"I Thought You Were The Uncensored Place": Norms, Rules, and Moderation in AI-Generated Sexual Content Communities
cs.CR
Submitted: 2026-08-13
Updated: 2026-08-31
Comments: To appear at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2026
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: As AI-generated sexual content (AIG-SC) is increasingly produced, online communities have emerged to support creators' needs.
Terminology
Abstract
As AI-generated sexual content (AIG-SC) is increasingly produced, online communities have emerged to support creators' needs. To understand whether and how community governance attempts work to prevent abuse while supporting free expression, we interviewed 24 members and moderators of large AIG-SC online communities (10,000+ members) with stated rules against creating and sharing abusive content (e.g., AI-generated CSAM). Through in-depth interviews, we offer insight into: (1) how and why these communities form; (2) implicit community norms; (3) explicitly stated rules---and their operationalization via content moderation; and (4) tensions between community values and moderation that leave space for abusive behavior. Our findings reveal a complex picture: while many creators and communities have personal boundaries against abuse, advice and resources for creating any form of AI-generated sexual content are accessible to users regardless of their intentions. Further complicating community moderation are norms that center anti-censorship and non-judgment, which leave moderators to justify their actions using the limits of the law and terms of service. We end by reflecting on the ways in which technical, community, and legal governance may most effectively mitigate the production of abusive content.
Sources
- Examining Risks in the AI Companion Application Ecosystem
- Warning Message Content Increases Help Seeking in a Large-Scale Dark Web CSAM Intervention
- Evaluating Concept Filtering Defenses against Child Sexual Abuse Material Generation by Text-to-Image Models
- Deepfake Pornography is Resilient to Regulatory and Platform Shocks
- From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
- Dual-Use AI Face Swap Apps Are Mostly Unsafe: A Systematic Safety Audit
- Characterizing Resource Sharing Practices on Underground Internet Forum Synthetic Non-Consensual Intimate Image Content Creation Communities
- A Large-Scale Study of Telegram Bots
- Deterring Searches for Child Sexual Abuse Material on Google Search and Promoting Help-Seeking
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills
- Token Composition: A Graph Based on EVM Logs