Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review
Charlene Chu,
Simon Donato-Woodger,
Shehroz S Khan
et al.
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.
Objective
To address this gap, we conducted a scoping review of mitigation strategi… Show more
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