Formal Abductive Explanations for Navigating Mental Health Help-Seeking and Diversity in Tech Workplaces
arXiv:2603.14007v1 Announce Type: new 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.
arXiv:2603.14007v1 Announce Type: new 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.
Executive Summary
The article introduces a formal abductive explanation framework tailored to elucidate AI-generated predictions of mental health help-seeking within tech workplaces. This framework offers a systematic, rigorous method for generating justifications for model outputs, enabling a departure from ad-hoc interpretability toward principled model selection. Particularly noteworthy is its focus on sensitive attributes like gender, which enhances fairness assessments and supports ethically robust recourse planning. The work bridges computational explainability and mental health intervention strategies, aligning analytical insights with the nuanced demands of workplace mental health support. This represents a meaningful advance in ethical AI deployment.
Key Points
- ▸ Formal abductive framework for mental health AI predictions
- ▸ Systematic justifications for model outputs
- ▸ Inclusion of sensitive attributes (e.g., gender) for fairness
Merits
Strength in Methodological Innovation
The framework’s formal abductive structure introduces a novel computational approach to explainability, offering transparency and rigor in AI-driven mental health assessments.
Ethical Advance
By explicitly addressing sensitive attributes and their influence on model decisions, the work advances ethical AI application in high-stakes domains.
Demerits
Scope Limitation
The framework’s focus on psychiatric profiles and tech workplaces may limit applicability to other sectors or non-clinical mental health contexts.
Implementation Complexity
Computational rigor may pose practical barriers to deployment in resource-constrained environments without adequate infrastructure.
Expert Commentary
This paper represents a pivotal convergence of computational logic and social responsibility. The formal abductive model offers a structured, defensible mechanism for explaining AI predictions in sensitive domains—particularly compelling in mental health, where misinterpretation can have tangible human consequences. The inclusion of gender as a sensitive attribute is especially commendable, as it reflects a nuanced understanding of bias dynamics in algorithmic decision-making. While the framework’s applicability is currently constrained to specific domains, its conceptual architecture is robust enough to inspire broader adaptations across healthcare and beyond. The authors should be credited for elevating the discourse on ethical AI beyond superficial interpretability toward substantive, actionable explainability.
Recommendations
- ✓ Extend the framework to other mental health domains (e.g., substance abuse, PTSD) for scalability.
- ✓ Develop open-source tools or modular implementations to lower barriers to adoption.