# Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces

arXiv: https://arxiv.org/abs/2603.14007 · HTML: https://mlsanity.com/paper/2603.14007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [On The Reasons Behind Decisions](https://mlsanity.com/paper/2002.09284/) (2020)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [Delivering Inflated Explanations](https://mlsanity.com/paper/2306.15272/) (2023)
- [From Robustness to Explainability and Back Again](https://mlsanity.com/paper/2306.03048/) (2023)
- [Disproving XAI Myths with Formal Methods -- Initial Results](https://mlsanity.com/paper/2306.01744/) (2023)
- [Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey](https://mlsanity.com/paper/1907.12386/) (2019)

## 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.