AI Fairness for People with Disabilities: Point of View
This work addresses fairness issues in AI for people with disabilities, an important but often overlooked domain, though it is incremental as it builds on existing fairness frameworks without introducing new methods or data.
The paper examines how AI and machine learning impact fair treatment for people with disabilities, highlighting that fairness in this context differs from other protected attributes due to the diversity of disabilities and sensitivity of disability information. It explores existing fairness definitions and proposes approaches to address these challenges in AI applications.
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.