Forewarning Artificial Intelligence about Cognitive Biases
Bibliographic record
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.HighlightsArtificial 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".