AI-generated child sexual abuse material: what’s the harm?

Abstract The development of generative artificial intelligence (AI) tools capable of producing wholly or partially synthetic child sexual abuse material (AI CSAM) presents profound challenges for child protection, law enforcement, and societal responses to child exploitation. While some argue that the harmfulness of AI CSAM differs fundamentally from other CSAM due to a perceived absence of direct victimization, this perspective fails to account for the range of risks associated with its production and consumption. AI has been implicated in the creation of synthetic CSAM of children who have not previously been abused, the revictimization of known survivors of abuse, the facilitation of grooming, coercion, and sexual extortion, and the normalization of child sexual exploitation. Additionally, AI CSAM may serve as a new or enhanced pathway into offending by lowering barriers to engagement, desensitizing users to progressively extreme content, and undermining protective factors for individuals with a sexual interest in children. Drawing on a narrative review and conceptual synthesis of technical literature, psychological and criminological research, and civil society and law enforcement reporting, this paper provides a high-level primer on key generative AI technologies and examines the harms associated with AI CSAM. It cautions against claims that AI CSAM may function as a harm reduction tool, emphasizing how appeals to harmlessness obscure real risks and may contribute to inertia in ecosystem responses.