🤖 AI Summary
This study addresses the lack of systematic examination regarding risk identification, mitigation strategies, and practical protective efficacy in adolescent AI safety research. Grounded in the YAIR risk taxonomy and the MIT mitigation taxonomy, this work conducts a systematic review of one hundred empirical studies to construct a “risk–countermeasure” mapping framework that precisely identifies unvalidated safety gaps. The findings reveal that existing protective measures remain largely theoretical, with evaluations disproportionately emphasizing technical performance over actual protective effectiveness, resulting in a severe deficiency in mechanism implementation and validation. This project proposes concrete research directions to bridge these gaps, providing critical empirical evidence for advancing adolescent AI safety protection within the field of Human-Computer Interaction.
📝 Abstract
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.