通过分析模型内部表示，发现话题增强能提升自杀意念检测的可解释性。
Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
- 用可视化与几何分析揭示模型如何编码心理风险因素
- 话题增强使移民、家庭问题等弱势因素表征更清晰
- 适合关注心理健康AI可解释性的研究者与从业者
自杀意念检测模型通常仅以整体性能指标评估，但对其内部如何表征心理风险因素知之甚少。在高风险的心理健康应用中，理解这些内部表示对安全、透明和负责任部署至关重要。本文超越准确率，分析在原始数据集与话题增强数据集上训练的检测模型，其内部表示空间中对心理风险因素的编码方式。通过可视化与几何分析，考察话题相关特征的连贯性与可分性。结果表明，话题感知增强显著提升了移民、家庭问题、经济危机等被忽视心理社会风险因素的表征清晰度与区分度。这说明，增强不仅提升模型性能，还带来更结构化、可解释的内部表示。
原文摘要 · Abstract (English)
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. Using visualization and geometric analysis, we examine the coherence and separability of topic-related features. Our results show that topic-aware augmentation increases the clarity and distinctness of underrepresented psychosocial risk factors such as immigration, family issues, and financial crisis. These findings suggest that augmentation not only improves model performance but also leads to more structured and interpretable internal representations.
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