Using Copies to Remove Sensitive Data: A Case Study on Fair Superhero Alignment Prediction

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4 Citations (Scopus)

Abstract

Ensuring classification models are fair with respect to sensitive data attributes is a crucial task when applying machine learning models to real-world problems. Particularly in company production environments, where the decision output by models may have a direct impact on individuals and predictive performance should be maintained over time. In this article, build upon [17], we propose copies as a technique to mitigate the bias of trained algorithms in circumstances where the original data is not accessible and/or the models cannot be re-trained. In particular, we explore a simple methodology to build copies that replicate the learned decision behavior in the absence of sensitive attributes. We validate this methodology in the low-sensitive problem of superhero alignment. We demonstrate that this naïve approach to bias reduction is feasible in this problem and argue that copies can be further exploited to embed models with desiderata such as fair learning.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 9th Iberian Conference, IbPRIA 2019, Proceedings
EditorsAythami Morales, Julian Fierrez, José Salvador Sánchez, Bernardete Ribeiro
PublisherSpringer
Pages182-193
Number of pages12
ISBN (Print)9783030313319
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019 - Madrid, Spain
Duration: 1 Jul 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11867 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019
Country/TerritorySpain
CityMadrid
Period1/07/194/07/19

Keywords

  • Bias reduction
  • Copying classifiers
  • Fairness
  • Superhero alignment

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