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    <abstract>Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired prompts across five English dialects, we formalize this failure as the dialect penalty, where filters trigger based on linguistic surface features rather than semantic intent. Text-level filters fail in opposing directions: NSFW-T over-flags benign dialect prompts and LatentGuard over-flags toxic ones (bias gaps up to +28.29 pp), while the OpenAI Moderation API under-detects them. A controlled typo ablation confirms this penalty originates from flagging dialectal features, not generic out-of-distribution sensitivity. The pixel-level generator is largely dialect-agnostic; the penalty enters at text processing and cascades unevenly to post-hoc guardrails. We show this bias tracks training data imbalance and is mitigable via group-balanced retraining, with an ablation attributing the gain to balanced exposure rather than to the worst-group objective of GroupDRO (group distributionally robust optimization). Current pipelines systematically fail dialect speakers, an equity failure masked by mean accuracy benchmarks. Our official code and dataset are publicly available at https://github.com/minguinho26/dialect-penalty-t2i.\par Content Warning: This paper contains offensive, toxic, or disturbing text prompts and generated images.</abstract>
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%0 Conference Proceedings
%T Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines
%A Kim, Minkyu
%A Choi, Juhwan
%A Kim, YoungBin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kim-etal-2026-safe
%X Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired prompts across five English dialects, we formalize this failure as the dialect penalty, where filters trigger based on linguistic surface features rather than semantic intent. Text-level filters fail in opposing directions: NSFW-T over-flags benign dialect prompts and LatentGuard over-flags toxic ones (bias gaps up to +28.29 pp), while the OpenAI Moderation API under-detects them. A controlled typo ablation confirms this penalty originates from flagging dialectal features, not generic out-of-distribution sensitivity. The pixel-level generator is largely dialect-agnostic; the penalty enters at text processing and cascades unevenly to post-hoc guardrails. We show this bias tracks training data imbalance and is mitigable via group-balanced retraining, with an ablation attributing the gain to balanced exposure rather than to the worst-group objective of GroupDRO (group distributionally robust optimization). Current pipelines systematically fail dialect speakers, an equity failure masked by mean accuracy benchmarks. Our official code and dataset are publicly available at https://github.com/minguinho26/dialect-penalty-t2i.\par Content Warning: This paper contains offensive, toxic, or disturbing text prompts and generated images.
%U https://aclanthology.org/2026.findings-emnlp.2189/
%P 46866-46888

Markdown (Informal)

[Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines](https://aclanthology.org/2026.findings-emnlp.2189/) (Kim et al., Findings 2026)

• Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines (Kim et al., Findings 2026)

ACL

• Minkyu Kim, Juhwan Choi, and YoungBin Kim. 2026. Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46866–46888, Budapest, Hungary. Association for Computational Linguistics.