Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines

2026-08-30

AI Summary

研究解决了文本到图像安全管道中对非标准方言的不公平过滤问题，通过评估不同英语方言的提示并提出通过平衡训练数据来缓解这一问题的方法。

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. Content Warning: This paper contains offensive, toxic, or disturbing text prompts and generated images.

Problem

• dialect penalty
• text-to-image safety
• linguistic surface features
• semantic intent
• equity failure

Innovation

• dialect penalty
• text-to-image safety
• linguistic surface features
• group-balanced retraining