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Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Author

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  • Aspen Lillywhite

    (Community Rehabilitation and Disability Studies, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N4N1, Canada)

  • Gregor Wolbring

    (Community Rehabilitation and Disability Studies, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N4N1, Canada)

Abstract

Artificial intelligence (AI) and machine learning (ML) advancements increasingly impact society and AI/ML ethics and governance discourses have emerged. Various countries have established AI/ML strategies. “AI for good” and “AI for social good” are just two discourses that focus on using AI/ML in a positive way. Disabled people are impacted by AI/ML in many ways such as potential therapeutic and non-therapeutic users of AI/ML advanced products and processes and by the changing societal parameters enabled by AI/ML advancements. They are impacted by AI/ML ethics and governance discussions and discussions around the use of AI/ML for good and social good. Using identity, role, and stakeholder theories as our lenses, the aim of our scoping review is to identify and analyze to what extent, and how, AI/ML focused academic literature, Canadian newspapers, and Twitter tweets engage with disabled people. Performing manifest coding of the presence of the terms “AI”, or “artificial intelligence” or “machine learning” in conjunction with the term “patient”, or “disabled people” or “people with disabilities” we found that the term “patient” was used 20 times more than the terms “disabled people” and “people with disabilities” together to identify disabled people within the AI/ML literature covered. As to the downloaded 1540 academic abstracts, 234 full-text Canadian English language newspaper articles and 2879 tweets containing at least one of 58 terms used to depict disabled people (excluding the term patient) and the three AI terms, we found that health was one major focus, that the social good/for good discourse was not mentioned in relation to disabled people, that the tone of AI/ML coverage was mostly techno-optimistic and that disabled people were mostly engaged with in their role of being therapeutic or non-therapeutic users of AI/ML influenced products. Problems with AI/ML were mentioned in relation to the user having a bodily problem, the usability of AI/ML influenced technologies, and problems disabled people face accessing such technologies. Problems caused for disabled people by AI/ML advancements, such as changing occupational landscapes, were not mentioned. Disabled people were not covered as knowledge producers or influencers of AI/ML discourses including AI/ML governance and ethics discourses. Our findings suggest that AI/ML coverage must change, if disabled people are to become meaningful contributors to, and beneficiaries of, discussions around AI/ML.

Suggested Citation

  • Aspen Lillywhite & Gregor Wolbring, 2020. "Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People," Societies, MDPI, vol. 10(1), pages 1-27, February.
    • Handle: RePEc:gam:jsoctx:v:10:y:2020:i:1:p:23-:d:325716
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    References listed on IDEAS

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    1. Gregor Wolbring, 2016. "Employment, Disabled People and Robots: What Is the Narrative in the Academic Literature and Canadian Newspapers?," Societies, MDPI, vol. 6(2), pages 1-16, April.
    2. Gregor Wolbring & Rachel Mackay & Theresa Rybchinski & Jacqueline Noga, 2013. "Disabled People and the Post-2015 Development Goal Agenda through a Disability Studies Lens," Sustainability, MDPI, vol. 5(10), pages 1-31, September.
    3. Andrew Crane & Trish Ruebottom, 2011. "Stakeholder Theory and Social Identity: Rethinking Stakeholder Identification," Journal of Business Ethics, Springer, vol. 102(1), pages 77-87, March.
    4. Cinzia Castiglioni & Edoardo Lozza & Andrea Bonanomi, 2019. "The Common Good Provision Scale (CGP): A Tool for Assessing People’s Orientation towards Economic and Social Sustainability," Sustainability, MDPI, vol. 11(2), pages 1-14, January.
    5. Aaron Ahuvia, 2001. "Traditional, Interpretive, and Reception Based Content Analyses: Improving the Ability of Content Analysis to Address Issues of Pragmatic and Theoretical Concern," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 54(2), pages 139-172, May.
    6. Cullinane, Kevin & Toy, Neal, 2000. "Identifying influential attributes in freight route/mode choice decisions: a content analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(1), pages 41-53, March.
    7. Gregor Wolbring & Lucy Diep, 2016. "The Discussions around Precision Genetic Engineering: Role of and Impact on Disabled People," Laws, MDPI, vol. 5(3), pages 1-23, September.
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    Cited by:

    1. Braganza, Ashley & Chen, Weifeng & Canhoto, Ana & Sap, Serap, 2021. "Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust," Journal of Business Research, Elsevier, vol. 131(C), pages 485-494.
    2. Gregor Wolbring & Aspen Lillywhite, 2023. "Coverage of Allies, Allyship and Disabled People: A Scoping Review," Societies, MDPI, vol. 13(11), pages 1-38, November.
    3. Gregor Wolbring, 2022. "Auditing the ‘Social’ of Quantum Technologies: A Scoping Review," Societies, MDPI, vol. 12(2), pages 1-38, March.
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    6. Gregor Wolbring & Laiba Nasir, 2024. "Intersectionality of Disabled People through a Disability Studies, Ability-Based Studies, and Intersectional Pedagogy Lens: A Survey and a Scoping Review," Societies, MDPI, vol. 14(9), pages 1-77, September.

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