Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces
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Abstract (arXiv)
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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References · 6
- 2020On The Reasons Behind Decisionscited by 100
- 2022Logic-Based Explainability in Machine Learningcited by 48
- 2023Delivering Inflated Explanationscited by 16
- 2023From Robustness to Explainability and Back Againcited by 14
- 2023Disproving XAI Myths with Formal Methods -- Initial Resultscited by 12
- 2019Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Surveycited by 5
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