Role and Use of Race in AI/ML Models Related to Health
This addresses the problem of ethical and practical issues in using race in health AI/ML for researchers, practitioners, and policymakers, but it is incremental as it offers a framework rather than a novel solution.
The paper tackles the lack of a comprehensive framework for addressing race-related challenges in health AI/ML models by providing a systematic landscape analysis structured around the AI/ML lifecycle, with 'points to consider' to guide stakeholders.
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.