Charlene Chu, Simon Donato-Woodger, Shehroz S Khan, Tianyu Shi, Kathleen Leslie, Samira Abbasgholizadeh-Rahimi, Rune Nyrup, Amanda Grenier
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...
March 22, 2024: JMIR aging