# Your AI Bosses Are Still Prejudiced: The Emergence of Stereotypes in LLM-Based Multi-Agent Systems

## TL;DR

This paper demonstrates that LLM-based multi-agent systems can spontaneously develop stereotypes and biases through interactions, highlighting emergent social behaviors similar to humans, even without initial biases.

## Contribution

It introduces a novel experimental framework showing how stereotypes emerge in AI agents, revealing the influence of interaction dynamics and hierarchical structures.

## Key findings

- AI agents develop stereotypes without initial biases
- Stereotypes strengthen with more interactions and hierarchy
- Emergent group effects mirror human social biases

## Abstract

While stereotypes are well-documented in human social interactions, AI systems are often presumed to be less susceptible to such biases. Previous studies have focused on biases inherited from training data, but whether stereotypes can emerge spontaneously in AI agent interactions merits further exploration. Through a novel experimental framework simulating workplace interactions with neutral initial conditions, we investigate the emergence and evolution of stereotypes in LLM-based multi-agent systems. Our findings reveal that (1) LLM-Based AI agents develop stereotype-driven biases in their interactions despite beginning without predefined biases; (2) stereotype effects intensify with increased interaction rounds and decision-making power, particularly after introducing hierarchical structures; (3) these systems exhibit group effects analogous to human social behavior, including halo effects, confirmation bias, and role congruity; and (4) these stereotype patterns manifest consistently across different LLM architectures. Through comprehensive quantitative analysis, these findings suggest that stereotype formation in AI systems may arise as an emergent property of multi-agent interactions, rather than merely from training data biases. Our work underscores the need for future research to explore the underlying mechanisms of this phenomenon and develop strategies to mitigate its ethical impacts.