TY  - JOUR
AU  - Chu, Charlene
AU  - Donato-Woodger, Simon
AU  - Khan, Shehroz S
AU  - Shi, Tianyu
AU  - Leslie, Kathleen
AU  - Abbasgholizadeh-Rahimi, Samira
AU  - Nyrup, Rune
AU  - Grenier, Amanda
PY  - 2024
DA  - 2024/3/22
TI  - Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review
JO  - JMIR Aging
SP  - e53564
VL  - 7
KW  - age
KW  - ageing
KW  - ageism
KW  - aging
KW  - algorithm
KW  - algorithmic bias
KW  - artificial intelligence
KW  - bias
KW  - digital ageism
KW  - elder
KW  - elderly
KW  - geriatric
KW  - gerontology
KW  - machine learning
KW  - older adult
KW  - older people
KW  - older person
KW  - review methodology
KW  - review methods
KW  - scoping
KW  - search
KW  - searching
KW  - synthesis
AB  - 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. Objective: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. 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). 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. 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. Trial Registration: Open Science Framework AMG5P; https://osf.io/amg5p 
SN  - 2561-7605
UR  - https://aging.jmir.org/2024/1/e53564
UR  - https://doi.org/10.2196/53564
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38517459
DO  - 10.2196/53564
ID  - info:doi/10.2196/53564
ER  -