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

Chaudhary, Meenu and Gaur, Loveleen and Chakrabarti, Amlan and Singh, Gurmeet and Jones, Paul and Kraus, Sascha (2025) An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence. Journal of Innovation & Knowledge, 10 (3). NA. ISSN 2530-7614

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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 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.

Item Type:	Journal Article
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory
Divisions:	School of Business and Management (SBM)
Depositing User:	Gurmeet Singh
Date Deposited:	24 Apr 2025 01:21
Last Modified:	24 Apr 2025 01:21
URI:	https://repository.usp.ac.fj/id/eprint/14936

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