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

Charlene Chu   ^{1, 2, 3, 4 *} , BScN, GNC(c), PhD ;   Simon Donato-Woodger   ^{1 *} , BScN ;   Shehroz S Khan   ^{2, 5 *} , PhD ;   Tianyu Shi   ^{1, 6 *} , MSc ;   Kathleen Leslie   ⁷ , BScN, JD, PhD ;   Samira Abbasgholizadeh-Rahimi   ^{8 *} , BEng, PhD ;   Rune Nyrup   ^{9 *} , PhD ;   Amanda Grenier   ^{10 *} , PhD

¹ Lawrence Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada

² Knowledge, Innovation, Talent, Everywhere (KITE), Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

³ Institute for Life Course and Aging, Faculty of Social Work, University of Toronto, Toronto, ON, Canada

⁴ Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada

⁵ Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

⁶ Department of Civil Engineering, University of Toronto, Toronto, ON, Canada

⁷ Faculty of Health Disciplines, Athabasca University, Athabasca, AB, Canada

⁸ Department of Family Medicine, McGill University, Montreal, QC, Canada

⁹ Centre for Science Studies, Department of Mathematics, Aarhus University, Aarhus, Denmark

¹⁰ Factor-Inwentash Faculty of Social Work, University of Toronto and Baycrest Hospital, Toronto, ON, Canada

*these authors contributed equally