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

Belona Sonna; Alain Momo; Alban Grastien

cs.AIMar 14, 2026

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

Authors: Belona Sonna, Alain Momo, Alban Grastien

Organizations: Australian National University · Australian Reinforcing Company

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.

Figures & tables

Steps	$x_{0}$	$x_{1}$	$x_{2}$	$x_{3}$	$x_{4}$	$x_{5}$	$x_{6}$	$x_{7}$	$x_{8}$	$x_{15}$	$x_{16}$	$\exists x^{\prime}$
1	?	1	0	1	1	0	1	1	1	1	1	$\bot$
2	?	?	0	1	1	0	1	1	1	1	1	$\bot$
3	?	?	?	1	1	0	1	1	1	1	1	$\bot$
4	?	?	?	?	1	0	1	1	1	1	1	$\top$
5	?	?	?	1	?	0	1	1	1	1	1	$\bot$
6	?	?	?	1	?	?	1	1	1	1	1	$\bot$

Table 1: Computing an explanation for individual

[1,1,0,1,1,0,1,1,1,0,0,0,0,0,0,1,1,0,0]

Unbiased decisions	Negative	Positive
864	290	103

Table 2: Individual bias in the decision making process

Features	Non influenced	Negative	Positive
$x_{0}$	1027	113	117
$x_{1}$	864	290	103
$x_{2}$	1065	107	85
$x_{3}$	630	422	205
$x_{4}$	1126	59	72
$x_{5}$	1109	81	67

Table 3: Influence of features on the model outputs

Outcomes	Combinations	Ratio
Outcomes	Combinations	Whole	Specific
Negative	$x_{3}\land x_{14}\land x_{18}$	9.3%	19.1%
Negative	$x_{3}\land x_{11}\land x_{18}$	10.6%	21.9%
	$x_{1}\land x_{14}\land x_{18}$	8.5%	17.5%
	$x_{1}\land x_{3}\land x_{18}$	10.5%	21.8%
	$x_{1}\land x_{3}\land x_{14}$	8.9%	18.5%

Table 4: Critical combinations of features

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Figure 1: Classification report of the trained model.

Explore similar work

Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data
Jun 19, 2026Arsham Azam, Rasikh Ali, Tayyaba Farhat +1Explainable Artificial Intelligence Depression Detection
Faculty of Computer Science and Information Technology, The Superior University, Lahore 54600, Pakistan · Intelligent Data Visual Computing Research (IDVCR), Lahore 54600, Pakistan · Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 12571, Saudi Arabia
Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers
Jun 23, 2026Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon +2Healthcare Depression Detection
Department of Information Sciences and Technology George Mason University USA · Department of Computer Science and Engineering Jahangirnagar University Bangladesh · Department of Computer Science AIUB Bangladesh +2
Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling
Jun 22, 2026Ahnaf Atef Choudhury, Shahriar Siddique Ayon, Md. Ebrahim Hossain +1Clinical Prediction
Department of Information Sciences and Technology George Mason University USA · Department of Computer Science American International University-Bangladesh Bangladesh · Department of Computer Science and Engineering Dhaka University of Engineering and Technology Bangladesh

Jun 19, 2026J/K move · Enter open · S save

Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data

Arsham Azam, Rasikh Ali, Tayyaba Farhat, Sheeraz Akram

Faculty of Computer Science and Information Technology, The Superior University, Lahore 54600, Pakistan · Intelligent Data Visual Computing Research (IDVCR), Lahore 54600, Pakistan · Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 12571, Saudi Arabia

Explainable Artificial Intelligence Depression Detection

Open paper AskPDF arXiv

Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using multimodal data and Federated Learning (FL) to identify early indicators of career-related mental health problems in a privacy-preserving and culturally responsive manner. The framework combines structured behavioral data and facial emotion features from interview videos via an intermediate fusion neural network with attention mechanisms. Label smoothing was applied to improve model generalizability. FL was used across institutions to enable collaborative training without raw data sharing. Evaluation was conducted using the Student Mental Health Survey dataset from university students across Pakistan. Our model attained an F1-score of 89.12%, recall of 86.54%, accuracy of 92.08%, and precision of 91.88%. Using Integrated Gradients and SHAP, the model identified key behavioral markers of depression including avoidance of direct gaze, lower facial expressiveness, and social withdrawal, consistent with psychological theory. This research presents an interpretable, scalable, and context-sensitive AI system for mental health pre-diagnosis with potential integration into student support services globally.

Abstract

Explore similar work

Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data

Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers

Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling