This paper is in the following e-collection/theme issue:

Reviews on Aging (62) Involvement of Elderly Populations in Health and Health Services Research (60) Digital Health Reviews (1742) Machine Learning (2095) Artificial Intelligence (2162) Information and Patient Education on Healthy Aging (54)

Published on in Vol 7 (2024)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53564, first published .
Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review

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

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

Authors of this article:

Charlene Chu1, 2, 3, 4 Author Orcid Image ;   Simon Donato-Woodger1 Author Orcid Image ;   Shehroz S Khan2, 5 Author Orcid Image ;   Tianyu Shi1, 6 Author Orcid Image ;   Kathleen Leslie7 Author Orcid Image ;   Samira Abbasgholizadeh-Rahimi8 Author Orcid Image ;   Rune Nyrup9 Author Orcid Image ;   Amanda Grenier10 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

Journals

  1. Khan S, Shi T, Donato-Woodger S, Chu C. Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning. Algorithms 2025;18(2):55 View
  2. Ho J, Hartanto A, Koh A, Majeed N. Gender biases within Artificial Intelligence and ChatGPT: Evidence, Sources of Biases and Solutions. Computers in Human Behavior: Artificial Humans 2025;4:100145 View
  3. Amundsen D. Breaking Bias: Addressing Ageism in Artificial Intelligence. Journal of Ageing and Longevity 2025;5(3):36 View
  4. Ammar S, Triki N, Karray M, Ksantini M. A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain–Computer Interfaces. Sensors 2025;25(24):7426 View

Conference Proceedings

  1. Mani G. 2025 IEEE Evolution - Life Members Conference. A Framework for Implementing Co-Conceptual Model of Products and Services for the Aged Community View