Pushing the Limits of Fairness in Algorithmic Decision-Making

被引：0

作者：

Shah, Nisarg ^{[1
]}

机构：

[1] Univ Toronto, Toronto, ON, Canada

来源：

PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023 | 2023年

基金：

加拿大自然科学与工程研究理事会;

关键词：

DIVISION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Designing provably fair decision-making algorithms is a task of growing interest and importance. In this article, I argue that preference-based notions of fairness proposed decades ago in the economics literature and subsequently explored in-depth within computer science (specifically, within the field of computational social choice) are aptly suited for a wide range of modern decision-making systems, from conference peer review to recommender systems to participatory budgeting.

引用

页码：7051 / 7056

页数：6

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