  A significant level of stigma and inequality exists in mental healthcare,
especially in under-served populations. Inequalities are reflected in the data
collected for scientific purposes. When not properly accounted for, machine
learning (ML) models leart from data can reinforce these structural
inequalities or biases. Here, we present a systematic study of bias in ML
models designed to predict depression in four different case studies covering
different countries and populations. We find that standard ML approaches show
regularly biased behaviors. We also show that mitigation techniques, both
standard and our own post-hoc method, can be effective in reducing the level of
unfair bias. No single best ML model for depression prediction provides
equality of outcomes. This emphasizes the importance of analyzing fairness
during model selection and transparent reporting about the impact of debiasing
interventions. Finally, we provide practical recommendations to develop
bias-aware ML models for depression risk prediction.
