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arXiv · 2512.11505

BAID: A Benchmark for Bias Assessment of AI Detectors

Priyam Basu·Yunfeng Zhang·Vipul Raheja
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Abstract

AI-generated text detectors have recently gained adoption in educational and professional contexts. Prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs) however, there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose BAID, a comprehensive evaluation framework for AI detectors across various types of biases. As a part of the framework, we introduce over 200k samples spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic. We also generated synthetic versions of each sample with carefully crafted prompts to preserve the original content while reflecting subgroup-specific writing styles. Using this, we evaluate four open-source state-of-the-art AI text detectors and find consistent disparities in detection performance, particularly low recall rates for texts from underrepresented groups. Our contributions provide a scalable, transparent approach for auditing AI detectors and emphasize the need for bias-aware evaluation before these tools are deployed for public use.

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BibTeXRIS

Priyam Basu, Yunfeng Zhang, Vipul Raheja. 2025-12-12. BAID: A Benchmark for Bias Assessment of AI Detectors. https://arxiv.org/abs/2512.11505

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