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Fairness and bias are crucial concepts in artificial intelligence, yet theyare relatively ignored in machine learning applications in clinical psychiatry.We computed fairness metrics and present bias mitigation strategies using amodel trained on clinical mental health data. We collected structured datarelated to the admission, diagnosis, and treatment of patients in thepsychiatry department of the University Medical Center Utrecht. We trained amachine learning model to predict future administrations of benzodiazepines onthe basis of past data. We found that gender plays an unexpected role in thepredictions-this constitutes bias. Using the AI Fairness 360 package, weimplemented reweighing and discrimination-aware regularization as biasmitigation strategies, and we explored their implications for modelperformance. This is the first application of bias exploration and mitigationin a machine learning model trained on real clinical psychiatry data.

Bias Discovery in Machine Learning Models for Mental Health