Gender and Political Bias in Large Language Models: A Demonstration Platform

• URL: http://arxiv.org/abs/2509.16264v2
• Date: Tue, 23 Sep 2025 03:43:30 GMT
• Title: Gender and Political Bias in Large Language Models: A Demonstration Platform
• Authors: Wenjie Lin, Hange Liu, Xutao Mao, Yingying Zhuang, Jingwei Shi, Xudong Han, Tianyu Shi, Jinrui Yang,
• Abstract summary: ParlAI Vote is an interactive system for exploring European Parliament debates and votes.<n>It includes rich demographic data such as gender, age, country, and political group.<n>Users can browse debates, inspect linked speeches, compare real voting outcomes with predictions from frontier LLMs.
• Score: 12.223144746389371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ParlAI Vote, an interactive system for exploring European Parliament debates and votes, and for testing LLMs on vote prediction and bias analysis. This platform connects debate topics, speeches, and roll-call outcomes, and includes rich demographic data such as gender, age, country, and political group. Users can browse debates, inspect linked speeches, compare real voting outcomes with predictions from frontier LLMs, and view error breakdowns by demographic group. Visualizing the EuroParlVote benchmark and its core tasks of gender classification and vote prediction, ParlAI Vote highlights systematic performance bias in state-of-the-art LLMs. The system unifies data, models, and visual analytics in a single interface, lowering the barrier for reproducing findings, auditing behavior, and running counterfactual scenarios. It supports research, education, and public engagement with legislative decision-making, while making clear both the strengths and the limitations of current LLMs in political analysis.

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