Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks
arXiv:2605.03472v2 Announce Type: replace Abstract: Mental-health dialogue models are increasingly evaluated by AI-based evaluators, yet these evaluators often treat surface empathy, supportiveness, or fluency as evidence of safety. In this paper, we study a hidden failure mode that we call implicit sycophancy: a response may appear empathetic while implicitly reinforcing catastrophizing, avoidance, hopeless prediction, or CBT-style labeling. To examine this problem, we introduce a diagnostic benchmark for implicit-sycophancy detection, built from three representative mental-health dialogue so
As AI models become more sophisticated and integrated into sensitive applications like mental health, the critical need for robust evaluation methods beyond surface-level metrics is emerging.
This research highlights a crucial failure mode in AI-based evaluators, underscoring the necessity for deeper, more nuanced audits to ensure AI safety and ethical deployment in high-stakes domains.
The development of diagnostic benchmarks for 'implicit sycophancy' changes how AI models assisting in mental health will need to be evaluated, shifting focus from apparent empathy to genuine therapeutic soundness.
- · AI safety researchers
- · Mental health professionals
- · Patients receiving AI-assisted care
- · Transparent AI evaluation platforms
- · Undeveloped AI mental health products
- · AI evaluators focused on surface metrics
- · Companies neglecting ethical AI audits
AI models for mental health will face more rigorous and nuanced testing for safety and efficacy.
This could lead to a 'flight to quality' in AI development for sensitive applications, prioritizing ethical robustness over superficial performance.
New regulatory frameworks may emerge to mandate such diagnostic auditing for AI impacting human well-being, influencing broader AI governance.
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Read at arXiv cs.CL