Towards Gender Fairness for Mental Health Prediction

Abstract

Mental health is becoming an increasingly prominent health challenge. Despite a plethora of studies analysing and mitigating bias for a variety of tasks such as face recognition and credit scoring, research on machine learning (ML) fairness for mental health has been sparse to date. In this work, we focus on gender bias in mental health and make the following contributions. First, we examine whether bias exists in existing mental health datasets and algorithms. Our experiments were conducted using Depresjon, Psykose and D-Vlog. We identify that both data and algorithmic bias exist. Second, we analyse strategies that can be deployed at the pre-processing, in-processing and post-processing stages to mitigate for bias and evaluate their effectiveness. Third, we investigate factors that impact the efficacy of existing bias mitigation strategies and outline recommendations to achieve greater gender fairness for mental health. Upon obtaining counter-intuitive results on D-Vlog dataset, we undertake further experiments and analyses, and provide practical suggestions to avoid hampering bias mitigation efforts in ML for mental health.

Evaluating and mitigating unfairness in multimodal remote mental health assessments

“…Despite their longstanding central role in real-time mental health research, focused studies on their fairness, particularly in comparing different modalities and evaluating self-rated versus clinically diagnosed labels, are markedly scant. Existing studies [40, 41] primarily concentrate on gender fairness in unimodal or bimodal classifiers, leaving a significant aspect of this research area unexplored.…”