A Latency-Based Risk Escalation Framework for Safe Deployment of AI Systems in Mental Health

AI systems are increasingly deployed in mental health contexts, where delayed intervention and overconfident autonomy can lead to significant harm. This work introduces a latency-based risk escalation framework that governs when AI systems should reduce autonomy and escalate to human oversight. The framework treats delayed recovery, increasing corrective effort, and shrinking recovery capacity as first-class safety signals. It is explicitly non-diagnostic and non-therapeutic, functioning as a governance and control layer rather than a predictive or clinical model. Conceptual structure, escalation tiers, and illustrative scenarios are presented, while implementation-specific parameterization, thresholds, and optimization details are intentionally withheld. The framework is intended to support safer AI deployment and motivate controlled pilot evaluations.