AI生成儿童性侵内容成新威胁，需重构安全机制应对
Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
- 针对儿童性侵内容生成难题，提出15个全周期安全挑战
- 现有安全技术因数据受限失效，需新方法突破限制
- 适合研究者、开发者与政策制定者参考落地防护方案
现代人工智能系统对儿童安全构成深远新威胁。AI正被滥用于生成人工智能驱动的儿童性虐待材料，助长儿童性剥削，并降低施害门槛。本文指出，防范此类风险亟需全新的AI安全范式。现有安全技术依赖数据可及性、透明度和评估机制，而这些在儿童性虐待材料相关伦理与法律约束下难以实现。我们分析了由此带来的新挑战，如数据集审计受限、红队测试困难、微调防范失效等。进而，在模型研发全生命周期中，梳理出15个在线儿童性剥削与虐待领域的开放问题，涵盖数据集构建、模型设计、部署及长期维护。我们向研究人员、开发者和政策制定者提出针对性建议，弥合理论安全与儿童保护现实之间的鸿沟。本工作旨在将防止AI助长儿童性剥削，确立为AI研究的核心安全维度，推动负责任的AI原则转化为切实有效的儿童保护机制。
原文摘要 · Abstract (English)
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.
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