Log In Sign Up

Fair and Useful Cohort Selection

As important decisions about the distribution of society's resources become increasingly automated, it is essential to consider the measurement and enforcement of fairness in these decisions. In this work we build on the results of Dwork and Ilvento ITCS'19, which laid the foundations for the study of fair algorithms under composition. In particular, we study the cohort selection problem, where we wish to use a fair classifier to select k candidates from an arbitrarily ordered set of size n>k, while preserving individual fairness and maximizing utility. We define a linear utility function to measure performance relative to the behavior of the original classifier. We develop a fair, utility-optimal O(n)-time cohort selection algorithm for the offline setting, and our primary result, a solution to the problem in the streaming setting that keeps no more than O(k) pending candidates at all time.


page 1

page 2

page 3

page 4


Fair and Optimal Cohort Selection for Linear Utilities

The rise of algorithmic decision-making has created an explosion of rese...

A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form

In chaotic modern society, there is an increasing demand for the realiza...

Fairly Allocating Utility in Constrained Multiwinner Elections

Fairness in multiwinner elections is studied in varying contexts. For in...

Multi-Category Fairness in Sponsored Search Auctions

Fairness in advertising is a topic of particular concern motivated by th...

Leximax Approximations and Representative Cohort Selection

Finding a representative cohort from a broad pool of candidates is a goa...

Fair Allocation through Selective Information Acquisition

Public and private institutions must often allocate scare resources unde...

Learning Individually Fair Classifier with Causal-Effect Constraint

Machine learning is increasingly being used in various applications that...