Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems

2026-09-10

AI Summary

研究通过创建WORLDVIEW基准并审计文本到图像系统的提示修订层，揭示了文化偏见的来源，发现非西方和非英语背景被过度标记和简化为刻板印象。

Abstract

Commercial text-to-image systems silently revise user prompts before generating images, a step users typically cannot disable or even see. Yet, existing audits of cultural bias examine only the final images and treat generation as a single pipeline, so they cannot tell where the bias originates. We introduce WORLDVIEW, a multilingual benchmark of 8,960 prompts across 15 languages and 31 language-context pairings. Using it, we audit the revision layer in three systems (DALL-E-3, Imagen-4, GPT-Image-1.5) through a three-step analysis of how heavily it marks each cultural context, whether it flattens that context into a narrow vocabulary, and whether that vocabulary is stereotypical. Relative to a no-context English baseline, the US is the least-marked context, while non-Western and non-Anglophone contexts are marked far more heavily, flattened into narrow vocabularies applied across topically diverse prompts, and reduced to recognizable cultural stereotypes. Comparing images from original versus revised prompts on models without a revision layer, we identify the layer itself as a previously undocumented, causal source of this stereotyping. To locate cultural bias, and fix it, we must audit the system as deployed, not the model alone.

Problem

• text-to-image systems
• cultural bias
• prompt revision

Innovation

• Prompt Revision
• Cultural Bias
• Text-to-Image Systems
• WORLDVIEW Benchmark