  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.
