@inproceedings{basu-etal-2026-baid,
title = "{BAID}: A Benchmark for Bias Assessment of {AI} Detectors",
author = "Basu, Priyam and
Zhang, Yunfeng and
Raheja, Vipul",
editor = "Mysore, Sheshera and
Kumar, Sachin and
Balachandran, Vidhisha and
Hayati, Shirley Anugrah and
Brahman, Faeze and
Moussa, Hanane Nour and
Salemi, Alireza",
booktitle = "Proceedings of the Second Workshop on Customizable {NLP}: Progress and Challenges in Customizing {NLP} for a Domain, Application, Group, or Individual ({C}ustom{NLP}4{U})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.customnlp4u-1.1/",
pages = "1--10",
ISBN = "979-8-89176-396-8",
abstract = "AI-generated text detectors gain adoption in educational and professional contexts, their fairness remains underexamined. While prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs), there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose a comprehensive evaluation framework for AI detectors across various types of biases. As part of this framework, we introduce a suite of targeted datasets spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic. Using this, we evaluate four open-source state-of-theart 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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%0 Conference Proceedings
%T BAID: A Benchmark for Bias Assessment of AI Detectors
%A Basu, Priyam
%A Zhang, Yunfeng
%A Raheja, Vipul
%Y Mysore, Sheshera
%Y Kumar, Sachin
%Y Balachandran, Vidhisha
%Y Hayati, Shirley Anugrah
%Y Brahman, Faeze
%Y Moussa, Hanane Nour
%Y Salemi, Alireza
%S Proceedings of the Second Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-396-8
%F basu-etal-2026-baid
%X AI-generated text detectors gain adoption in educational and professional contexts, their fairness remains underexamined. While prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs), there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose a comprehensive evaluation framework for AI detectors across various types of biases. As part of this framework, we introduce a suite of targeted datasets spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic. Using this, we evaluate four open-source state-of-theart 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.
%U https://aclanthology.org/2026.customnlp4u-1.1/
%P 1-10
Markdown (Informal)
[BAID: A Benchmark for Bias Assessment of AI Detectors](https://aclanthology.org/2026.customnlp4u-1.1/) (Basu et al., CustomNLP4U 2026)
ACL
- Priyam Basu, Yunfeng Zhang, and Vipul Raheja. 2026. BAID: A Benchmark for Bias Assessment of AI Detectors. In Proceedings of the Second Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), pages 1–10, San Diego, California, USA. Association for Computational Linguistics.