Exploring Relations among Fairness Notions in Discrete Fair Division
Jugal Garg, Eklavya Sharma

TL;DR
This paper systematically analyzes 22 fairness notions in discrete fair division, establishing a hierarchy of implications among them across various settings, and introduces an inference engine tool for automated reasoning.
Contribution
It provides a near-complete hierarchy of fairness notions, many new implications, and a novel inference engine tool for automated analysis in fair division.
Findings
Most implication relationships are characterized, with many new results.
The hierarchy clarifies the relative strength of fairness notions.
An inference engine automates the reasoning process.
Abstract
Fair allocation of indivisible items among agents is a fundamental and extensively studied problem. However, fairness does not have a single universally accepted definition, leading to a variety of competing fairness notions. Some of these notions are considered stronger or more desirable, but they are also more difficult to guarantee. In this work, we examine 22 different notions of fairness and organize them into a hierarchy. Formally, we say that a fairness notion implies another notion if every -fair allocation is also -fair. We give a near-complete picture of implications among fairness notions: for almost every pair of notions, we either prove an implication or give a counterexample demonstrating that the implication does not hold. Although some of these results are already known, many are new. We examine multiple settings, including the allocation of goods,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEthics and Social Impacts of AI · Digitalization, Law, and Regulation · Privacy, Security, and Data Protection
