Fairness and bias correction in machine learning for depression prediction across four study populations.

ABSTRACT: 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 learned 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 regularly present 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. There is no one best ML model for depression prediction that provides equality of outcomes. This emph

SUBMITTER: Dang VN 

PROVIDER: S-EPMC10991528 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature