@inproceedings{chen-etal-2026-measuring,
    title = "Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos",
    author = "Chen, Haodong  and
      Huang, Qiang  and
      Zhao, Jiaqi  and
      Jiang, Qiuping  and
      Chang, Xiaojun  and
      Yu, Jun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1857/",
    pages = "39963--39987",
    ISBN = "979-8-89176-390-6",
    abstract = "Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demographic cues. A central challenge in measuring such social bias is attribution under visual confounding: real-world images entangle race and gender with correlated factors such as background and clothing, obscuring attribution. We propose a **face-only counterfactual evaluation paradigm** that isolates demographic effects while preserving real-image realism. Starting from real photographs, we generate counterfactual variants by editing only facial attributes related to race and gender, keeping all other visual factors fixed. Based on this paradigm, we construct **FOCUS**, a dataset of 480 scene-matched counterfactual images across six occupations and ten demographic groups, and propose **REFLECT,** a benchmark comprising three decision-oriented tasks: two-alternative forced choice, multiple-choice socioeconomic inference, and numeric salary recommendation. Experiments on five state-of-the-art VLMs reveal that demographic disparities persist under strict visual control and vary substantially across task formulations. These findings underscore the necessity of controlled, counterfactual audits and highlight task design as a critical factor in evaluating social bias in multimodal models."
}