Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.

Militello, Laura G, Julie Diiulio, Debbie L Wilson, Khoa A Nguyen, Christopher A Harle, Walid Gellad, and Wei-Hsuan Lo-Ciganic. 2025. “Using Human Factors Methods to Mitigate Bias in Artificial Intelligence-Based Clinical Decision Support.”. Journal of the American Medical Informatics Association : JAMIA 32 (2): 398-403.

Abstract

OBJECTIVES: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

MATERIALS AND METHODS: This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.

RESULTS: The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.

DISCUSSION: Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.

CONCLUSION: This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.

Last updated on 05/20/2025
PubMed