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An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence

Meenu Chaudhary Orcid Logo, Loveleen Gaur, Amlan Chakrabarti, Gurmeet Singh, Paul Jones Orcid Logo, Sascha Kraus Orcid Logo

Journal of Innovation & Knowledge, Volume: 10, Issue: 3, Start page: 100700

Swansea University Author: Paul Jones Orcid Logo

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DOI (Published version): 10.1016/j.jik.2025.100700

Abstract

Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process of p...

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Published in: Journal of Innovation & Knowledge
ISSN: 2444-569X
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69312
first_indexed 2025-04-18T11:11:35Z
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spelling 2025-05-21T11:12:13.3585274 v2 69312 2025-04-18 An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence 21e2660aaa102fe36fc981880dd9e082 0000-0003-0417-9143 Paul Jones Paul Jones true false 2025-04-18 CBAE Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process of prediction is not known? Due to the lack of interpretability, ML models' exclusive nature and growing intricacy make it challenging for field experts to comprehend these multifaceted black boxes. To address the concern of interpretability, trust and transparency of black-box predictions, this study explores the application of explainable artificial intelligence (XAI) in identifying the factors that escalate the ECn, analysing the negative impact on productivity, employee morale and financial stability. We propose a predictive model that compares the best two top-performing algorithms based on the performance metrics. Thereafter, we suggest applying an explainable artificial intelligence based on Shapley values, i.e., the Shapley Additive exPlanations approach (SHAP), to identify and compare the feature importance of top-performing algorithms logistic regression and random forest analysis on our dataset. The interpretability of the predictive outcome unboxes the predictions, enhancing trust and facilitating retention strategies. Journal Article Journal of Innovation &amp; Knowledge 10 3 100700 Elsevier BV 2444-569X Explainable AI; Logistic regression; Random forest; Machine learning; Employee churn 1 5 2025 2025-05-01 10.1016/j.jik.2025.100700 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee This work was supported by the Open Access Publishing Fund provided by the Free University of Bozen-Bolzano. 2025-05-21T11:12:13.3585274 2025-04-18T12:09:35.8376613 Faculty of Humanities and Social Sciences School of Management - Business Management Meenu Chaudhary 0000-0003-3727-7460 1 Loveleen Gaur 2 Amlan Chakrabarti 3 Gurmeet Singh 4 Paul Jones 0000-0003-0417-9143 5 Sascha Kraus 0000-0003-4886-7482 6 69312__34326__31513ba2e12c4faaa21f712a43d75178.pdf 69312.VoR.pdf 2025-05-21T10:54:14.9949798 Output 3049093 application/pdf Version of Record true © 2025 The Authors. This is an open access article under the CC BY-NC-ND license. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
spellingShingle An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
Paul Jones
title_short An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
title_full An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
title_fullStr An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
title_full_unstemmed An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
title_sort An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence
author_id_str_mv 21e2660aaa102fe36fc981880dd9e082
author_id_fullname_str_mv 21e2660aaa102fe36fc981880dd9e082_***_Paul Jones
author Paul Jones
author2 Meenu Chaudhary
Loveleen Gaur
Amlan Chakrabarti
Gurmeet Singh
Paul Jones
Sascha Kraus
format Journal article
container_title Journal of Innovation &amp; Knowledge
container_volume 10
container_issue 3
container_start_page 100700
publishDate 2025
institution Swansea University
issn 2444-569X
doi_str_mv 10.1016/j.jik.2025.100700
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process of prediction is not known? Due to the lack of interpretability, ML models' exclusive nature and growing intricacy make it challenging for field experts to comprehend these multifaceted black boxes. To address the concern of interpretability, trust and transparency of black-box predictions, this study explores the application of explainable artificial intelligence (XAI) in identifying the factors that escalate the ECn, analysing the negative impact on productivity, employee morale and financial stability. We propose a predictive model that compares the best two top-performing algorithms based on the performance metrics. Thereafter, we suggest applying an explainable artificial intelligence based on Shapley values, i.e., the Shapley Additive exPlanations approach (SHAP), to identify and compare the feature importance of top-performing algorithms logistic regression and random forest analysis on our dataset. The interpretability of the predictive outcome unboxes the predictions, enhancing trust and facilitating retention strategies.
published_date 2025-05-01T14:15:28Z
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score 11.089677

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