# "I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products

arXiv: https://arxiv.org/abs/2506.14018 · HTML: https://mlsanity.com/paper/2506.14018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Scaling Reinforcement Learning for Content Moderation with Large Language Models](https://mlsanity.com/paper/2512.20061/) (2025)
- [Black Box Absorption: LLMs Undermining Innovative Ideas](https://mlsanity.com/paper/2510.20612/) (2025)
- [World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the World](https://mlsanity.com/paper/2608.07510/) (2026)

## Abstract (arXiv)

While recent research has focused on developing safeguards for generative AI (GAI) model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies and their enforcement in GAI online tools -- consumer-facing web-based GAI applications. We first analyzed content moderation policies of 14 GAI online tools. While these policies are comprehensive in outlining moderation practices, they usually lack details on practical implementations and are not specific about how users can aid in moderation or appeal moderation decisions. Next, we examined user-experienced content moderation successes and failures through Reddit discussions on GAI online tools. We found that although moderation systems succeeded in blocking malicious generations pervasively, users frequently experienced frustration in failures of both moderation systems and user support after moderation. Based on these findings, we suggest improvements for content moderation policy and user experiences in real-world GAI products.