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Age-related bias and artificial intelligence: a scoping review

Author

Listed:
  • Charlene H. Chu

    (University of Toronto
    University of Toronto
    Toronto Rehabilitation Institute—University Health Network
    University of Toronto)

  • Simon Donato-Woodger

    (University of Toronto)

  • Shehroz S. Khan

    (Toronto Rehabilitation Institute—University Health Network
    University of Toronto)

  • Rune Nyrup

    (University of Cambridge)

  • Kathleen Leslie

    (Faculty of Health Disciplines, Athabasca University)

  • Alexandra Lyn

    (MacEwan University)

  • Tianyu Shi

    (University of Toronto
    University of Toronto)

  • Andria Bianchi

    (Toronto Rehabilitation Institute—University Health Network
    Unity Health Toronto
    University of Toronto)

  • Samira Abbasgholizadeh Rahimi

    (McGill University
    Mila—Quebec AI Institute, Montreal
    Jewish General Hospital)

  • Amanda Grenier

    (University of Toronto
    University of Toronto
    Baycrest Hospital)

Abstract

There are widespread concerns about bias and discriminatory output related to artificial intelligence (AI), which may propagate social biases and disparities. Digital ageism refers to ageism reflected design, development, and implementation of AI systems and technologies and its resultant data. Currently, the prevalence of digital ageism and the sources of AI bias are unknown. A scoping review informed by the Arksey and O’Malley methodology was undertaken to explore age-related bias in AI systems, identify how AI systems encode, produce, or reinforce age-related bias, what is known about digital ageism, and the social, ethical and legal implications of age-related bias. A comprehensive search strategy that included five electronic bases and grey literature sources including legal sources was conducted. A framework of machine learning biases spanning from data to user by Mehrabi et al. is used to present the findings (Mehrabi et al. 2021). The academic search resulted in 7595 articles that were screened according to the inclusion criteria, of which 307 were included for full-text screening, and 49 were included in this review. The grey literature search resulted in 2639 documents screened, of which 235 were included for full text screening, and 25 were found to be relevant to the research questions pertaining to age and AI. As a result, a total of 74 documents were included in this review. The results show that the most common AI applications that intersected with age were age recognition and facial recognition systems. The most frequent machine learning algorithms used were convolutional neural networks and support vector machines. Bias was most frequently introduced in the early ‘data to algorithm’ phase in machine learning and the ‘algorithm to user’ phase specifically with representation bias (n = 33) and evaluation bias (n = 29), respectively (Mehrabi et al. 2021). The review concludes with a discussion of the ethical implications for the field of AI and recommendations for future research.

Suggested Citation

  • Charlene H. Chu & Simon Donato-Woodger & Shehroz S. Khan & Rune Nyrup & Kathleen Leslie & Alexandra Lyn & Tianyu Shi & Andria Bianchi & Samira Abbasgholizadeh Rahimi & Amanda Grenier, 2023. "Age-related bias and artificial intelligence: a scoping review," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01999-y
    DOI: 10.1057/s41599-023-01999-y
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    References listed on IDEAS

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    1. Dominique Guegan & Bertrand Hassani, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835213, HAL.
    2. Dale Dannefer, 2003. "Cumulative Advantage/Disadvantage and the Life Course: Cross-Fertilizing Age and Social Science Theory," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 58(6), pages 327-337.
    3. Dominique Guegan & Bertrand Hassani, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Post-Print halshs-01835213, HAL.
    4. Qingyu Zhao & Ehsan Adeli & Kilian M. Pohl, 2020. "Training confounder-free deep learning models for medical applications," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    5. Kieran Walsh & Thomas Scharf & Norah Keating, 2017. "Social exclusion of older persons: a scoping review and conceptual framework," European Journal of Ageing, Springer, vol. 14(1), pages 81-98, March.
    6. Stephane Helleringer & Chong You & Laurence Fleury & Laetitia Douillot & Insa Diouf & Cheikh Tidiane Ndiaye & Valerie Delaunay & Rene Vidal, 2019. "Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(9), pages 219-260.
    7. Helen Margetts & Cosmina Dorobantu, 2019. "Rethink government with AI," Nature, Nature, vol. 568(7751), pages 163-165, April.
    8. James Zou & Londa Schiebinger, 2018. "AI can be sexist and racist — it’s time to make it fair," Nature, Nature, vol. 559(7714), pages 324-326, July.
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