Mental Health AI Safety Claims Must Preserve Temporal Evidence

2026-05-09

AI Summary

This study addresses a critical gap in current safety evaluations of AI systems for mental health, which often overlook the temporal dynamics of human–AI interactions and thus fail to detect clinical risks arising from dialogue sequencing, cumulative effects, or delayed deterioration. To remedy this, the authors propose SCOPE-MH, a novel framework that formally defines “temporal safety unidentifiability” and introduces safety assessment principles grounded in the preservation of temporal evidence. Leveraging formal modeling, sequential dialogue analysis, and the expert-annotated AnnoMI dataset, SCOPE-MH establishes domain-specific evaluation criteria tailored to mental health applications. Empirical results demonstrate that conventional turn-level scoring methods miss key failure modes, whereas SCOPE-MH effectively captures temporally dependent risks, substantially enhancing the clinical relevance and reliability of safety assessments.

Abstract

The safety of mental health AI is often judged at the wrong temporal scale. Current evaluations typically score isolated responses, endpoint outcomes, or aggregate dialogue quality, while clinically consequential failures may arise from the order and accumulation of interactions themselves, including delayed escalation, repeated reinforcement, dependency formation, failed repair, and gradual deterioration across turns. This paper argues that this mismatch is not merely a limitation of evaluation coverage but a source of invalid safety conclusions. We introduce Temporal Safety Non-Identifiability, a formal account of why safety properties that depend on sequence, timing, accumulation, or recovery cannot be certified by protocols that discard those features. From this formalization, we develop SCOPE (Safety Claims Over Preserved Evidence) as a general principle for aligning safety claims with the evidence an evaluation actually retains, and instantiate it as SCOPE-MH, a mental-health instantiation of this reporting standard. We operationalize SCOPE-MH through a proof-of-concept on the AnnoMI dataset of expert-annotated motivational interviewing conversations, which reveals mechanisms of failure that per-turn behavior scoring does not represent. We propose SCOPE-MH as a diagnostic complement to existing evaluation infrastructure and argue that evaluation preserving temporal evidence is necessary, not optional, for safety-critical mental health AI deployment.

Problem

• Mental Health AI
• Temporal Safety
• Safety Evaluation
• Sequence Dependence
• Temporal Evidence

Innovation

• Temporal Safety Non-Identifiability
• SCOPE-MH
• mental health AI safety
• temporal evidence preservation
• dialogue sequence evaluation