Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives
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DOI：
10.1136/jme-2022-108850
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发表时间：
2023-02-23
影响因子：
4.1
通讯作者：
Rogers, Wendy A.
中科院分区：
文献类型：
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作者：
Aquino, Yves Saint James;Carter, Stacy M.;Rogers, Wendy A.
背景人们越来越担心人工智能 (AI) 在医疗保健领域的应用可能会使本来就代表性不足和边缘化的群体（例如，基于性别或种族）处于不利地位。目标我们的目标是探讨利益相关者在试图减轻算法偏差时认可的策略范围，并考虑算法偏差责任的道德问题。方法 该研究涉及对医护人员、筛查项目经理、消费者健康代表、监管机构、数据科学家和开发人员进行深入的半结构化访谈。结果调查结果揭示了在三个关键问题上存在相当大的分歧。首先，关于偏见是否是医疗保健人工智能中的一个问题的观点各不相同，大多数参与者都同意偏见是一个问题（我们称之为偏见批评观点），少数人持相反观点（否认偏见观点），还有一些人认为人工智能的好处超过了偏见问题带来的任何伤害或错误（偏见辩护者观点）。其次，对于减少偏见的策略以及谁负责这些策略存在分歧。最后，对于在人工智能的开发中是否包含或排除社会文化标识符（例如种族、民族或性别多元化身份）作为减轻偏见的一种方式，存在不同的看法。结论/意义根据参与者的观点，我们提出了利益相关者可能寻求的回应，包括加强跨学科合作、量身定制的利益相关者参与活动、了解算法偏差的实证研究以及修改人工智能开发中主导方法的策略，例如使用参与式方法，以及增加研究团队的多样性和包容性以及研究参与者的招募和选择。
BackgroundThere is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). ObjectivesOur objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. MethodologyThe study involves in-depth, semistructured interviews with healthcare workers, screening programme managers, consumer health representatives, regulators, data scientists and developers. ResultsFindings reveal considerable divergent views on three key issues. First, views on whether bias is a problem in healthcare AI varied, with most participants agreeing bias is a problem (which we call the bias-critical view), a small number believing the opposite (the bias-denial view), and some arguing that the benefits of AI outweigh any harms or wrongs arising from the bias problem (the bias-apologist view). Second, there was a disagreement on the strategies to mitigate bias, and who is responsible for such strategies. Finally, there were divergent views on whether to include or exclude sociocultural identifiers (eg, race, ethnicity or gender-diverse identities) in the development of AI as a way to mitigate bias. Conclusion/significanceBased on the views of participants, we set out responses that stakeholders might pursue, including greater interdisciplinary collaboration, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias and strategies to modify dominant approaches in AI development such as the use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.