Inherent Limitations of AI Fairness

TL;DR

This paper critically examines the inherent limitations of current AI fairness approaches, highlighting their unrealistic expectations and suggesting the need for non-AI solutions to support fair decision-making.

Contribution

It provides a comprehensive survey of criticisms and inherent limitations of AI fairness paradigms, offering a nuanced perspective and identifying opportunities for non-AI interventions.

Findings

Technical solutions have significant limitations in achieving true fairness.

Current AI fairness approaches can be misleading and unrealistic.

Non-AI solutions may be necessary to support fair decision processes.

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.