Exploring the Boundaries of Content Moderation in Text-to-Image Generation
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Abstract (arXiv)
This paper analyzes the community safety guidelines of five text-to-image (T2I) generation platforms and audits five T2I models, focusing on prompts related to the representation of humans in areas that might lead to societal stigma. While current research primarily focuses on ensuring safety by restricting the generation of harmful content, our study offers a complementary perspective. We argue that the concept of safety is difficult to define and operationalize, reflected in a discrepancy between the officially published safety guidelines and the actual behavior of the T2I models, and leading at times to over-censorship. Our findings call for more transparency and an inclusive dialogue about the platforms' content moderation practices, bearing in mind their global cultural and social impact.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Sep 2024.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gesturescited by 4
- 2026HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasingcited by 1
- 2026FIGURA: A Modular Prompt Engineering Method for Artistic Figure Photography in Safety-Filtered Text-to-Image Modelscited by 0
- 2026HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing23 May 2026
- 2026FIGURA: A Modular Prompt Engineering Method for Artistic Figure Photography in Safety-Filtered Text-to-Image Models20 Feb 2026
- 2025Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gestures24 Feb 2025