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

2026-03-14

AI Summary

This study addresses the lack of interpretability and fairness in existing AI models for predicting help-seeking behaviors related to mental health in technology workplaces, which hinders ethically grounded interventions. To bridge this gap, the work introduces formal abductive explanations into this domain for the first time, proposing an interpretable framework that systematically uncovers the rationale behind model decisions and explicitly analyzes the influence of sensitive attributes such as gender. By jointly optimizing interpretability and fairness, the approach enables tailored model selection and intervention strategies based on individual psychological profiles. This provides a foundational technical pathway toward trustworthy and equitable AI applications in workplace mental health support.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• abductive explanation
• mental health help-seeking
• tech workplaces
• fairness
• sensitive attributes

Innovation

• formal abductive explanation
• mental health help-seeking
• algorithmic fairness
• sensitive attributes
• interpretable AI