Algorithmic Fairness: A Tale of Two Approaches
This is a preliminary draft and may not have been subjected to the formal review process of the NBER. This page will be updated as the chapter is revised.
The growing use of artificial intelligence in high-stakes decision-making has raised important questions about how to address potential discriminatory outcomes. Two distinct approaches have emerged: one from computer science, which focuses on regulating algorithms directly, and another from economics, which emphasizes market design and incentives. This paper examines these competing frameworks and argues that the economic approach leads to superior welfare outcomes.