Inherent Limitations of AI Fairness

by   Maarten Buyl, et al.
Ghent University

As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In particular, the study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Though many technical solutions for measuring and achieving AI fairness have been proposed, their model of AI fairness has been widely criticized in recent years for being misleading and unrealistic. In our paper, we survey these criticisms of AI fairness and identify key limitations that are inherent to the prototypical paradigm of AI fairness. By carefully outlining the extent to which technical solutions can realistically help in achieving AI fairness, we aim to provide readers with the background necessary to form a nuanced opinion on developments in the field of fair AI. This delineation also provides research opportunities for non-AI solutions peripheral to AI systems in supporting fair decision processes.


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