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

ArXi:2605.28098v1 Announce Type: new Multi-agent systems are increasingly deployed to 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.