Is it possible to vaccinate AI against bias? An exploratory study in epilepsy

Abstract

<b>Question:</b> Can a simple prompt-based "inoculation" instructing large language models to ignore clinically irrelevant socioeconomic details reduce bias and improve accuracy in epilepsy diagnosis and treatment recommendations?<b>Findings:</b> In this experimental study of 480 responses from 6 large language models to paired high- vs low-socioeconomic status epilepsy vignettes, base diagnostic and treatment accuracies were 36% and 51%, respectively, with bias gaps of 45 and 25 percentage points, respectively; adding an inoculation prompt increased accuracy to 55% and 63% and reduced bias gaps to 27 and 8 percentage points, though effects varied by model, with some showing near-complete bias elimination and others demonstrating paradoxical worsening in certain conditions.<b>Meaning:</b> Prompt-based inoculation may offer a practical, low-cost strategy to partially mitigate socioeconomic bias and modestly improve the quality of large language model clinical recommendations, but model-specific behavior and residual disparities highlight the need for ongoing oversight and complementary bias-mitigation strategies.

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