Mental Health AI Safety Claims Must Preserve Temporal Evidence

Researchers argue that current mental health AI safety evaluations fail to detect clinically significant failures because they assess isolated responses rather than temporal patterns across conversations. The paper introduces Temporal Safety Non-Identifiability to formalize why sequence-dependent failures cannot be certified by turn-level evaluations, proposing SCOPE-MH as a new evaluation standard that preserves conversation history and cumulative effects.

Mental health AI systems face a fundamental evaluation gap that current safety protocols systematically miss. Traditional assessments score individual responses or aggregate conversation quality, but clinical harms often emerge from temporal dynamics: delayed crisis escalation, dependency formation through repeated reinforcement, failed repair attempts, and gradual deterioration across dialogue turns. This temporal blindness creates a false sense of safety where systems pass isolated benchmarks while failing in real clinical contexts.

The research builds on growing recognition that AI safety evaluation must match the actual mechanisms of harm. In mental health applications, the stakes are uniquely high—a chatbot might provide technically sound advice in isolation while systematically undermining a user's therapeutic progress through accumulated micro-interactions. The paper formalizes this problem through Temporal Safety Non-Identifiability, demonstrating mathematically why certain failure modes are inherently invisible to turn-level evaluation protocols.

The practical implications are substantial for AI developers and healthcare systems deploying mental health tools. SCOPE-MH operationalizes the principle by requiring evaluations that preserve full conversational evidence rather than aggregating it away. Testing on the AnnoMI motivational interviewing dataset reveals failure mechanisms completely hidden by traditional per-turn scoring.

For the AI safety field, this work signals that compartmentalized evaluation approaches risk certifying systems that are unsafe in deployment. Organizations building mental health AI will face pressure to adopt temporal-aware evaluation standards, potentially delaying product launches but improving genuine safety. Regulators considering AI healthcare guidelines may increasingly demand evidence preservation requirements, shifting how companies must report and validate safety claims.

• →Mental health AI safety evaluations systematically miss temporal failure modes like delayed escalation and dependency formation by assessing isolated responses rather than conversation sequences.
• →Temporal Safety Non-Identifiability formalizes why sequence-dependent harms cannot be certified by turn-level evaluation protocols, establishing a theoretical foundation for evaluation redesign.
• →SCOPE-MH provides an operational standard requiring evaluations to preserve full conversational evidence rather than aggregate it, revealing hidden failure mechanisms.
• →Current safety claims about mental health AI may be invalid because they lack evidence of the temporal dynamics where clinical harms actually occur.
• →Healthcare AI deployment will increasingly require temporal-aware evaluation standards, potentially impacting product development timelines and regulatory approval processes.