Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health.
Orthod Craniofac Res
; 26 Suppl 1: 124-130, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-37846615
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Aprendizado de Máquina
Idioma:
En
Revista:
Orthod Craniofac Res
Assunto da revista:
ODONTOLOGIA
/
ORTODONTIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos