Year: 2026
Type: conference-paper
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.
Authors: Minkyu Kim, Juhwan Choi, YoungBin Kim
Institution: Chung-Ang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7204959846