arXiv:2606.07714v1 Announce Type: new Abstract: Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors. In high-stakes mental health applications, understanding these internal representations is essential for safety, transparency, and responsible deployment. In this work, we move beyond accuracy and analyze how suicide detection models trained on original and topic-augmented datasets encode psychological risk factors in their internal representation space. Usi
The increasing deployment of AI in sensitive applications like mental health necessitates a deeper understanding of model interpretability beyond traditional performance metrics.
This research addresses critical concerns around safety, transparency, and responsible deployment of AI in high-stakes fields by focusing on how models internally represent complex psychological factors.
The focus in AI model evaluation expands from mere accuracy to include the interpretability of internal representations, particularly for ethically sensitive applications.
- · AI ethics researchers
- · Mental health professionals leveraging AI
- · Patients relying on AI-driven diagnoses
- · Developers of transparent AI systems
- · AI developers prioritizing only aggregate performance
- · Black-box AI model providers in sensitive sectors
- · Organizations deploying uninterpretable AI
Increased demands for explainable AI (XAI) tools and methodologies, especially in healthcare.
New regulatory frameworks and certification processes for AI models that emphasize transparency and interpretability in critical applications.
Public distrust in AI systems that cannot transparently explain their decision-making processes, leading to slower adoption in sensitive areas.
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Read at arXiv cs.LG