Promoting Fairness in AI Implementation

Efforts to mitigate bias in artificial intelligence (AI) in healthcare have focused heavily on technical solutions such as diversifying training datasets, improving interpretability, and auditing models for fairness. While essential, these approaches overlook a critical dimension: the ways AI systems are implemented and used in practice. Even the most technically fair algorithms can produce inequitable outcomes if clinicians adopt, interpret, or apply them in patterned ways shaped by institutional norms, professional hierarchies, and patient characteristics. Drawing on social science research and our own experiences implementing AI models in healthcare settings, we argue that implementation is an important driver of bias in healthcare AI. We suggest that clinicians' discretionary use of AI could reproduce or even amplify inequities and that "human-in-the-loop" oversight may offer only limited safeguards against these risks. To ensure equitable outcomes, governance strategies must move beyond individual vigilance and adopt a sociotechnical perspective that accounts for the broader systems in which AI tools are embedded. We call for system-level monitoring, institutional accountability, and new frameworks for studying fairness in AI implementation.