Inherent Limitations of AI Fairness

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.

Accepted for publication at the Communications of the ACM

References in corpus (14)

• Equality of Opportunity in Supervised Learning
• Improving fairness in machine learning systems: What do industry practitioners need?
• 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
• Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices
• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• A Convex Framework for Fair Regression
• Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
• Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics
• Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis
• Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
• Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
• "What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
• Subverting machines, fluctuating identities: Re-learning human categorization
• The Sanction of Authority: Promoting Public Trust in AI