Paper by Jean Marie Tshimula et al: “Over the years, there seems to be a unidirectional top-down approach to decision-making in providing social services to the masses. This has often led to poor uninformed decisions being made with outcomes which do not necessarily match needs. Similarly from the grassroots level, it has been challenging to give opinions that reach the governing authorities (decision-making organs). The government consequently sets targets geared towards addressing societal concerns, but which do not often achieve desired results where such government endeavors are not in harmony with societal needs.
With public opinions being heard and given consideration, societal needs can be better known and priorities set to address these concerns. This paper therefore presents a priority-based voting model for governments to collect public opinion data that bring suggestions to boost their endeavors in the right direction using crowdsourcing and big data analytics….(More)”.
Paper by Erna Ruijer et al: “This article contributes to the growing body of literature within public management on open government data by taking
a political perspective. We argue that open government data are a strategic resource of organizations and therefore organizations are not likely to share it. We develop an analytical framework for studying the politics of open government data, based on theories of strategic responses to institutional processes, government transparency, and open government data. The framework shows that there can be different organizational strategic responses to open data—varying from conformity to active resistance—and that different institutional antecedents influence these responses. The value of the framework is explored in two cases: a province in the Netherlands and a municipality in France. The cases provide insights into why governments might release datasets in certain policy domains but not in others thereby producing “strategically opaque transparency.” The article concludes that the politics of open government data framework helps us understand open data practices in relation to broader institutional pressures that influence government transparency….(More)”.
Paper by James Andreoni and Marta Serra-Garcia: “What is the value of pledges if they are often reneged upon? In this paper we show – both theoretically and experimentally – that pledges can be used to screen donors and to better understand their motives for giving. In return, nonprofit managers can use the information they glean from pledges to better target future charitable giving appeals and interventions to donors, such as expressions of gratitude. In an experiment, we find that offering the option to pledge gifts induces self-selection. If expressions of gratitude are then targeted to individuals who select into pledges, reneging can be significantly reduced. Our findings provide an explanation for the potential usefulness of pledges….(More)”.
Paper by Bart Cammaerts and Robin Mansell: “This article considers challenges to policy and regulation presented by the dominant digital platforms. A radical democratic framing of the deliberative process is developed to acknowledge the full complexity of power relations that are in play in policy and regulatory debates and this view is contrasted with a liberal democratic perspective.
We show how these different framings have informed historical and contemporary approaches to the challenges presented by conflicting interests in economic value and a range of public values in the context of media content, communication infrastructure and digital platform policy and regulation. We argue for an agonistic approach to digital platform policy and regulatory debate so as to encourage a denaturalization of the prevailing logics of commercial datafication. We offer some suggestions about how such a generative discourse might be encouraged in such a way that it starts to yield a new common sense about the further development of digital platforms; one that might favor a digital ecology better attuned to consumer and citizen interests in democratic societies….(More)”.
Paper by Feijuan He et al: “Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research….(More)”
Essay by Jamie Grace: “This essay is an introductory exploration of machine learning technologies and their inherent human rights issues in criminal justice contexts. These inherent human rights issues include privacy concerns, the chilling of freedom of expression, problems around potential for racial discrimination, and the rights of victims of crime to be treated with dignity.
This essay is built around three case studies – with the first on the digital ‘mining’ of rape complainants’ mobile phones for evidence for disclosure to defence counsel. This first case study seeks to show how AI or machine learning tech might hypothetically either ease or inflame some of the tensions involved for human rights in this context. The second case study is concerned with the human rights challenges of facial recognition of suspects by police forces, using automated algorithms (live facial recognition) in public places. The third case study is concerned with the development of useful self-regulation in algorithmic governance practices in UK policing. This essay concludes with an emphasis on the need for the ‘politics of information’ (Lyon, 2007) to catch up with the ‘politics of public protection’ (Nash, 2010)….(More)”.
A special section of Internet Policy Review edited by Christian Katzenbach and Thomas Christian Bächle: “With this new special section Defining concepts of the digital society in Internet Policy Review, we seek to foster a platform that provides and validates exactly these overarching frameworks and theories. Based on the latest research, yet broad in scope, the contributions offer effective tools to analyse the digital society. Their authors offer concise articles that portray and critically discuss individual concepts with an interdisciplinary mindset. Each article contextualises their origin and academic traditions, analyses their contemporary usage in different research approaches and discusses their social, political, cultural, ethical or economic relevance and impact as well as their analytical value. With this, the authors are building bridges between the disciplines, between research and practice as well as between innovative explanations and their conceptual heritage….(More)”
Christian Katzenbach, Alexander von Humboldt Institute for Internet and Society
Lena Ulbricht, Berlin Social Science Center
Ulises A. Mejias, State University of New York at Oswego
Nick Couldry, London School of Economics & Political Science
Axel Bruns, Queensland University of Technology
Thomas Poell, University of Amsterdam
David Nieborg, University of Toronto
José van Dijck, Utrecht University
Tobias Matzner, University of Paderborn
Carsten Ochs, University of Kassel
Paper by Quinton Mayne, Jorrit De Jong, and Fernando Fernandez-Monge: “Governments around the world are increasingly recognizing the power of problem-oriented governance as a way to address complex public problems. As an approach to policy design and implementation, problem-oriented governance radically emphasizes the need for organizations to continuously learn and adapt. Scholars of public management, public administration, policy studies, international development, and political science have made important contributions to this problem-orientation turn; however, little systematic attention has been paid to the question of the state capabilities that underpin problem-oriented governance. In this article, we address this gap in the literature.
We argue that three core capabilities are structurally conducive to problem-oriented governance: a reflective-improvement capability, a collaborative capability, and a data-analytic capability. The article presents a conceptual framework for understanding each of these capabilities, including their chief constituent elements. It ends with a discussion of how the framework can advance empirical research as well as public-sector reform….(More)”.
Paper by Mark A. Rothstein et al: “Mobile devices with health apps, direct-to-consumer genetic testing, crowd-sourced information, and other data sources have enabled research by new classes of researchers. Independent researchers, citizen scientists, patient-directed researchers, self-experimenters, and others are not covered by federal research regulations because they are not recipients of federal financial assistance or conducting research in anticipation of a submission to the FDA for approval of a new drug or medical device. This article addresses the difficult policy challenge of promoting the welfare and interests of research participants, as well as the public, in the absence of regulatory requirements and without discouraging independent, innovative scientific inquiry. The article recommends a series of measures, including education, consultation, transparency, self-governance, and regulation to strike the appropriate balance….(More)”.
Paper by Lorenzo Barberis Canonico, Christopher Flathmann, Dr. Nathan McNeese: “There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence….(More)”.