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

Gender Bias in Depression Detection Using Audio Features