Disparity Laundering: Consensus Without Fairness in Clinical Multi-Agent Systems

The paper investigates how clinical large language models, when deployed as multi‑agent teams, fail to eliminate demographic bias through deliberation. Across five model families and four aggregation topologies on 120 MIMIC‑IV emergency cases, every configuration amplified or relocated bias, giving Black patients more acute triage. The authors term this phenomenon "disparity laundering" and show that only by making counterfactual demographic versions visible and pooling diverse perspectives at every deliberation round can the bias be substantially reduced without retraining or architectural changes.

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• llms
• agents
• safety