Bias Discovery in Machine Learning Models for Mental Health

Information

Floortje Scheepers¹  Judith Masthoff²  Jesse Kuiper²  Pablo Mosteiro²  Marco Spruit² 

[1] Afdeling Psychiatrie, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands;Department of Information and Computing Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands;

关键词: fairness;    bias;    artificial intelligence;    machine learning;    psychiatry;    health;   

DOI  :  10.3390/info13050237

来源: DOAJ

【 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.

【 授权许可】

Unknown