arXiv AI By Srimonti Dutta, Ratna Kandala

Mental Health AI Safety Claims Must Preserve Temporal Evidence

Read the original on arXiv AI →

arXiv:2605. 08827v2 Announce Type: replace Abstract: The safety of mental health AI is often judged at the wrong temporal scale.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 2

AI Morbidity and Mortality: A Framework for Clinical AI Failure Review

AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.

By Paulius Mui, Dean F. Sittig, Steve Labkoff, Sanjay Basu
arXiv AI
Jul 2

Adversarial Pragmatics for AI Safety Evaluation: A Benchmark for Instruction Conflict, Embedded Commands, and Policy Ambiguity

arXiv:2607. 01153v1 Announce Type: cross Abstract: Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model has followed an instruction, refused appropriately, complied with a policy, resisted an embedded command, or misreported progress in an agentic task.

By Brett Reynolds
arXiv AI
Aug 28

A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian

Anian is a safety‑gated multimodal AI backend designed for perinatal mental‑health support and mindfulness‑intervention routing. It maps user input into a four‑layer hierarchical state representation—emotion, psychosocial constructs, safety risk, and intervention routes—then fuses local and external risk signals to decide whether to generate AI responses or provide fixed safety content. Prototype evaluation on large public corpora showed high classification performance and perfect high‑risk recall in a controlled stress test, though clinical validity remains unestablished.

By Lei Wang, Xiao Wang, Lei Li