Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health |
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Authors: | Veerasathpurush Allareddy Maysaa Oubaidin Sankeerth Rampa Shankar Rengasamy Venugopalan Mohammed H. Elnagar Sumit Yadav Min Kyeong Lee |
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Affiliation: | 1. Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA;2. Health Care Administration Program, School of Business, Rhode Island College, Providence, Rhode Island, USA;3. Department of Orthodontics, Tufts University School of Dental Medicine, Boston, Massachusetts, USA;4. Department of Orthodontics, University of Nebraska Medical Center, Lincoln, Nebraska, USA |
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Abstract: | Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno-racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno-racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities. |
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Keywords: | algorithms artificial intelligence ethnoracial disparities health disparities machine learning racial bias |
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