Reproducing Real-World Demographic Biases in AI Agent Simulations
As researchers increasingly utilize large language models (LLMs) to simulate human behaviors and attitudes based on real-world demographic characteristics, important questions arise about how accurately these AI-generated agents replicate true demographic biases and preferences. While recent studies demonstrate promising alignment between simulated outputs and actual demographic trends—referred to as “algorithmic fidelity”—they also expose notable methodological challenges and limitations, including potential oversimplifications, exaggerated stereotypes, and inconsistent representations of marginalized groups. Understanding these nuances is essential for responsibly leveraging AI simulations as reliable proxies for real human populations in social science research.
