Cargando…

A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network reg...

Descripción completa

Detalles Bibliográficos
Autores principales: Fitzsimons, Jack, Al Ali, AbdulRahman, Osborne, Michael, Roberts, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515270/
https://www.ncbi.nlm.nih.gov/pubmed/33267455
http://dx.doi.org/10.3390/e21080741
_version_ 1783586778492436480
author Fitzsimons, Jack
Al Ali, AbdulRahman
Osborne, Michael
Roberts, Stephen
author_facet Fitzsimons, Jack
Al Ali, AbdulRahman
Osborne, Michael
Roberts, Stephen
author_sort Fitzsimons, Jack
collection PubMed
description Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly binds the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.
format Online
Article
Text
id pubmed-7515270
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75152702020-11-09 A General Framework for Fair Regression Fitzsimons, Jack Al Ali, AbdulRahman Osborne, Michael Roberts, Stephen Entropy (Basel) Article Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly binds the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data. MDPI 2019-07-29 /pmc/articles/PMC7515270/ /pubmed/33267455 http://dx.doi.org/10.3390/e21080741 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fitzsimons, Jack
Al Ali, AbdulRahman
Osborne, Michael
Roberts, Stephen
A General Framework for Fair Regression
title A General Framework for Fair Regression
title_full A General Framework for Fair Regression
title_fullStr A General Framework for Fair Regression
title_full_unstemmed A General Framework for Fair Regression
title_short A General Framework for Fair Regression
title_sort general framework for fair regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515270/
https://www.ncbi.nlm.nih.gov/pubmed/33267455
http://dx.doi.org/10.3390/e21080741
work_keys_str_mv AT fitzsimonsjack ageneralframeworkforfairregression
AT alaliabdulrahman ageneralframeworkforfairregression
AT osbornemichael ageneralframeworkforfairregression
AT robertsstephen ageneralframeworkforfairregression
AT fitzsimonsjack generalframeworkforfairregression
AT alaliabdulrahman generalframeworkforfairregression
AT osbornemichael generalframeworkforfairregression
AT robertsstephen generalframeworkforfairregression