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From Single to Societal: Analyzing Persona-Induced Bias in Multi-Agent Interactions

AAAI 2026 · Multi-agent systems and games › LLM agents · approach: LLM multi-agent decision making · 1 citations (OpenAlex)

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Systematically studies how assigned personas (gender and race) induce biases in trustworthiness, insistence, and conformity in LLM multi-agent collaborative problem-solving (GPQA) and persuasion interactions across GPT-4o, Gemini-1.5-Pro, and DeepSeek-V3.

Problem

Personas assigned to LLM agents to diversify behavior may introduce social biases into multi-agent interactions, which remains underexplored.

Approach

Measures persona-level trustworthiness (conformity of default agent to persona agent) and insistence (resistance of persona agent to default agent) in controlled dyadic and multi-agent interactions, finding advantaged groups (men, White) are less trusted and less insistent, and agents show in-group conformity favoritism.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: GPQA PMIYC Perspectrum

Benchmarks: GPQA PMIYC claims

Metrics: trustworthiness insistence conformity rate win rate persuasion effectiveness accuracy max-min difference average absolute difference

Baselines: no-persona baseline

Builds on: Social Identity Theory GPQA

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