FairNN: Conjoint learning of fair representations for fair decisions

Tongxin Hu, Vasileios Iosifidis, Wentong Liao*, Hang Zhang, Michael Ying Yang, Eirini Ntoutsi, Bodo Rosenhahn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing the reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularizer. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.

Original languageEnglish
Title of host publicationDiscovery Science
Subtitle of host publication 23rd International Conference, DS 2020, Proceedings
EditorsAnnalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin
PublisherSpringer
Pages581-595
Number of pages15
Volume12323
ISBN (Print)9783030615260
DOIs
Publication statusPublished - 15 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12323 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Auto-encoders
  • Bias
  • Fairness
  • Neural networks

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