Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces
This work addresses the need for trustworthy AI deployment and targeted interventions in mental health within diverse tech workplace settings, though it appears incremental by building on existing interpretability methods.
The paper tackled the problem of providing rigorous justifications for AI predictions of mental health help-seeking in tech workplaces, enabling principled model selection and ethically robust recourse planning, while examining the influence of sensitive attributes like gender for fairness.
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.