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Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review.

Authors :
Chu C
Donato-Woodger S
Khan SS
Shi T
Leslie K
Abbasgholizadeh-Rahimi S
Nyrup R
Grenier A
Source :
JMIR aging [JMIR Aging] 2024 Mar 22; Vol. 7, pp. e53564. Date of Electronic Publication: 2024 Mar 22.
Publication Year :
2024

Abstract

Background: Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies.<br />Objective: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML.<br />Methods: We followed a scoping review methodology framework developed by Arksey and O'Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report).<br />Results: We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result.<br />Conclusions: Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals.<br />Trial Registration: Open Science Framework AMG5P; https://osf.io/amg5p.<br /> (©Charlene Chu, Simon Donato-Woodger, Shehroz S Khan, Tianyu Shi, Kathleen Leslie, Samira Abbasgholizadeh-Rahimi, Rune Nyrup, Amanda Grenier. Originally published in JMIR Aging (https://aging.jmir.org), 22.03.2024.)

Details

Language :
English
ISSN :
2561-7605
Volume :
7
Database :
MEDLINE
Journal :
JMIR aging
Publication Type :
Academic Journal
Accession number :
38517459
Full Text :
https://doi.org/10.2196/53564