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

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Summary
This summary is machine-generated.

Digital ageism in machine learning (ML) models is a concern. This review identifies strategies to mitigate age-related bias in ML, focusing on data balancing, augmentation, and algorithmic modification to ensure fairness.

Keywords:
ageageingageismagingalgorithmalgorithmic biasartificial intelligencebiasdigital ageismelderelderlygeriatricgerontologymachine learningolder adultolder peopleolder personreview methodologyreview methodsscopingsearchsearchingsynthesis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Ethics

Background:

  • Digital ageism, or age-related bias, is prevalent in machine learning (ML) model development and deployment.
  • Existing research highlights the issue but lacks specific strategies and effectiveness analyses for mitigating age-related bias in ML.

Purpose of the Study:

  • To conduct a scoping review of strategies aimed at reducing age-related bias in ML models.
  • To address the gap in understanding mitigation techniques for digital ageism in AI.

Main Methods:

  • Adherence to the Arksey and O'Malley scoping review framework.
  • Comprehensive literature search across 6 major electronic databases and 2 gray literature databases.
  • Collaboration with an information specialist to refine search strategies.

Main Results:

  • Identification of 8 publications addressing age-related bias in ML.
  • Primary cause of bias identified as underrepresentation of older adults in datasets.
  • Mitigation strategies categorized into data balancing, data augmentation/supplementation, and algorithmic modification.

Conclusions:

  • Mitigating biases in ML is crucial for fairness, equity, and social good.
  • Highlights the need for continued research and development of effective strategies to combat digital ageism in ML.
  • Emphasizes ensuring ML systems serve the interests of all individuals.