  Depression is a large-scale mental health problem and a challenging area for
machine learning researchers in detection of depression. Datasets such as
Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) have been created
to aid research in this area. However, on top of the challenges inherent in
accurately detecting depression, biases in datasets may result in skewed
classification performance. In this paper we examine gender bias in the
DAIC-WOZ dataset. We show that gender biases in DAIC-WOZ can lead to an
overreporting of performance. By different concepts from Fair Machine Learning,
such as data re-distribution, and using raw audio features, we can mitigate
against the harmful effects of bias.
