• Melissa D McCradden, Shalmali Joshi, James A Anderson, Mjaye Mazwi, Anna Goldenberg, and Zlotnik Shaul Randi R Bioethics Department, The Hospital for Sick Children, Toronto, Ontario, Canada. De.
• Bioethics Department, The Hospital for Sick Children, Toronto, Ontario, Canada.
• J Am Med Inform Assoc. 2020 Dec 9; 27 (12): 2024-2027.

AbstractAccumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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