BAID: A Benchmark for Bias Assessment of AI Detectors

• URL: http://arxiv.org/abs/2512.11505v1
• Date: Fri, 12 Dec 2025 12:01:42 GMT
• Title: BAID: A Benchmark for Bias Assessment of AI Detectors
• Authors: Priyam Basu, Yunfeng Zhang, Vipul Raheja,
• Abstract summary: We propose BAID, a comprehensive evaluation framework for AI detectors across various types of biases.<n>We introduce over 200k samples spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic.<n>We find consistent disparities in detection performance, particularly low recall rates for texts from underrepresented groups.
• Score: 9.156813547624923
• License: http://creativecommons.org/licenses/by/4.0/
• 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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