Inherent Limitations of AI Fairness

• URL: http://arxiv.org/abs/2212.06495v2
• Date: Fri, 9 Jun 2023 09:41:55 GMT
• Title: Inherent Limitations of AI Fairness
• Authors: Maarten Buyl, Tijl De Bie
• Abstract summary: The study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful.
• Score: 16.588468396705366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In response, the study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful. 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 the background necessary to form a nuanced opinion on developments in 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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