Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare

作者：Michael Paul Cary, Sophia Bessias, Jonathan McCall, Michael J. Pencina, Siobahn Day Grady, Kay S. Lytle, Nicoleta Economou-Zavlanos · 发表于：Journal of Nursing Scholarship · 年份：2024 · DOI：10.1111/jnu.13007 · 被引用次数：33 · 研究领域：Artificial Intelligence in Healthcare and Education、Ethics and Social Impacts of AI、Electronic Health Records Systems

BACKGROUND: The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively. PURPOSE: We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative. METHODS: Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healt...