arXiv:2605.08827v2 Announce Type: replace 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 conclus
As mental health AI applications become more prevalent, the limitations of current evaluation methodologies are becoming apparent, necessitating a re-assessment of safety claims.
This paper highlights a critical flaw in how AI safety, particularly in sensitive domains like mental health, is assessed, moving beyond static evaluations to dynamic, real-world interaction analysis.
The focus for evaluating mental health AI will likely shift from isolated performance metrics to continuous, temporal analyses that capture the cumulative effects of interaction over time.
- · AI safety researchers
- · Ethical AI developers
- · Mental healthcare providers
- · Patients
- · AI developers with static evaluation methods
- · Untested mental health AI products
- · AI companies prioritizing speed over safety rigor
Immediate first-order effect is a renewed demand for more robust and temporally aware AI safety evaluation frameworks.
Plausible second-order consequence is the development of new AI safety standards and regulations that mandate temporal evidence in mental health applications.
Speculative but reasoned third-order consequence is a significant delay in the widespread adoption of mental health AI until these advanced safety protocols are established and proven.
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Read at arXiv cs.AI