A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health
Adela C. Timmons, Jacqueline B. Duong, Natalia Simo Fiallo, Theodore Lee, Huong Phuc Quynh Vo, Matthew W. Ahle, Jonathan S. Comer, LaPrincess C. Brewer, Stacy L. Frazier, Theodora Chaspari
Abstract
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.