文章基本信息

标题：Bias Discovery in Machine Learning Models for Mental Health
本地全文：下载
作者：Pablo Mosteiro ; Jesse Kuiper ; Judith Masthoff 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2022
卷号：13
期号：5
页码：237
DOI：10.3390/info13050237
语种：English
出版社：MDPI Publishing
摘要：Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model trained on clinical mental health data. We collected structured data related to the admission, diagnosis, and treatment of patients in the psychiatry department of the University Medical Center Utrecht. We trained a machine learning model to predict future administrations of benzodiazepines on the basis of past data. We found that gender plays an unexpected role in the predictions—this constitutes bias. Using the AI Fairness 360 package, we implemented reweighing and discrimination-aware regularization as bias mitigation strategies, and we explored their implications for model performance. This is the first application of bias exploration and mitigation in a machine learning model trained on real clinical psychiatry data.