Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
Neil Kale, Rebecca Portnoff, Pratiksha Thaker, Michael Simpson, Robertson Wang, Kevin Kuo, Chhavi Yadav, Virginia Smith
Why It Matters
What makes this one worth your time
As AI technologies evolve, addressing their misuse in child exploitation is critical for ensuring child safety and guiding responsible AI development.
This work highlights the urgent need for innovative AI safety measures to combat AI-facilitated child exploitation.
Summary
The paper discusses the risks posed by AI in generating child sexual abuse material and argues for new AI safety approaches that address the unique ethical and legal challenges in this area, proposing 15 open problems and targeted recommendations for stakeholders.
Key contributions
- Identification of 15 open problems related to AI and child sexual exploitation.
- Proposed targeted recommendations for researchers, developers, and policymakers.
- A framework for integrating child protection into AI safety practices.
Notable insights
- The paper identifies specific ethical and legal constraints that hinder traditional AI safety techniques, emphasizing the need for tailored solutions.
- It outlines a comprehensive set of open problems that span the entire AI development lifecycle, indicating a systematic approach to addressing the issue.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.05407v1 Announce Type: cross Abstract: Modern artificial intelligence (AI) systems present profound new risks to child safety. AI is increasingly being misused to create AI-generated child sexual abuse material, facilitate child sexual exploitation, and reduce barriers to harm. In this paper, we argue that protecting children from AI-facilitated sexual abuse requires new approaches to AI safety. Existing safety techniques assume data accessibility, transparency, and evaluation practices that are incompatible with the ethical and legal constraints surrounding child sexual abuse material. We examine how these constraints create new technical challenges, such as limitations on dataset auditing, red teaming, and fine-tuning prevention. In turn, we outline *15 open problems* in online child sexual exploitation and abuse across the AI development lifecycle, from dataset curation and model design to deployment and long-term maintenance. We propose targeted recommendations for researchers, developers, and policymakers to bridge the gap between theoretical AI safety and the realities of child protection. Our work aims to reframe preventing AI-facilitated child sexual abuse as a central, safety-critical dimension for AI research, motivating work that translates responsible AI principles into concrete safeguards against the exploitation of children.