Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health

Fletcher, Richard Ribón and Nakeshimana, Audace and Olubeko, Olusubomi (2021) Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Frontiers in Artificial Intelligence, 3. ISSN 2624-8212

[thumbnail of pubmed-zip/versions/4/package-entries/frai-03-561802.pdf] Text
pubmed-zip/versions/4/package-entries/frai-03-561802.pdf - Published Version

Download (2MB)

Abstract

In Low- and Middle- Income Countries (LMICs), machine learning (ML) and artificial intelligence (AI) offer attractive solutions to address the shortage of health care resources and improve the capacity of the local health care infrastructure. However, AI and ML should also be used cautiously, due to potential issues of fairness and algorithmic bias that may arise if not applied properly. Furthermore, populations in LMICs can be particularly vulnerable to bias and fairness in AI algorithms, due to a lack of technical capacity, existing social bias against minority groups, and a lack of legal protections. In order to address the need for better guidance within the context of global health, we describe three basic criteria (Appropriateness, Fairness, and Bias) that can be used to help evaluate the use of machine learning and AI systems: 1) APPROPRIATENESS is the process of deciding how the algorithm should be used in the local context, and properly matching the machine learning model to the target population; 2) BIAS is a systematic tendency in a model to favor one demographic group vs another, which can be mitigated but can lead to unfairness; and 3) FAIRNESS involves examining the impact on various demographic groups and choosing one of several mathematical definitions of group fairness that will adequately satisfy the desired set of legal, cultural, and ethical requirements. Finally, we illustrate how these principles can be applied using a case study of machine learning applied to the diagnosis and screening of pulmonary disease in Pune, India. We hope that these methods and principles can help guide researchers and organizations working in global health who are considering the use of machine learning and artificial intelligence.

Item Type: Article
Subjects: EP Archives > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 03 Mar 2023 06:10
Last Modified: 25 May 2024 07:37
URI: http://research.send4journal.com/id/eprint/1105

Actions (login required)

View Item
View Item