcomes it is necessary and sufficient for forest data to be open and ‘Findable, Accessible, Interoperable, Reusable’ (FAIR). There is indeed a powerful case. Open data — those that anyone can use and share without restrictions — can encourage transparency and reproducibility, foster innovation and be used more widely, thus translating into a greater public good (for example, https://creativecommons.org). Open biological collections and genetic sequences such as GBIF or GenBank have enabled species discovery, and open Earth observation data helps people to understand and monitor deforestation (for example, Global Forest Watch). But the perspectives of those who actually make the forest measurements are much less recognized, meaning that open and FAIR data can be extremely unfair indeed. We argue here that forest data policies and practices must be fair in the correct, linguistic use of the term — just and equitable.
In a world in which forest data origination — measuring, monitoring and sustaining forest science — is secured by large, long-term capital investment (such as through space missions and some officially supported national forest inventories), making all data open makes perfect sense. But where data origination depends on insecure funding and precarious employment conditions, top-down calls to make these data open can be deeply problematic. Even when well-intentioned, such calls ignore the socioeconomic context of the places where the forest plots are located and how knowledge is created, entrenching the structural inequalities that characterize scientific research and collaboration among and within nations. A recent review found scant evidence for open data ever lessening such inequalities. Clearly, only a privileged part of the global community is currently able to exploit the potential of open forest data. Meanwhile, some local communities are de facto owners of their forests and associated knowledge, so making information open — for example, the location of valuable species — may carry risks to themselves and their forests….(More)”.