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October 12, 2025Open Access

Benchmarking Gender and Political Bias in Large Language Models

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Benchmark assesses gender classification and vote prediction in large language models, highlighting bias.

Large language models frequently misclassify female Members of the European Parliament as male, indicating a systematic bias.
Evaluation shows that LLMs tend to favor centrist political groups while exhibiting reduced accuracy on far-left and far-right categories.
Proprietary models like GPT-4o outperform open-weight alternatives in robustness, fairness, and accuracy on politically sensitive tasks.
The EuroParlVote dataset provides essential data for future research on fairness and accountability in natural language processing.

Yang et al. (2025) studied this question.

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October 12, 2025Open Access

View Full Paper

Ask AI

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Authors

Benchmark assesses gender classification and vote prediction in large language models, highlighting bias.

Large language models frequently misclassify female Members of the European Parliament as male, indicating a systematic bias.
Evaluation shows that LLMs tend to favor centrist political groups while exhibiting reduced accuracy on far-left and far-right categories.
Proprietary models like GPT-4o outperform open-weight alternatives in robustness, fairness, and accuracy on politically sensitive tasks.
The EuroParlVote dataset provides essential data for future research on fairness and accountability in natural language processing.

Yang et al. (2025) studied this question.

View Full Paper

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Bookmark