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Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
Race and Racialization in Mental Health Research and Implications for Developing and Evaluating Machine Learning
Marta M Maslej1, Nelson Shen2,3, Iman Kassam2
1Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada.
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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