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  1. Algorithms Advise, Humans Decide: the Evidential Role of the Patient Preference Predictor.Nicholas Makins - forthcoming - Journal of Medical Ethics.
    An AI-based “patient preference predictor” (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP–that between algorithmic prediction and decision-making–and argue that much of the recent philosophical disagreement stems from this (...)
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  • Predicting and Preferring.Nathaniel Sharadin - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.
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  • AI knows best? Avoiding the traps of paternalism and other pitfalls of AI-based patient preference prediction.Andrea Ferrario, Sophie Gloeckler & Nikola Biller-Andorno - 2023 - Journal of Medical Ethics 49 (3):185-186.
    In our recent article ‘The Ethics of the Algorithmic Prediction of Goal of Care Preferences: From Theory to Practice’1, we aimed to ignite a critical discussion on why and how to design artificial intelligence (AI) systems assisting clinicians and next-of-kin by predicting goal of care preferences for incapacitated patients. Here, we would like to thank the commentators for their valuable responses to our work. We identified three core themes in their commentaries: (1) the risks of AI paternalism, (2) worries about (...)
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