On the ethics of algorithmic decision-making in healthcare
On the ethics of algorithmic decision-making in healthcare
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DOI：
10.1136/medethics-2019-105586
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发表时间：
2020-03-01
影响因子：
4.1
通讯作者：
Berens, Philipp
中科院分区：
文献类型：
--
作者：
Grote, Thomas;Berens, Philipp
近年来，大量备受瞩目的科学出版物一直在报道机器学习算法在医疗诊断或治疗建议方面优于临床医生。这引发了人们对部署相关算法的兴趣，目的是增强医疗保健决策。在本文中，我们认为，部署机器学习算法需要在认识和规范层面进行权衡，而不是直接提高临床医生和医疗机构的决策能力。虽然涉及机器学习可能会提高医疗诊断的准确性，但在试图评估给定诊断的可靠性时，它是以不透明为代价的。借鉴文献中的社会认识论和道德责任，我们认为，问题的不确定性潜在地破坏了临床医生的认识权威。此外，我们阐明了在医疗保健中涉及机器学习的潜在陷阱，包括家长式作风，道德责任和公平性。最后，我们讨论了机器学习算法的部署如何改变医疗诊断的证据规范。在这方面，我们希望为进一步从道德角度反思机器学习在加强医疗决策方面的机会和缺陷奠定基础。
In 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.