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  1. Modeling Ethics: Approaches to Data Creep in Higher Education.Madisson Whitman - 2021 - Science and Engineering Ethics 27 (6):1-18.
    Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...)
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  • Carceral algorithms and the history of control: An analysis of the Pennsylvania additive classification tool.Nathan C. Ryan, Darakhshan Mir, Swarup Dhar & Vanessa A. Massaro - 2022 - Big Data and Society 9 (1).
    Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool used to classify prisoners’ custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure (...)
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  • Fixing Technology with Society: The Coproduction of Democratic Deficits and Responsible Innovation at the OECD and the European Commission.Sebastian Pfotenhauer, Tess Doezema & Nina Frahm - 2022 - Science, Technology, and Human Values 47 (1):174-216.
    Long presented as a universal policy-recipe for social prosperity and economic growth, the promise of innovation seems to be increasingly in question, giving way to a new vision of progress in which society is advanced as a central enabler of technoeconomic development. Frameworks such as “Responsible” or “Mission-oriented” Innovation, for example, have become commonplace parlance and practice in the governance of the innovation–society nexus. In this paper, we study the dynamics by which this “social fix” to technoscience has gained legitimacy (...)
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  • Vulnerable Integrity.Tabea Ott & Peter Dabrock - 2023 - De Ethica 7 (3):47-60.
    This paper presents a social-theologically informed interpretation of the term integrity, as it occurs in fundamental law. It explores the manifestations of integrity violations and proceeds to draw an inference: an integrity violation can directly emanate from a misconception regarding integrity itself, as well as the implementation of protective measures that follow it. Integrity in its wholeness dimension is understood as open-endedness and non-seclusion rather than as a substantial, clearly definable characteristic of a person. This open-endedness and non-seclusion results from (...)
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  • Engaging with ethics in Internet of Things: Imaginaries in the social milieu of technology developers.Selena Nemorin, Alison Powell & Funda Ustek-Spilda - 2019 - Big Data and Society 6 (2).
    Discussions about ethics of Big Data often focus on the ethics of data processing: collecting, storing, handling, analysing and sharing data. Data-based systems, however, do not come from nowhere. They are designed and brought into being within social spaces – or social milieu. This paper connects philosophical considerations of individual and collective capacity to enact practical reason to the influence of social spaces. Building a deeper engagement with the social imaginaries of technology development through analysis of two years of fieldwork (...)
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  • What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.
    Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...)
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  • Negotiating the reuse of health-data: Research, Big Data, and the European General Data Protection Regulation.Ulrike Felt & Johannes Starkbaum - 2019 - Big Data and Society 6 (2).
    Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors and embeds the analysis in (...)
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  • Grassroots resource mobilization through counter-data action.Carl DiSalvo & Amanda Meng - 2018 - Big Data and Society 5 (2).
    In this paper, we document the counter-data action and data activism of a grassroots affordable housing advocacy group in Atlanta. Our observation and insight into these data activities and strategies are achieved through ethnographic and engaged research and participatory design. We find that counter-data action through community-collected data is rooted in a legacy of Atlanta’s black activism and black scholarship; that this data activism enabled resource mobilization and critical conscious making; and that design and media production are essential post counter-data (...)
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  • Data as performance – Showcasing cities through open data maps.Morgan Currie - 2020 - Big Data and Society 7 (1).
    This article describes how the City of Los Angeles is showcasing data-driven services to the public through dynamic visualisations of open data. I frame an analysis of this aspect of datafication in local government through linguistics and cultural theory; drawing on this set of literature I theorise the use of public data as both a performative tool and a performance of data-driven city services. I then discuss examples of interactive maps on the City of Los Angeles’ open data websites, produced (...)
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  • Data infrastructure literacy.Liliana Bounegru, Carolin Gerlitz & Jonathan Gray - 2018 - Big Data and Society 5 (2).
    A recent report from the UN makes the case for “global data literacy” in order to realise the opportunities afforded by the “data revolution”. Here and in many other contexts, data literacy is characterised in terms of a combination of numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just competencies in reading and working with datasets but also the ability to account for, intervene around and participate in the wider (...)
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  • Datafication and the practice of intelligence production.Holly Blackmore, Lyria Bennett Moses, Carrie Sanders & Janet Chan - 2022 - Big Data and Society 9 (1).
    Datafication of social life affects what society regards as knowledge. Jasanoff’s regimes of sight framework provides three ideal-type models of authorised knowing in environmental data practice. This paper applies Jasanoff's framework for analysing intelligence practice through an exploratory empirical study of crime and intelligence practitioners in a selection of police services in Australia, New Zealand, Canada and the United States. The paper argues that the ‘view from somewhere’ captures the essence of existing police intelligence practices in the four countries but (...)
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