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  1. Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis.Tahereh Saheb - 2024 - Science and Engineering Ethics 30 (2):1-26.
    As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of (...)
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  • Translational Neuroethics: A Vision for a More Integrated, Inclusive, and Impactful Field.Anna Wexler & Laura Specker Sullivan - 2023 - American Journal of Bioethics Neuroscience 14 (4):388-399.
    As early-career neuroethicists, we come to the field of neuroethics at a unique moment: we are well-situated to consider nearly two decades of neuroethics scholarship and identify challenges that have persisted across time. But we are also looking squarely ahead, embarking on the next generation of exciting and productive neuroethics scholarship. In this article, we both reflect backwards and turn our gaze forward. First, we highlight criticisms of neuroethics, both from scholars within the field and outside it, that have focused (...)
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  • Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap.Jana Schaich Borg - 2021 - Big Data and Society 8 (2).
    Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by (...)
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  • Equity in AgeTech for Ageing Well in Technology-Driven Places: The Role of Social Determinants in Designing AI-based Assistive Technologies.Giovanni Rubeis, Mei Lan Fang & Andrew Sixsmith - 2022 - Science and Engineering Ethics 28 (6):1-15.
    AgeTech involves the use of emerging technologies to support the health, well-being and independent living of older adults. In this paper we focus on how AgeTech based on artificial intelligence (AI) may better support older adults to remain in their own living environment for longer, provide social connectedness, support wellbeing and mental health, and enable social participation. In order to assess and better understand the positive as well as negative outcomes of AI-based AgeTech, a critical analysis of ethical design, digital (...)
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  • On the Contribution of Neuroethics to the Ethics and Regulation of Artificial Intelligence.Michele Farisco, Kathinka Evers & Arleen Salles - 2022 - Neuroethics 15 (1):1-12.
    Contemporary ethical analysis of Artificial Intelligence is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, (...)
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  • SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development.Georgina Curto & Flavio Comim - 2023 - Science and Engineering Ethics 29 (4):1-19.
    This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process (...)
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  • The contested role of AI ethics boards in smart societies: a step towards improvement based on board composition by sortition.Ludovico Giacomo Conti & Peter Seele - 2023 - Ethics and Information Technology 25 (4):1-15.
    The recent proliferation of AI scandals led private and public organisations to implement new ethics guidelines, introduce AI ethics boards, and list ethical principles. Nevertheless, some of these efforts remained a façade not backed by any substantive action. Such behaviour made the public question the legitimacy of the AI industry and prompted scholars to accuse the sector of ethicswashing, machinewashing, and ethics trivialisation—criticisms that spilt over to institutional AI ethics boards. To counter this widespread issue, contributions in the literature have (...)
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  • Ethical governance of artificial intelligence for defence: normative tradeoffs for principle to practice guidance.Alexander Blanchard, Christopher Thomas & Mariarosaria Taddeo - forthcoming - AI and Society:1-14.
    The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative (...)
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