MATTOS, Marcelo; SIQUEIRA, Sean; GARCIA, Ana. Fair and Equitable Machine Learning Algorithms in Healthcare: A Systematic Mapping. In: 16th International Conference on Agents and Artificial Intelligence – Volume 3: ICAART, 16. , 2024, Rome/Italy. Proceedings […]. Setúbal: Science and Technology Publications, Lda, 2024 . p. 815-822. DOI: http://dx.doi.org/10.5220/0012394700003636.
Fair and Equitable Machine Learning Algorithms in Healthcare: A Systematic Mapping
Authors
Marcelo S. Mattos (UNIRIO)
Sean W. M. Siqueira (UNIRIO)
Ana Cristina B. Garcia (UNIRIO)
Abstract
Artificial intelligence (AI) is being employed in many fields, including healthcare. While AI has the potential to improve people’s lives, it also raises ethical questions about fairness and bias. This article reviews the challenges and proposed solutions for promoting fairness in medical decisions aided by AI algorithms. A systematic mapping study was conducted, analyzing 37 articles on fairness in machine learning in healthcare from five sources: ACM Digital Library, IEEE Xplore, PubMed, ScienceDirect, and Scopus. The analysis reveals a growing interest in the field, with many recent publications. The study offers an up-to-date and comprehensive overview of approaches and limitations for evaluating and mitigating biases, unfairness, and discrimination in healthcare-focused machine learning algorithms. This study’s findings provide valuable insights for developing fairer, equitable, and more ethical AI systems for healthcare.
Keywords:
Fairness, Equity, Bias, Machine Learning, Healthcare.
