Identifying Biases in Clinical Decision Models Designed to Predict Need of Wraparound Services
Date
2021-03
Language
American English
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AMIA Informatics summit 2021 Conference Proceedings
Abstract
Investigation of systemic biases in AI models for the clinical domain have been limited. We re-created a series of models predicting need of wraparound services, and inspected them for biases across age, gender and race using the AI Fairness 360 framework. AI models reported performance metrics which were comparable to original efforts. Investigation of biases using the AI Fairness framework found low likelihood that patient age, gender and sex are introducing bias into our algorithms.
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Cite As
Kasthurirathne, S. N., Vest, J. R., Grannis, S. J., (2021, March). Identifying Biases in Clinical Decision Models Designed to Predict Need of Wraparound Services. AMIA Informatics summit 2021 Conference Proceedings.
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