Building Fair and Trustworthy Biomedical AI: A Tool for Identifying Key Decision Points

Abstract

Recent advancements in artificial intelligence (AI) have transformed biomedicine, offering tools for improved diagnostics, drug discovery, and patient care. Yet these innovations raise pressing ethical concerns, including bias, inequitable outcomes, and privacy risks, which highlight the need for deliberate attention to fairness, trust, and trustworthiness in AI development. In this paper, we argue that ethical responsibility should be embedded at both institutional and individual levels, and that multi-stakeholder engagement, especially with underrepresented groups, is essential to ensure AI tools meet diverse needs. Building on a framework originally developed for precision medicine research, we present an adapted decision-mapping tool-the Trustworthy AI Decision Map-that can anchor and structure dialogue about the ethical implications of specific AI tools. The map identifies key decision points across the AI life cycle that impact fairness and trustworthiness and facilitates dialogue among stakeholders. In making these decisions visible, the map seeks to enable teams to anticipate downstream consequences, integrate multiple perspectives, and support institutional accountability. We illustrate its potential through a case involving the deployment of AI in rural healthcare settings. Moving forward, we suggest that empirical testing with stakeholders is needed to validate and refine the map's utility in biomedical AI contexts to promote fair and trustworthy AI practices.

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