Mental Health AI Safety Claims Must Preserve Temporal Evidence
Mental health AI safety claims often fail because evaluations only look at isolated results, ignoring how interactions build over time. This paper introduces Temporal Safety Non-Identifiability to show that safety properties depending on sequence or accumulation cannot be certified by methods that discard temporal structure. It proposes SCOPE-MH to force developers to explicitly link safety claims with the evidence they actually retain.