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

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
  2. Khan S, Shi T, Chu C, Moustafa N, Ashraf A. 2025 IEEE International Conference on Emerging Trends in Engineering and Computing (ETECOM). Ensemble of Regressors to Handle Bias in Predicting Age from Facial Images View