Algorithm Design: A Fairness-Accuracy Frontier

• 成果类型: Article
• 署名作者: Liang, Annie; Lu, Jay; Mu, Xiaosheng; Okumura, Kyohei
• 署名单位: Northwestern University; University of California System; University of California Los Angeles; Princeton University
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/739826
• 发表日期: 2026
• 关键词: AFFIRMATIVE-ACTION; racial-discrimination; INFORMATION; welfare; BIAS; RACE; preferences; EFFICIENCY; utility; equity

摘要

Algorithm designers increasingly care not only about accuracy but also about fairness across predefined groups. We study the trade-off between these objectives and characterize it by a fairness-accuracy frontier: the set of outcomes that cannot be simultaneously improved in both dimensions. The shape of this frontier is governed by a simple property of the inputs, which we call group skew. In particular, reducing accuracy for both groups to increase fairness is justified if and only if inputs are group skewed. We also study an information design problem in which a designer regulates inputs but another agent chooses the algorithm. We show that, when inputs are not group-skewed, banning group identity or other informative inputs is strictly suboptimal.