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.
@article{arxiv.2603.14007,
title = {Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces},
author = {Belona Sonna and Alain Momo and Alban Grastien},
journal= {arXiv preprint arXiv:2603.14007},
year = {2026}
}
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Appeared in the Proceedings of the Empowering Women of Colour in AI-Driven Mental Health Research at IJCAI 2025