Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review (Preprint)

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.

To address this gap, we conducted a scoping review of …