Mental Health AI Safety Claims Must Preserve Temporal Evidence
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
Record details
Published: 9 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (9 May 2026), “Mental Health AI Safety Claims Must Preserve Temporal Evidence,” evidence record 4673, https://ethics.ai/record/4673 (originally published by arXiv).
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