Risk mitigation in algorithmic accountability: The role of machine learning copies

Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficienc...

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Authors: Unceta, Irene, Nin, Jordi, Pujol Vila, Oriol
Format: article
Status:Published version
Publication Date:2020
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/172487
Online Access:https://hdl.handle.net/2445/172487
Access Level:Open access
Keyword:Aprenentatge automàtic
Algorismes
Eficiència industrial
Machine learning
Algorithms
Industrial efficiency
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spelling Risk mitigation in algorithmic accountability: The role of machine learning copiesUnceta, IreneNin, JordiPujol Vila, OriolAprenentatge automàticAlgorismesEficiència industrialMachine learningAlgorithmsIndustrial efficiencyMachine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficiency and improve any decision-making process, and of course, spawn the creation of new products and services by using complex machine learning algorithms. In this scenario, the lack of actionable accountability-related guidance is potentially the single most important challenge facing the machine learning community. Machine learning systems are often composed of many parts and ingredients, mixing third party components or software-as-a-service APIs, among others. In this paper we study the role of copies for risk mitigation in such machine learning systems. Formally, a copy can be regarded as an approximated projection operator of a model into a target model hypothesis set. Under the conceptual framework of actionable accountability, we explore the use of copies as a viable alternative in circumstances where models cannot be re-trained, nor enhanced by means of a wrapper. We use a real residential mortgage default dataset as a use case to illustrate the feasibility of this approach.Public Library of Science (PLoS)2020202020202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion26 p.application/pdfhttps://hdl.handle.net/2445/172487Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1371/journal.pone.0241286PLoS One, 2020, num. 0241286https://doi.org/10.1371/journal.pone.0241286cc-by (c) Unceta, Irene et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1724872026-05-29T05:05:01Z
dc.title.none.fl_str_mv Risk mitigation in algorithmic accountability: The role of machine learning copies
title Risk mitigation in algorithmic accountability: The role of machine learning copies
spellingShingle Risk mitigation in algorithmic accountability: The role of machine learning copies
Unceta, Irene
Aprenentatge automàtic
Algorismes
Eficiència industrial
Machine learning
Algorithms
Industrial efficiency
title_short Risk mitigation in algorithmic accountability: The role of machine learning copies
title_full Risk mitigation in algorithmic accountability: The role of machine learning copies
title_fullStr Risk mitigation in algorithmic accountability: The role of machine learning copies
title_full_unstemmed Risk mitigation in algorithmic accountability: The role of machine learning copies
title_sort Risk mitigation in algorithmic accountability: The role of machine learning copies
dc.creator.none.fl_str_mv Unceta, Irene
Nin, Jordi
Pujol Vila, Oriol
author Unceta, Irene
author_facet Unceta, Irene
Nin, Jordi
Pujol Vila, Oriol
author_role author
author2 Nin, Jordi
Pujol Vila, Oriol
author2_role author
author
dc.subject.none.fl_str_mv Aprenentatge automàtic
Algorismes
Eficiència industrial
Machine learning
Algorithms
Industrial efficiency
topic Aprenentatge automàtic
Algorismes
Eficiència industrial
Machine learning
Algorithms
Industrial efficiency
description Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficiency and improve any decision-making process, and of course, spawn the creation of new products and services by using complex machine learning algorithms. In this scenario, the lack of actionable accountability-related guidance is potentially the single most important challenge facing the machine learning community. Machine learning systems are often composed of many parts and ingredients, mixing third party components or software-as-a-service APIs, among others. In this paper we study the role of copies for risk mitigation in such machine learning systems. Formally, a copy can be regarded as an approximated projection operator of a model into a target model hypothesis set. Under the conceptual framework of actionable accountability, we explore the use of copies as a viable alternative in circumstances where models cannot be re-trained, nor enhanced by means of a wrapper. We use a real residential mortgage default dataset as a use case to illustrate the feasibility of this approach.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/172487
url https://hdl.handle.net/2445/172487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0241286
PLoS One, 2020, num. 0241286
https://doi.org/10.1371/journal.pone.0241286
dc.rights.none.fl_str_mv cc-by (c) Unceta, Irene et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Unceta, Irene et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 26 p.
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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