Role and Use of Race in AI/ML Models Related to Health
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Abstract (arXiv)
The role and use of race within health-related artificial intelligence and machine learning (AI/ML) models has sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking. This perspective provides a broad-based, systematic, and cross-cutting landscape analysis of race-related challenges, structured around the AI/ML lifecycle and framed through "points to consider" to support inquiry and decision-making.
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12 of 95 references.
References · 12
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2014Certifying and removing disparate impactcited by 715
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
- 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 253
- 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
- 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
- 2015A Deeper Look at Dataset Biascited by 87
- 2023Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategiescited by 85
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- 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
- 2021An Artificial Intelligence Life Cycle: From Conception to Productioncited by 11
- 2021Managing Bias in Human-Annotated Data: Moving Beyond Bias Removalcited by 6
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