@inproceedings{zhang-etal-2026-beyond-single,
title = "Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in {LLM}s",
author = "Zhang, Zheng and
Wu, Yifan and
Zhou, Yuxiang and
Liu, Qi and
Mao, Qingyang and
Li, Ning and
Gao, Weibo and
Shi, QiYuan and
Huang, Zhenya and
Yu, Baosheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1875/",
pages = "42742--42762",
ISBN = "979-8-89176-459-0",
abstract = "Large language models (LLMs) are increasingly evaluated for fairness, yet most existing benchmarks audit one sensitive attribute at a time. This single-attribute paradigm can miss intersectional harms, where multiple attributes interact non-additively and produce failures that are invisible under marginal evaluation. We introduce ComFair, a configurable benchmark that converts existing fairness data into 479,066 controlled Dual- and Multi-attribute instances and pairs every augmented instance with its task-equivalent source. Across Recognition, Selection, Continuation, and Conversation, this pairing supports a unified audit of performance or bias-score shifts, how often added attributes introduce new errors or mask existing ones, and model-ranking stability. Experiments on ten LLMs show task- and subgroup-dependent sensitivity: discriminative tasks expose both transition directions even when aggregate changes are modest, generative tasks exhibit larger and directionally asymmetric shifts, and rankings vary with attribute composition. ComFair therefore provides a scalable controlled sensitivity audit{---}rather than a model of the natural joint distribution of identities{---}for testing the robustness of fairness-benchmark conclusions beyond single-attribute settings."
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%0 Conference Proceedings
%T Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs
%A Zhang, Zheng
%A Wu, Yifan
%A Zhou, Yuxiang
%A Liu, Qi
%A Mao, Qingyang
%A Li, Ning
%A Gao, Weibo
%A Shi, QiYuan
%A Huang, Zhenya
%A Yu, Baosheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-beyond-single
%X Large language models (LLMs) are increasingly evaluated for fairness, yet most existing benchmarks audit one sensitive attribute at a time. This single-attribute paradigm can miss intersectional harms, where multiple attributes interact non-additively and produce failures that are invisible under marginal evaluation. We introduce ComFair, a configurable benchmark that converts existing fairness data into 479,066 controlled Dual- and Multi-attribute instances and pairs every augmented instance with its task-equivalent source. Across Recognition, Selection, Continuation, and Conversation, this pairing supports a unified audit of performance or bias-score shifts, how often added attributes introduce new errors or mask existing ones, and model-ranking stability. Experiments on ten LLMs show task- and subgroup-dependent sensitivity: discriminative tasks expose both transition directions even when aggregate changes are modest, generative tasks exhibit larger and directionally asymmetric shifts, and rankings vary with attribute composition. ComFair therefore provides a scalable controlled sensitivity audit—rather than a model of the natural joint distribution of identities—for testing the robustness of fairness-benchmark conclusions beyond single-attribute settings.
%U https://aclanthology.org/2026.emnlp-main.1875/
%P 42742-42762
Markdown (Informal)
[Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs](https://aclanthology.org/2026.emnlp-main.1875/) (Zhang et al., EMNLP 2026)
ACL
- Zheng Zhang, Yifan Wu, Yuxiang Zhou, Qi Liu, Qingyang Mao, Ning Li, Weibo Gao, QiYuan Shi, Zhenya Huang, and Baosheng Yu. 2026. Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 42742–42762, Budapest, Hungary. Association for Computational Linguistics.