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AI Fairness for People with Disabilities: Point of View
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We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods. We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race. One major difference is the extreme diversity of ways disabilities manifest, and people adapt. Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination. Given these differences, we explore definitions of fairness and how well they work in the disability space. Finally, we suggest ways of approaching fairness for people with disabilities in AI applications.
Forward citations
Cited by 2 Pith papers
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Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH
T2I models systematically depict disability via wheelchair and blindfold tropes with reduced diversity and SCM scores that mirror real-world high-warmth/low-competence stereotypes.
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AI and Accessibility: A Discussion of Ethical Considerations
A viewpoint article that outlines the ethical challenges of AI for people with disabilities and urges responsible development.
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