用严谨方法解释AI如何判断科技从业者是否需要心理帮助
Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces
- 构建形式化反演解释框架，挖掘AI决策背后的逻辑
- 揭示性别等敏感属性对心理求助预测的影响
- 帮助制定公平且可信赖的心理干预方案
本文提出一种形式化的反演解释框架，系统揭示AI在科技职场环境中预测心理求助行为背后的推理依据。通过计算模型输出的严格理由，该方法支持针对不同精神健康状况选择合适模型，并为伦理合规的应对策略提供基础。超越传统的随意可解释性，本研究明确考察性别等敏感属性对模型决策的影响，是公平性评估的关键环节。该框架与职场心理健康复杂现实相契合，助力可信部署与精准干预。
原文摘要 · Abstract (English)
This work proposes a formal abductive explanation framework designed to systematically uncover rationales underlying AI predictions of mental health help-seeking within tech workplace settings. By computing rigorous justifications for model outputs, this approach enables principled selection of models tailored to distinct psychiatric profiles and underpins ethically robust recourse planning. Beyond moving past ad-hoc interpretability, we explicitly examine the influence of sensitive attributes such as gender on model decisions, a critical component for fairness assessments. In doing so, it aligns explanatory insights with the complex landscape of workplace mental health, ultimately supporting trustworthy deployment and targeted interventions.
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