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

TL;DR

This paper introduces a formal abductive explanation framework for AI models predicting mental health help-seeking in tech workplaces, enhancing interpretability, fairness, and ethical decision-making.

Contribution

It presents a novel formal framework for systematically generating explanations of AI predictions related to mental health in workplace settings, incorporating fairness considerations.

Findings

Provides rigorous justifications for model outputs

Enables model selection based on psychiatric profiles

Assesses influence of sensitive attributes like gender

Abstract

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.