# Matchings with Group Fairness Constraints: Online and Offline Algorithms

@article{Sankar2021MatchingsWG, title={Matchings with Group Fairness Constraints: Online and Offline Algorithms}, author={Govind S. Sankar and Anand Louis and Meghana Nasre and Prajakta Nimbhorkar}, journal={ArXiv}, year={2021}, volume={abs/2105.09522} }

We consider the problem of assigning items to platforms in the presence of group fairness constraints. In the input, each item belongs to certain categories, called classes in this paper. Each platform specifies the group fairness constraints through an upper bound on the number of items it can serve from each class. Additionally, each platform also has an upper bound on the total number of items it can serve. The goal is to assign items to platforms so as to maximize the number of items…

## 7 Citations

### Bipartite Matchings with Group Fairness and Individual Fairness Constraints

- Computer ScienceArXiv
- 2022

This work addresses group as well as individual fairness constraints in matchings in the context of assigning items to platforms by providing a polynomial-time algorithm that computes a probabilistic individually fair distribution over group fair matchings.

### Matchings Under Diversity Constraints

- Computer ScienceArXiv
- 2022

The technical core of the proofs is a new connection between these problems and the problem of matchings in hypergraphs, which addresses a logistical challenge involving opening platforms in the presence of diversity constraints.

### Class Fairness in Online Matching

- Computer ScienceArXiv
- 2022

This work initiates the study of class fairness in this setting, where agents are partitioned into a set of classes and the matching is required to be fair with respect to the classes.

### Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

- Computer ScienceAAMAS
- 2022

This paper generalizes the existing work to offer fair treatment guarantees to both sides of the market simultaneously, at a calculated worst case drop to operator profit, and considers group and individual Rawlsian fairness criteria.

### Measuring Fairness under Unawareness of Sensitive Attributes: A Quantification-Based Approach

- Computer Science
- 2021

This work tackles the problem of measuring group fairness under unawareness of sensitive attributes, by using techniques from quantification, a supervised learning task concerned with directly providing group-level prevalence estimates (rather than individual-level class labels), and shows that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.

### Fairness Maximization among Offline Agents in Online-Matching Markets

- Computer ScienceWINE
- 2021

This paper proposes online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs, and presents two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness maximizations (GFM), respectively.

### Group Fairness for Knapsack Problems

- Computer Science, BusinessAAMAS
- 2021

The fair knapsack problems encompass various important problems, such as participatory budgeting, fair budget allocation, advertising, and the total number of items from each category.

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