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

Militello, Diiulio, Wilson, Nguyen, Harle, Gellad, Lo-Ciganic. Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support. Journal of the American Medical Informatics Association : JAMIA. 2025 Feb 1; 32(2):398-403, DOI: 10.1093/jamia/ocae291.

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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.