Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
📰 ArXiv cs.AI
Learn to interpret topic representation in suicide ideation detection models for safer and more transparent mental health applications
Action Steps
- Train a suicide ideation detection model using a dataset of mental health texts
- Apply topic modeling techniques to extract psychologically meaningful risk factors
- Analyze the internal representations of the model to understand how it captures risk factors
- Evaluate the model's performance using metrics beyond accuracy, such as interpretability and transparency
- Refine the model to improve its ability to capture nuanced psychological concepts
Who Needs to Know This
Data scientists and mental health professionals can benefit from this knowledge to improve the safety and transparency of suicide ideation detection models
Key Insight
💡 Understanding internal representations of suicide ideation detection models is crucial for safety, transparency, and responsible deployment
Share This
🚨 Go beyond accuracy in suicide ideation detection models! 🤖 Learn to interpret topic representation for safer & more transparent mental health apps #AIforMentalHealth #InterpretableML
Key Takeaways
Learn to interpret topic representation in suicide ideation detection models for safer and more transparent mental health applications
Full Article
Title: Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
Abstract:
arXiv:2606.07714v1 Announce Type: cross 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
Abstract:
arXiv:2606.07714v1 Announce Type: cross 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
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