Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2022

Abstract

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the method based on how the neurons and attributes responsible for unfairness are distributed among the input attributes and the hidden neurons. Our experimental evaluation shows that our approach is effective (i.e., always identify the best fairness improving method) and efficient (i.e., with an average time overhead of 5 minutes).

Keywords

Fairness, Machine Learning, Fairness Improvement, Causality Analysis

Discipline

Software Engineering

Research Areas

Information Systems and Management

Publication

Proceedings of ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2022, Singapore, November 14-16

First Page

1

Last Page

12

Identifier

10.1145/3540250.3549103

Publisher

ACM

City or Country

Singapore

Additional URL

http://doi.org/10.1145/3540250.3549103

Download

Share

COinS