Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study

• URL: http://arxiv.org/abs/2510.04641v1
• Date: Mon, 06 Oct 2025 09:45:32 GMT
• Title: Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study
• Authors: Ayan Majumdar, Feihao Chen, Jinghui Li, Xiaozhen Wang,
• Abstract summary: Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases.<n>We present a comprehensive evaluation framework aimed at English texts to assess the ability of LLMs in detecting demographic-targeted social biases.<n>We then conduct a systematic evaluation with models across scales and techniques, including prompting, in-context learning, and fine-tuning.
• Score: 1.6682715542079583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases, creating a regulatory need for data auditing and developing scalable bias-detection methods. Although prior work has investigated biases in text datasets and related detection methods, these studies remain narrow in scope. They typically focus on a single content type (e.g., hate speech), cover limited demographic axes, overlook biases affecting multiple demographics simultaneously, and analyze limited techniques. Consequently, practitioners lack a holistic understanding of the strengths and limitations of recent large language models (LLMs) for automated bias detection. In this study, we present a comprehensive evaluation framework aimed at English texts to assess the ability of LLMs in detecting demographic-targeted social biases. To align with regulatory requirements, we frame bias detection as a multi-label task using a demographic-focused taxonomy. We then conduct a systematic evaluation with models across scales and techniques, including prompting, in-context learning, and fine-tuning. Using twelve datasets spanning diverse content types and demographics, our study demonstrates the promise of fine-tuned smaller models for scalable detection. However, our analyses also expose persistent gaps across demographic axes and multi-demographic targeted biases, underscoring the need for more effective and scalable auditing frameworks.

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