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This systematic review of 100 HCI studies on AI safety for youth reveals a significant gap between identified risks and evaluated countermeasures. The analysis, utilizing the YAIR and MIT Mitigation taxonomies, found that while numerous risks to youth interacting with AI have been documented across various settings, most proposed protections remain ideated, with very few implemented or empirically evaluated for their effectiveness in preventing harm. The findings highlight critical areas for future HCI research to develop and rigorously test practical safeguards for youth AI safety.
Most AI safety measures for youth are still just ideas, with almost no real-world testing to see if they actually work.
While HCI increasingly examines AI-safety for youth, the literature lacks a comprehensive view of what risks have been identified, how they are addressed, and whether proposed protections work in-practice. We systematically reviewed 100 empirical HCI studies involving children and youth interacting with or exposed to AI across schools, homes, care settings, and public services. Using the YAIR taxonomy for risks and the MIT Mitigation Taxonomy for countermeasures, we map which risks have been identified, whether each risk is addressed by countermeasure(s), and whether each countermeasure for that risk is implemented and even evaluated. The risk-countermeasure mapping shows that most risks are matched only with proposed/ideated countermeasures; few countermeasures have been implemented, and fewer still evaluated; and existing evaluations often measure technical performance rather than protection from harm. We identify where coverage is absent, where safeguards remain untested, and propose concrete directions for HCI research to strengthen youth AI-safety.
