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Race and Racialization in Mental Health Research and Implications for Developing and Evaluating Machine Learning Models: A Rapid Review
Marta M. Maslej, Nelson Shen, Iman Kassam, Terri Rodak, Laura Sikstrom
1088 - 1089
10.3233/SHTI220281
Research Article
Studies in Health Technology and Informatics
Volume 290: MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation
Abstract

Machine learning models are often trained on sociodemographic features to predict mental health outcomes. Biases in the collection of race-related data can limit the development of useful and fair models. To assess the current state of this data in mental health research, we conducted a rapid review guided by Critical Race Theory. Findings reveal limitations in the measurement and reporting of race and ethnicity, potentially leading to models that amplify health inequities.

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