Imagine a group of AI agents collaborating on a project. Sounds futuristic, right? But what if these digital collaborators exhibit the same biases we see in humans? New research reveals how large language models (LLMs) in multi-agent settings can perpetuate implicit gender stereotypes, particularly when assigning roles and responsibilities. Researchers explored different scenarios, from office settings to political situations, and found that LLMs often assigned traditionally “male” tasks (like technical troubleshooting) to male personas and “female” tasks (like organization) to female personas, even when no skills or qualifications were explicitly stated. This raises an important question: how can we ensure fairness in the age of collaborative AI? The study suggests that biases escalate after these AI agents interact, mirroring human behaviors like groupthink. While larger, more complex LLMs like GPT-4 are adept at identifying implicit bias in theory, they struggle to avoid it in practice. The study proposes two key strategies to mitigate bias: fine-tuning the models on unbiased data and implementing 'self-reflection' prompts, encouraging the AI to examine its own decisions. Initial results show these methods, especially when combined, hold promise for creating more equitable AI interactions. However, more research is needed to refine these techniques and prevent the perpetuation of stereotypes in multi-agent LLM systems. As AI becomes more integrated into our lives, ensuring fair and unbiased interactions is crucial for a more equitable future.