Instability Floors: Separating Bias from Noise in Fairness Audits of Clinical LLM Agents with FairMedAgent

Research identifies a significant issue of 'instability floors' in fairness audits of clinical language-model agents, where a substantial portion of reported flip rates (changes in agent actions) are attributable to stochastic noise rather than demographic bias. This intrinsic variability, observed even when no patient descriptors change, complicates the accurate assessment of fairness and operational reliability in these critical systems. The study measured this noise, finding it can account for a considerable percentage of action changes, varying by clinical task and LLM vendor.

The inherent instability observed in clinical language-model agents complicates the reliable assessment of fairness and operational consistency. Understanding and quantifying this stochastic noise is critical for developing robust audit methodologies and ensuring that clinical AI systems perform predictably and equitably across diverse patient populations. This directly impacts trust, regulatory compliance, and the safe deployment of AI in sensitive domains.

Fairness audits using counterfactual methods report a 'flip rate' based on changes in agent actions when patient demographic descriptors are altered.

Forward consideration, not a verified fact.