Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems

Multi-agent systems are increasingly deployed to support various tasks where agents interact to achieve individual and collective objectives. Although these systems can enhance task performance and decision-making, fairness preservation through bias reduction remains challenging. This study examines how agent-level biases shift and impact system-wide fairness. We use prompts to expose individual agents to group-favoring bias, then assess downstream impacts at the system level. To quantify the im

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ethics.ai (27 May 2026), “Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems,” evidence record 3612, https://ethics.ai/record/3612 (originally published by arXiv).

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