Session Introduction: Fairness and Bias in Biomedical AI/ML: Defining Goals and Putting Them Into Practice
Year: 2025
Type: conference-paper
Abstract: Concerns regarding generalizability and ensuring that artificial intelligence and machine learning (AI/ML) work effectively and accurately across different populations continue to present challenges for the ethical development and deployment of biomedical. Even though bias and fairness have been prioritized as issues for biomedical AI/ML, underlying differences in how researchers conceptualize and operationalize bias and fairness can contribute to difficulties in achieving goals for addressing fairness and mitigating bias. This session of the 2026 Pacific Symposium on Biocomputing offers the opportunity for interdisciplinary discussion and perspectives on addressing fairness in biomedical AI/ML.
Authors: Nicole Martinez-Martin, Abdoul Jalil Djiberou Mahamadou, Magdalena Ng, Mildred K. Cho
Institutions: Stanford Medicine, Stanford University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4417312661
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