A domain-specific language for describing machine learning datasets

Datasets are essential for training and evaluating machine learning (ML) models. However, they are also at the root of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing a data-centric cultural shift, where data issues are...

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Detalles Bibliográficos
Autores: Giner Miguelez, Joan, Gómez, Abel, Cabot, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/149162
Acceso en línea:http://hdl.handle.net/10609/149162
https://doi.org/10.1016/j.cola.2023.101209
Access Level:acceso abierto
Palabra clave:Datasets
machine learning
MDE
Domain-specific languages
fairness
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spelling A domain-specific language for describing machine learning datasetsGiner Miguelez, JoanGómez, AbelCabot, JordiDatasetsmachine learningMDEDomain-specific languagesfairnessDatasets are essential for training and evaluating machine learning (ML) models. However, they are also at the root of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing a data-centric cultural shift, where data issues are given the attention they deserve and more standard practices for gathering and describing datasets are discussed and established. So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets. In this sense, and inspired by these proposals, we define a new domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, provenance, and social concerns. We believe this DSL will facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The DSL is implemented as a Visual Studio Code plugin, and it has been published under an open-source license.Elsevier202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/149162https://doi.org/10.1016/j.cola.2023.101209reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésJournal of Computer Languages, 2023, 76, 1-16.https://doi.org/10.1016/j.cola.2023.101209CC BYhttp://creativecommons.org/licenses/by/4.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1491622026-05-28T12:42:01Z
dc.title.none.fl_str_mv A domain-specific language for describing machine learning datasets
title A domain-specific language for describing machine learning datasets
spellingShingle A domain-specific language for describing machine learning datasets
Giner Miguelez, Joan
Datasets
machine learning
MDE
Domain-specific languages
fairness
title_short A domain-specific language for describing machine learning datasets
title_full A domain-specific language for describing machine learning datasets
title_fullStr A domain-specific language for describing machine learning datasets
title_full_unstemmed A domain-specific language for describing machine learning datasets
title_sort A domain-specific language for describing machine learning datasets
dc.creator.none.fl_str_mv Giner Miguelez, Joan
Gómez, Abel
Cabot, Jordi
author Giner Miguelez, Joan
author_facet Giner Miguelez, Joan
Gómez, Abel
Cabot, Jordi
author_role author
author2 Gómez, Abel
Cabot, Jordi
author2_role author
author
dc.subject.none.fl_str_mv Datasets
machine learning
MDE
Domain-specific languages
fairness
topic Datasets
machine learning
MDE
Domain-specific languages
fairness
description Datasets are essential for training and evaluating machine learning (ML) models. However, they are also at the root of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing a data-centric cultural shift, where data issues are given the attention they deserve and more standard practices for gathering and describing datasets are discussed and established. So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets. In this sense, and inspired by these proposals, we define a new domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, provenance, and social concerns. We believe this DSL will facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The DSL is implemented as a Visual Studio Code plugin, and it has been published under an open-source license.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
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 http://hdl.handle.net/10609/149162
https://doi.org/10.1016/j.cola.2023.101209
url http://hdl.handle.net/10609/149162
https://doi.org/10.1016/j.cola.2023.101209
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Computer Languages, 2023, 76, 1-16.
https://doi.org/10.1016/j.cola.2023.101209
dc.rights.none.fl_str_mv CC BY
http://creativecommons.org/licenses/by/4.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY
http://creativecommons.org/licenses/by/4.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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