A study found that standard machine learning approaches often exhibit biased behaviours in predicting depression across different populations. It also demonstrated that both standard and novel post-hoc-bias mitigation techniques can effectively reduce unfair bias, though no single model achieves equality of outcomes.
The biases that were identified risk reinforcing structural inequalities in mental healthcare, particularly affecting underserved populations. This underscores the importance of analyzing fairness during model selection and transparently reporting the impact of debiasing interventions to ensure equitable healthcare applications.
Learn more about this study here: https://doi.org/10.1038/s41598-024-58427-7
Reference
Dang, V.N., Cascarano, A., Mulder, R.H. et al. Fairness and bias correction in machine learning for depression prediction across four study populations. Sci Rep 14, 7848 (2024).