mlsanityData to 7 Oct 2026

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

arXiv 2609.03221 ↗First posted 2 Sep 2026cs.CL cs.AI cs.CYcited by 0

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Abstract (arXiv)

Counterfactual fairness audits of clinical language-model agents report a flip rate: how often an action changes when only the patient's demographic descriptor changes. Part of that rate is not demographic. A stochastic agent also changes its own action when nothing changes, and a flip rate cannot be interpreted without knowing how often. We measured it. Re-running one condition ten times over sixteen synthetic vignettes at default sampling changed a clinical agent's action in 8.7 percent of replicate pairs, from 2.2 percent for intensive-care escalation to 17.9 percent for controlled-substance caution, an output given no operational criteria. Across six models from five vendors, pooled floors ranged from 2.5 to 23.7 percent; in this panel neither disclosed size, vendor, nor hosting ordered them. The floor depends on the decoding configuration: majority voting over five draws removed 39 percent of it (95 percent confidence interval (CI) 18 to 64); at temperature 0 three of four locally served models showed no disagreement, but a hosted model still did. We also show that, for a binary action, the flip rate expected under no demographic effect equals the floor and a real effect adds only its square, so a flip rate inside the floor is not evidence of fairness, and direction must be tested with a signed paired test. We give a four-step reporting procedure and release FairMedAgent, the harness, with its protocol, vignettes and analysis scripts, so any team can measure the floor for its own agent.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 63 references that are in the map. First posted on arXiv 2 Sep 2026.

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20 of 63 references.

References · 20

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2018Datasheets for Datasetscited by 1,371
  4. 2017Counterfactual Fairnesscited by 802
  5. 2019Measurement and Fairnesscited by 299
  6. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  7. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environmentscited by 215
  8. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 150
  9. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  10. 2024Non-Determinism of "Deterministic" LLM Settingscited by 84
  11. 2025MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agentscited by 56
  12. 2025Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inferencecited by 47
8 more
  1. 2024A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Modelscited by 45
  2. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
  3. 2024Bias patterns in the application of LLMs for clinical decision support: A comprehensive studycited by 27
  4. 2022Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendationscited by 20
  5. 2024CLIMB: A Benchmark of Clinical Bias in Large Language Modelscited by 7
  6. 2024How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?cited by 4
  7. 2026EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triagecited by 3
  8. 2026AgentFairBench: Do LLM Agents Discriminate When They Act?cited by 2

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  1. 2026Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits
  2. 2026Counterfactual Evaluation Reveals Hidden Capability Profiles in Clinical LLMs and Agents
  3. 2026Same Predictions, Different Harms: Causal Auditing of Patient World Models
  4. 2026Counterfactual Fairness Evaluation of LLM-Based Contact Center Agent Quality Assurance System
  5. 2025Auditing medical multi-agent AI reveals risks of false consensus
  6. 2026Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models
  7. 2026Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints
  8. 2026Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning