Work

Representation Bias in Political Sample Simulations with Large Language Models

Year: 2025

Type: conference-paper

Abstract: This study explores representation biases in simulating political samples using Large Language Models (LLMs), focusing on vote choice and public opinion. We evaluate seven LLMs from diverse cultural backgrounds with data from the American National Election Studies, German Longitudinal Election Study, and Zuobiao Dataset. The study identifies three dimensions of bias: societal and cultural contexts, demographic groups, and political institutions. Results show higher simulation accuracy for vote choice than public opinion, particularly in English-speaking and democratic countries with bipartisan systems. The cultural backgrounds of development teams significantly influence simulation performance. The findings offer insights into addressing biases in AI-driven computational social science.

DOI: 10.1145/3701716.3715591

Authors: Weihong Qi, Hanjia Lyu, Jiebo Luo

Institutions: Indiana University Bloomington, Indiana University, University of Rochester

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: gold

OpenAlex ID: W4410636814

FWCI: 10.46

Cites: 12

Cited by: 10

Related to: 10