Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making

[EN] Employee attrition and high turnover have become critical challenges faced by various sectors in today's competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effe...

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Detalhes bibliográficos
Autores: Marín, Gabriel, Galán, Jose Javier, Galdon-Salvador, Jose-Luis|||0009-0004-2911-4308
Tipo de documento: artigo
Data de publicação:2023
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/212976
Acesso em linha:https://riunet.upv.es/handle/10251/212976
Access Level:Acceso aberto
Palavra-chave:Explainable AI (XAI)
Interpretability
Decision making
Employee attrition
Machine learning
Human resources
ORGANIZACION DE EMPRESAS
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spelling Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-MakingMarín, GabrielGalán, Jose JavierGaldon-Salvador, Jose-Luis|||0009-0004-2911-4308Explainable AI (XAI)InterpretabilityDecision makingEmployee attritionMachine learningHuman resourcesORGANIZACION DE EMPRESAS[EN] Employee attrition and high turnover have become critical challenges faced by various sectors in today's competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model's predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs.MDPI AGDepartamento de Organización de EmpresasEscuela Técnica Superior de Ingeniería InformáticaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-11-17journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/212976reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2129762026-06-13T07:49:27Z
dc.title.none.fl_str_mv Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
title Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
spellingShingle Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
Marín, Gabriel
Explainable AI (XAI)
Interpretability
Decision making
Employee attrition
Machine learning
Human resources
ORGANIZACION DE EMPRESAS
title_short Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
title_full Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
title_fullStr Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
title_full_unstemmed Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
title_sort Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
dc.creator.none.fl_str_mv Marín, Gabriel
Galán, Jose Javier
Galdon-Salvador, Jose-Luis|||0009-0004-2911-4308
author Marín, Gabriel
author_facet Marín, Gabriel
Galán, Jose Javier
Galdon-Salvador, Jose-Luis|||0009-0004-2911-4308
author_role author
author2 Galán, Jose Javier
Galdon-Salvador, Jose-Luis|||0009-0004-2911-4308
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Organización de Empresas
Escuela Técnica Superior de Ingeniería Informática
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Explainable AI (XAI)
Interpretability
Decision making
Employee attrition
Machine learning
Human resources
ORGANIZACION DE EMPRESAS
topic Explainable AI (XAI)
Interpretability
Decision making
Employee attrition
Machine learning
Human resources
ORGANIZACION DE EMPRESAS
description [EN] Employee attrition and high turnover have become critical challenges faced by various sectors in today's competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model's predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/212976
url https://riunet.upv.es/handle/10251/212976
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404477266591744
score 15.812429

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