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  1. Using artificial intelligence to enhance patient autonomy in healthcare decision-making.Jose Luis Guerrero Quiñones - forthcoming - AI and Society:1-10.
    The use of artificial intelligence in healthcare contexts is highly controversial for the (bio)ethical conundrums it creates. One of the main problems arising from its implementation is the lack of transparency of machine learning algorithms, which is thought to impede the patient’s autonomous choice regarding their medical decisions. If the patient is unable to clearly understand why and how an AI algorithm reached certain medical decision, their autonomy is being hovered. However, there are alternatives to prevent the negative impact of (...)
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  • Should AI allocate livers for transplant? Public attitudes and ethical considerations.Max Drezga-Kleiminger, Joanna Demaree-Cotton, Julian Koplin, Julian Savulescu & Dominic Wilkinson - 2023 - BMC Medical Ethics 24 (1):1-11.
    Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses (...)
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  • A Genealogical Approach to Algorithmic Bias.Marta Ziosi, David Watson & Luciano Floridi - 2024 - Minds and Machines 34 (2):1-17.
    The Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness metrics), rather than addressing the underlying social and technical conditions that (re)produce it. In this article, we propose a complementary strategy that uses genealogy as a constructive, epistemic critique to explain algorithmic bias in terms of the conditions that enable it. We focus on XAI feature attributions (Shapley values) and counterfactual approaches as potential tools to gauge these conditions (...)
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