Journal of Medical Ethics 46 (3):205-211 (2020)
AbstractIn recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.
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Epistemology of Disagreement: The Good News.David Christensen - 2007 - Philosophical Review 116 (2):187-217.
Vices of the Mind: From the Intellectual to the Political.Quassim Cassam - 2019 - Oxford University Press.
How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1):205395171562251.
Citations of this work
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.
Who is Afraid of Black Box Algorithms? On the Epistemological and Ethical Basis of Trust in Medical AI.Juan Manuel Durán & Karin Rolanda Jongsma - 2021 - Journal of Medical Ethics 47 (5):medethics-2020-106820.
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.
Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy.Sebastian Laacke, Regina Mueller, Georg Schomerus & Sabine Salloch - 2021 - American Journal of Bioethics 21 (7):4-20.
Limits of Trust in Medical AI.Joshua James Hatherley - 2020 - Journal of Medical Ethics 46 (7):478-481.
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