Fairness and Bias Management in Health AI: consensus-Based Recommendations for Best Practices Across the AI Lifecycle

Q: What does this research mean for the field?

Consensus-based best practices implemented across the entire AI lifecycle are necessary to effectively manage bias and advance fairness in health AI amidst uneven regulatory oversight. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Merage Ghane; Matthew Elmore; Irene Dankwa‐Mullan; Shawn Stapleton; Kellie Owens; Sana Khalid; Allie Delonay; Alaa Youssef; Mwisa Chisunka; Chevon Rariy; Ariadna Vargas; Julio Bonis; Nicoleta Economou-Zavlanos; David Talby

doi:10.1080/15265161.2026.2677391

June 10, 2026The American Journal of BioethicsOpen Access

Fairness and Bias Management in Health AI: consensus-Based Recommendations for Best Practices Across the AI Lifecycle

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Abstract

In a landscape marked by uneven oversight, consensus-based guidance plays a crucial role in advancing AI fairness and managing bias. It contributes to a shared understanding across disciplines, ena...

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Ghane et al. (2026) studied this question.

synapsesocial.com/papers/6a5c1a747a544a9c12aa20a5 https://doi.org/10.1080/15265161.2026.2677391

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Fairness and Bias Management in Health AI: consensus-Based Recommendations for Best Practices Across the AI Lifecycle

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Fairness and Bias Management in Health AI: consensus-Based Recommendations for Best Practices Across the AI Lifecycle

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Abstract

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