What is the Point of Fairness? Disability, AI and The Complexity of Justice
It highlights a critical gap in AI ethics for disabled populations, urging a shift from fairness to justice to address structural injustices.
The paper critiques the use of a singular 'fairness' framework in AI ethics for disability, arguing it can reinforce power dynamics and harm marginalized disabled people, based on two computer vision case studies.
Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analyzing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.