This research introduces a novel framework for identifying biases that emerge when multiple AI models interact in conversation, addressing a critical gap in AI safety evaluation.

• Reveals how conversational context can amplify biases beyond what appears in individual model responses
• Demonstrates that multi-agent systems present unique bias risks not captured by traditional single-model evaluations
• Provides a methodological approach to quantify and detect biases that emerge through AI-to-AI interactions

This work is crucial for security as it helps identify potentially harmful biases that could remain hidden in deployed AI systems, particularly in sensitive decision-making contexts where multiple AI agents collaborate.

Unmasking Conversational Bias in AI Multiagent Systems