Bias Reducing Multitask Learning on Mental Health Prediction

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops pp. 1 - 8
• Main Authors: Zanna, Khadija, Sridhar, Kusha, Yu, Han, Sano, Akane
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.10.2022
• Subjects: Analytical models; Anxiety disorders; bias; epistemic uncertainty; fairness metric; Machine learning; Mental health; Monte-Carlo dropout; multi-task learning; Physiology; Predictive models; protected label; Uncertainty
• ISSN: 2156-8111
• DOI: 10.1109/ACII55700.2022.9953850

There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, and identify illnesses at an earlier stage when interventions may be more effective. However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities. This lack of standards persists due to factors such as technical difficulties, complexities of high dimensional clinical health data, etc., which are especially true for physiological signals. This along with prior evidence of relations between some physiological signals with certain demographic identities restates the importance of exploring bias in mental health prediction models that utilize physiological signals. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models using ECG data. Our method is based on the idea of epistemic uncertainty and its relationship with model weights and feature space representation. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not, and our bias mitigation method performed better at reducing the bias in the model, when compared to the reweighting mitigation technique. Our analysis on feature importance also helped identify relationships between heart rate variability and multiple demographic groupings.