Mental Health AI Safety Claims Must Preserve Temporal Evidence
A new academic paper submitted to arXiv argues that current safety evaluations for mental health artificial intelligence are fundamentally flawed because they ignore temporal dynamics. The authors, Srimonti Dutta and Ratna Kandala, contend that judging AI safety based on isolated responses or aggregate dialogue quality fails to capture clinically consequential failures such as delayed escalation, dependency formation, and gradual deterioration across conversation turns. They introduce the concept of 'Temporal Safety Non-Identifiability' to explain why safety properties dependent on sequence and timing cannot be certified by protocols that discard these features. To address this, the researchers propose SCOPE (Safety Claims Over Preserved Evidence), a general principle aligning safety claims with retained evidence, and its specific instantiation, SCOPE-MH. Through a proof-of-concept using the AnnoMI dataset of expert-annotated motivational interviewing conversations, the study reveals failure mechanisms invisible to per-turn scoring. The authors assert that preserving temporal evidence in evaluations is not optional but necessary for the safe deployment of AI in critical mental health contexts, offering SCOPE-MH as a diagnostic complement to existing infrastructure.
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Mental Health AI Safety Claims Must Preserve Temporal Evidence
A new academic paper submitted to arXiv argues that current safety evaluations for mental health artificial intelligence are fundamentally flawed because they ignore temporal dynamics. The authors, Srimonti Dutta and Ratna Kandala, contend that judging AI safety based on isolated responses or aggregate dialogue quality fails to capture clinically consequential failures such as delayed escalation, dependency formation, and gradual deterioration across conversation turns. They introduce the concept of 'Temporal Safety Non-Identifiability' to explain why safety properties dependent on sequence and timing cannot be certified by protocols that discard these features. To address this, the researchers propose SCOPE (Safety Claims Over Preserved Evidence), a general principle aligning safety claims with retained evidence, and its specific instantiation, SCOPE-MH. Through a proof-of-concept using the AnnoMI dataset of expert-annotated motivational interviewing conversations, the study reveals failure mechanisms invisible to per-turn scoring. The authors assert that preserving temporal evidence in evaluations is not optional but necessary for the safe deployment of AI in critical mental health contexts, offering SCOPE-MH as a diagnostic complement to existing infrastructure.
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