Evaluating AI Safety in Teen Conversations

A study of 648 simulated, multi-turn conversations between fictional teenagers and nine AI chatbot models found that 27.5% included at least one critical mental-health safety failure, with 62% of those failures emerging only later in the exchange rather than in the first response. Clinician-authored scenarios covered self-harm and safety risks, medical and therapeutic advice, and potentially dependent relationships with AI, while evaluators assessed 27 safety checks including diagnosis overreach, missed referrals to human support, inappropriate role claims, and presenting AI as a substitute relationship. Common failures included initially appropriate responses that later shifted into unsupported medical reassurance, reduced encouragement to seek outside help, or resumed parental roleplay after agreeing to stop; nine conversations also showed problems in handling suicide attempts or self-harm. Performance varied across models, although overlapping scenario-adjusted ranges prevented clear rankings, and the researchers noted that API results may not reflect consumer applications with additional safeguards. In a separate comparison, adding an instruction explicitly identifying the user as a teenager and prioritizing real-world support reduced critical-failure conversations from 31.1% to 11.5%, suggesting that age-specific safety guidance can help but does not eliminate risks.

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