Forewarning Artificial Intelligence about Cognitive Biases

Jonathan Wang and Donald A. Redelmeier
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Jonathan Wang: Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
Donald A. Redelmeier: Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada

Medical Decision Making, 2025, vol. 45, issue 7, 913-916

Abstract: Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, “Please keep in mind cognitive biases and other pitfalls of reasoning,†might mitigate biases in OpenAI’s generative pretrained transformer large language model. We used 10 clinically nuanced cases to test specific biases with and without a forewarning. Responses from the forewarning group were 50% longer and discussed cognitive biases more than 100 times more frequently compared with responses from the control group. Despite these differences, the forewarning decreased overall bias by only 6.9%, and no bias was extinguished completely. These findings highlight the need for clinician vigilance when interpreting generated responses that might appear seemingly thoughtful and deliberate. Highlights Artificial intelligence models can be warned to avoid racial and gender bias. Forewarning artificial intelligence models to avoid cognitive biases does not adequately mitigate multiple pitfalls of reasoning. Critical reasoning remains an important clinical skill for practicing physicians.

Keywords: algorithm bias; artificial intelligence; bias in medicine; clinical decision making; cognitive psychology; large language model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:7:p:913-916

DOI: 10.1177/0272989X251346788

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