Child Safety in AI

Modern AI tools introduce new risks to children, including mental health concerns related to risks of unhealthy attachment and interactions that could lead to self-harm and suicidal ideation, as well as the ability to create and misuse synthetic content such as sexual deepfake images or videos that can facilitate harassment, grooming, and extortion. At the same time, child safety imposes unique constraints on traditional safety approaches. Unlike many other AI safety domains, child safety often prohibits direct access to harmful data, limits evaluation on real-world examples, and requires collaboration with specialized stakeholders such as NGOs, hotlines, law enforcement, and child-protection experts. These constraints create new scientific challenges that require dedicated research methodologies. This workshop will consider technical and sociotechnical solutions across the AI lifecycle, including topics such as safe data curation, reliable system safeguards, robust open-weight model design, adversarially resilient deployment, and effective long-term monitoring. By emphasizing open technical problems, such as evaluating safety without access to harmful data, preventing harmful capability emergence, and designing safeguards under adversarial pressure (all while taking into account legal and ethical constraints related to children) the workshop aims to catalyze a research agenda that treats child safety as a core, safety-critical dimension of AI.