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  1. First- and Second-Level Bias in Automated Decision-making.Ulrik Franke - 2022 - Philosophy and Technology 35 (2):1-20.
    Recent advances in artificial intelligence offer many beneficial prospects. However, concerns have been raised about the opacity of decisions made by these systems, some of which have turned out to be biased in various ways. This article makes a contribution to a growing body of literature on how to make systems for automated decision-making more transparent, explainable, and fair by drawing attention to and further elaborating a distinction first made by Nozick between first-level bias in the application of standards and (...)
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  • Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence.Alexander Buhmann & Christian Fieseler - forthcoming - Business Ethics Quarterly:1-34.
    Responsible innovation in artificial intelligence calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of (...)
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  • The ethics of algorithms: key problems and solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2022 - AI and Society 37 (1):215-230.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...)
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  • Against “Democratizing AI”.Johannes Himmelreich - forthcoming - AI and Society:1-14.
    This paper argues against the call to democratize artificial intelligence. Several authors demand to reap purported benefits that rest in direct and broad participation: In the governance of AI, more people should be more involved in more decisions about AI—from development and design to deployment. This paper opposes this call. The paper presents five objections against broadening and deepening public participation in the governance of AI. The paper begins by reviewing the literature and carving out a set of claims that (...)
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  • Weapons of Moral Construction? On the Value of Fairness in Algorithmic Decision-Making.Simona Tiribelli & Benedetta Giovanola - 2022 - Ethics and Information Technology 24 (1).
    Fairness is one of the most prominent values in the Ethics and Artificial Intelligence debate and, specifically, in the discussion on algorithmic decision-making. However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying (...)
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  • Steering Representations—Towards a Critical Understanding of Digital Twins.Paulan Korenhof, Vincent Blok & Sanneke Kloppenburg - 2021 - Philosophy and Technology 34 (4):1751-1773.
    Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering, the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission who wishes (...)
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  • The Ethics of Algorithms: Key Problems and Solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2021 - AI and Society.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...)
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  • Transparency as Design Publicity: Explaining and Justifying Inscrutable Algorithms.Michele Loi, Andrea Ferrario & Eleonora Viganò - 2020 - Ethics and Information Technology 23 (3):253-263.
    In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in explaining (...)
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  • Conservative AI and social inequality: conceptualizing alternatives to bias through social theory.Mike Zajko - forthcoming - AI and Society:1-10.
    In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist’s view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of the ways (...)
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  • Algorithmic Bias and the Value Sensitive Design Approach.Judith Simon, Pak-Hang Wong & Gernot Rieder - 2020 - Internet Policy Review 9 (4).
    Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the key (...)
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  • Machine Thinking, Fast and Slow.Jean-François Bonnefon & Iyad Rahwan - 2020 - Trends in Cognitive Sciences 24 (12):1019-1027.
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