The reliability of artificial intelligence systems providing mental health advice is being questioned due to fundamental flaws in how they are evaluated.
Current assessment methods often rely on stateless testing, which fails to account for the contextual continuity required in real-world therapeutic interactions.
This discrepancy means that AI models may appear competent in isolated benchmarks while performing poorly in sustained, multi-turn conversations with users.
The critique underscores a growing gap between technical benchmarks and practical safety standards in the generative AI sector.
As the threshold for acceptable quality in AI-generated content shifts, the stakes for applications involving human well-being are particularly high.
Investors and regulators are increasingly focused on whether current evaluation frameworks can adequately protect users from misjudged or harmful advice.