Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

Chowdhury, Soumyadeb; Joel-Edgar, Sian; Dey, Prasanta Kumar; Bhattacharya, Sudeshna; Kharlamov, Alexander

doi:10.1080/09585192.2022.2066981

Cited by 140AI and HR TechnologiesExplainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

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Chowdhury, Soumyadeb, Joel-Edgar, Sian, Dey, Prasanta Kumar, Bhattacharya, Sudeshna, Kharlamov, Alexander·2023·The International Journal of Human Resource Management

DOI 10.1080/09585192.2022.2066981

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Abstract

Employee turnover (ET) is a major issue faced by firms in all business sectors. Artificial intelligence (AI) machine learning (ML) prediction models can help to classify the likelihood of employees voluntarily departing from employment using historical employee datasets. However, output responses generated by these AI-based ML models lack transparency and interpretability, making it difficult for HR managers to understand the rationale behind the AI predictions. If managers do not understand how and why responses are generated by AI models based on the input datasets, it is unlikely to augment data-driven decision-making and bring value to the organisations. The main purpose of this article is to demonstrate the capability of Local Interpretable Model-Agnostic Explanations (LIME) technique to intuitively explain the ET predictions generated by AI-based ML models for a given employee dataset to HR managers. From a theoretical perspective, we contribute to the International Human Resource Management literature by presenting a conceptual review of AI algorithmic transparency and then discussing its significance to sustain competitive advantage by using the principles of resource-based view theory. We also offer a transparent AI implementation framework using LIME which will provide a useful guide for HR managers to increase the explainability of the AI-based ML models, and therefore mitigate trust issues in data-driven decision-making.

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Traditionally, output responses produced by complex AI algorithms are unexplainable, which makes the reliability and validity of these outputs untrustworthy.
“Traditionally, output responses produced by complex AI algorithms are unexplainable, which makes the reliability and validity of these outputs untrustworthy (Chowdhury et al., 2022).”
From Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT (2023) · cites this paper as Chowdhury et al., 2022

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Bottesch, S., Schwenke, C., Förster, M., & Klier, M. (2025) · Electronic Markets · doi:10.1007/s12525-025-00842-3 · Cited by 16 · see citing works
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Responsible AI and human collaboration in tourism management: Ethical considerations and identity disclosure
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Revolutionizing infectious disease surveillance: Multi-omics technologies and AI-driven integration
Aswini, R., Saranya, B., Gayathri, K., & Elumalai, K. (2025) · Decoding Infection and Transmission · doi:10.1016/j.dcit.2025.100061 · Cited by 8 · see citing works
The impact of AI on service employees: a systematic review
Cui, S., & Mao, Y. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2613648 · Cited by 11 · see citing works
“For example, when organizations employ AI for performance evaluations, the complexity and variability of the algorithms make it hard for employees to fully understand the logic behind evaluations (Chowdhury et al., 2023).”
Developing human resource analytics capability architecture in organizations
Shet, S. V. (2025) · Person-centered review · doi:10.1108/pr-01-2024-0009 · Cited by 10 · see citing works
Artificial intelligent technologies in Japanese manufacturing firms: an empirical survey study
Hadid, W., Horii, S., & Yokota, A. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2358409 · Cited by 13 · see citing works
“…empirical research on the impact of AI, its enablers and scale of implementation in practice (Brock and Von Wangenheim 2019; Chiarini 2021; Chowdhury et al. 2022;; Duan, Edwards, and Dwivedi 2019; Giermindl et al. 2022; Jaiswal, Arun, and Varma 2022; Johnson et al. 2022; Merhi and…”
Approach and avoidance mechanisms in the effects of algorithm transparency on the service performance of food-delivery riders
Liu, M., Liu, X., Li, L., Zhou, J., & Liu, S. (2024) · Current Issues in Tourism · doi:10.1080/13683500.2024.2376208 · Cited by 12 · see citing works
“When algorithm transparency is high, individuals can have a better understanding of the rules and criteria for algorithmic decisions (Chowdhury et al., 2022), fostering greater trust and willingness to collaborate (Grimmelikhuijsen, 2022; Shin, 2020).”
AI based smart business management and control analysis based decision making by machine learning model
Wu, M., & Qin, X. (2024) · Entertainment Computing · doi:10.1016/j.entcom.2024.100724 · Cited by 9
Paradoxes of implementing AI-augmented HRM in the workplace: A systematic literature review and future research agenda
Anshima, Bhardwaj, B., & Sharma, D. (2026) · Management Review Quarterly · doi:10.1007/s11301-025-00581-x · Cited by 7 · see citing works
Mapping the impacts of neural networks on human resource management research: a bibliometric analysis
Sakib, M. N., Jabber, M. A., Younus, M., Bithee, M. M., Sannyamat, M. M., Saha, A., & Guha, A. (2025) · Future Business Journal · doi:10.1186/s43093-025-00515-9 · Cited by 5 · see citing works
Navigating the Ethical Landscape: Considerations in Implementing AI-ML Systems in Human Resources
Basnet, S. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.0324.0755 · Cited by 9 · see citing works
Unlocking the Potential: Literature Review on the Evolving Role of AI in HRM
Du, J. (2024) · Frontiers in Management Science · doi:10.56397/fms.2024.02.05 · Cited by 6
“Existing research often lacks granularity in addressing how AI applications in HRM may vary based on organizational size, industry, or cultural factors (Chowdhury et al., 2022).”
Background
The Mediating Role of Work Engagement on The Relationship Between Job Security, Organizational Justice, Job Embeddedness and Turnover Intention of Pakistan's Textile Industry
Malik, A., Shahwar, M., Amin, M., & Shahid, M. N. (2023) · Current Trends in Law and Society · doi:10.52131/ctls.2023.0301.0012 · Cited by 7 · see citing works
Intelligent technology and enhanced well-being: can artificial intelligence mitigate digital overload?
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Can Legal and Professional Personnel Selection Principles be Met With Machine Learning (Artificial Intelligence)?
Campion, M. A. (2025) · Human Resource Management · doi:10.1002/hrm.70025 · Cited by 4 · see citing works
Yapay Zekâ Kaygısının Gelecek Kaygısı ve Yenilikçi Davranışlar Üzerindeki Etkisi: Üniversite Öğrencileri Üzerine Bir Uygulama
Ülkü, T., Özcan, S. U., & POLATÇI, S. (2025) · İş ve İnsan Dergisi · doi:10.18394/iid.1547768 · Cited by 4 · see citing works
Machine Learning Applications in Workforce Management: Strategies for Enhancing Productivity and Employee Engagement
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“Chowdhury et al. [17] attempts to highlight the contribution of the Local Interpretable Model-Agnostic Explanations (LIME) software package to AI-based ML model transparency.”
Explained, yet misunderstood: How AI Literacy shapes HR Managers' interpretation of User Interfaces in Recruiting Recommender Systems
Kalff, Y., & Simbeck, K. (2025) · HR@RecSys · doi:10.48550/arxiv.2509.06475 · Cited by 4
The Impact of AI Based Decision Making, Market Intelligence, and Chatbot Integration on Business Performance in Indonesian Start-ups
Hasya, A., & Sabri, A. Q. M. (2025) · JURNAL BISNIS STRATEGI · doi:10.14710/jbs.34.1.1-10 · Cited by 4
Human Resource Analytics: A Hybrid Review
Goswami, T. G., & Mansi (2025) · South Asian Journal of Human Resources Management · doi:10.1177/23220937251322236 · Cited by 4 · see citing works
Transforming talent acquisition for sustainable HRM: a structuration perspective
Dutta, D., Althaf, A., & Topno, J. S. (2025) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-08-2024-0401 · Cited by 3 · see citing works
The Art of Retention: Advancing Sustainable Management Through Age-Diverse Turnover Modeling
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“Exploratory Data Analysis: A comprehensive correlation analysis was conducted to understand the relationships between variables [29].”
The Digital Edge: A Systematic Review of AI-Driven Competitive Advantage
Condeço, D., Ferreira, J. J., & Zhang, Z. (2026) · Journal of Computer Information Systems · doi:10.1080/08874417.2026.2615088 · Cited by 3 · see citing works
The impacts of machine learning on human resource management: a systematic literature review and bibliometric analysis
Sakib, N., & Islam, S. (2026) · Future Business Journal · doi:10.1186/s43093-025-00704-6 · Cited by 1 · see citing works
AI-Driven Talent Acquisition: Predicting Employee Success in Indian Public Sector Recruitment
Karthik, M., Malhotra, P., Rautrao, R. R., Manikandan, G., Indrapriya, M., & Tiwari, M. (2025) · International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) · doi:10.1109/icicke65317.2025.11136259 · Cited by 2 · see citing works
“An examination of how the suggested AI system performs within current recruitment methods should be conducted to establish its beneficial results [10].”
The Impact of Artificial Intelligence and Robotic Process Automation on Accounting Performance and Employee Satisfaction in Financial Services in Indonesia
Judijanto, L., Zaman, D., Louisther, H., Ghurabillah, G., & Hasibuan, R. (2025) · The Es Accounting And Finance · doi:10.58812/esaf.v3i02.509 · Cited by 3
Pengembangan Sistem Pendukung Keputusan Cerdas untuk Seleksi Karyawan Berbasis Multi-Kriteria dan Machine Learning
Wibowo, B. D. P., & Anggraini, S. N. P. (2026) · JINTEN: Journal of Intelligent Systems and Digital Science · doi:10.66031/jinten.v1i1.502 · Cited by 2
“Chowdhury et al. (2023) menjelaskan bahwa transparansi menjadi salah satu tantangan utama dalam penerapan kecerdasan buatan pada manajemen sumber daya manusia karena keputusan yang dihasilkan sering kali sulit dipahami oleh pengguna.”
Explainable AI for Employee Retention in Green Human Resource Management: Integrating Prediction, Interpretation, and Policy Simulation
Nguyen, D. C., Tenney, D., & Kongar, E. A. (2026) · Sustainability · doi:10.3390/su18062740 · Cited by 4 · see citing works
The adoption of human resources analytics in construction projects in Jordan: antecedents and consequences
Abujraiban, A., Assaf, G., & Hmoud, A. Y. R. (2025) · Journal of Management Analytics · doi:10.1080/23270012.2025.2455550 · Cited by 2 · see citing works
The Effect of Work Environment, Organizational Justice, and Job Embeddedness on Turnover Intention of Pakistan's Textile Production Employees: The Mediating Role of Work Engagement
Amin, M., Shahwar, M., Hussain, Z., et al. (2024) · Pakistan journal of humanities and social sciences · doi:10.52131/pjhss.2024.v12i3.2404 · Cited by 2
Application of Machine Learning in Human Resources Management: Research on Employee Performance Prediction Model
Liu, Y. (2024) · International Conference Data Science and Management · doi:10.1145/3686081.3686102 · Cited by 2
“For example, in the recruitment process, ML can predict the job performance of candidates by analyzing resume data; in terms of employee retention, ML models can identify employees with a high risk of leaving and take measures in advance [9].”
A Management Control oriented Governance Framework for Artificial Intelligence
Sentinella, R., Schnegg, M., & Möller, K. (2023) · Die Unternehmung · doi:10.5771/0042-059x-2023-2-162 · Cited by 2
“Researchers showed that increasing transparency in AI models mitigated trust issues and increased adoption (Chowdhury et al., 2022).”
Background
AI-enabled sustainable talent management for workforce stability: A systematic review on human capital, skill and retention in the era of twin transition
Philomina, I. G. A., Anbumathy, M. M., & Vineeth, K. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130454 · Cited by 1 · see citing works
Artificial intelligent-enhanced federated approach for employee performance evaluation using multimodal data
Zhang, L.-Y. (2026) · Discover Computing · doi:10.1007/s10791-026-10301-y · Cited by 1
Artificial Intelligence technologies’ impact on the work design of healthcare professionals: a scoping review and future research agenda
Scholz, F., Sahakian, T. S., GÜVEN, N., Renkema, M., Shulzhenko, E., Cuijpers, C., Schouten, G., Wouters, E., & de Vries, E. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2711067 · Cited by 1 · see citing works
“…and interpretability shape HCPs work design characteristics, and find workable solutions aimed at the explainability and transparency of the AI (Chowdhury et 2023) to instigate trust among HCPs. Future studies should also examine the synergies identified in our AI-driven Work Synergy Model and…”
Trust in human–AI collaboration in finance: a bibliometric–systematic literature review
Mirabile, M., Corazza, G. E., & Alonso, J. M. (2026) · AI & Society · doi:10.1007/s00146-026-03049-y · Cited by 4 · see citing works
“…relies on transparency and accountability as formal safeguards (Behl et al. 2023), treats algorithmic openness as the basis for managerial reliance (Chowdhury et al. 2023), equates confidence with explainable and auditable decisions aligned to oversight (Sabharwal et al.2025), and embeds assurance…”
Unravelling technostress in human–AI collaborative brainstorming: examining the impact of AI technology characteristics on brainstorming outcomes through AI control anxiety
Wang, S., Yan, Q., & Leng, J. (2026) · Kybernetes · doi:10.1108/k-11-2025-2887 · Cited by 1 · see citing works
The intersection of AI, HRM, and organizational learning: review of emerging trends and unanswered questions
Roy, A. (2026) · Knowledge Management Research & Practice · doi:10.1080/14778238.2026.2648157 · Cited by 1 · see citing works
Intelligent systems for assessing competency development in students and graduates of engineering specialisations: expectations of educators, students, and employers
Николаевна, А. С., Lukash, A. V., & Davydov, A. (2025) · The Education and science journal · doi:10.17853/1994-5639-2025-8-136-166 · Cited by 1 · see citing works
The Ethical Implications of AI in Management: Navigating Challenges in Decision-Making
Usha, S. M., Medatwal, C., Vutti, P. P., Muralidhar, L. B., Rai, A., & Verma, A. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940240 · Cited by 2 · see citing works
“The body of research on AI in HRM has grown significantly in recent years [10, 11].”
Understanding Organizational Transitions: Interpretable Top Manager Turnover Prediction using a Heterogeneous Graph Neural Network
Guo, Y.-M., Ren, Q., & Broucke, S. V. (2024) · International Conference on Computer Science and Artificial Intelligence · doi:10.1145/3709026.3709028 · Cited by 1
Unpacking the mechanisms linking digital talent management to employee performance: Mediation by e-HRM and moderation by data analytics capability
Hai, N. M., Hai, H. T. Q., Dao, Q. T., Minh, N. T. C., & Mai, G. V. T. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103730
Employee acceptance of AI-driven performance management systems in hospitality
(277978), Y. L., Konar, R., Xiaoxin, L., & Wirtz, J. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103585
Delegating decision authority to AI in recruitment: an empirical study of trust, control and algorithmic accountability
Hahn, A.-S., Martinez, L. F., & Ferreira, A. I. (2026) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-03-2026-0293
Managing the transformation of the cognitive potential of the economy through the introduction of generative AI as a personnel innovation in the face of labor shortages
Kozhukalova, O. Y., Krasnyanskaya, O. V., Karavaeva, J. S., Grigorenko, O. V., & Vavilina, A. V. (2026) · VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES ECONOMICS · doi:10.24143/2073-5537-2026-3-55-69
Predicting leadership emergence in global virtual teams: machine learning applications in algorithmic HRM
Rickley, M., Mudireddy, A. R., Ray, A., & Taras, V. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2741401
Artificial Intelligence in HR Services and Employee Experience: The Role of Personalization and Organizational Digital Culture
Mahmoud, M. H., Ali, A. A., Saad, W. A., & Garza‐Reyes, J. A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2730088
SWAR-CI: Synthetic Workforce Augmentation and Risk-Calibrated Intelligence for Strategic Employee Attrition Management
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Influential
Human–AI Collaboration and Digital Competency: Examining Their Roles in Shaping Employee Creativity and Job Performance in Indonesia's Technology Sector
Hakim, C., & Syaefullah, S. (2026) · West Science Social and Humanities Studies · doi:10.58812/wsshs.v4i08.3101
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Influential
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction
Setiawan, R., Pranoto, G. T., Abdullah, Z., et al. (2026) · Journal of Information Systems and Informatics · doi:10.63158/journalisi.v8i4.1732
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A systematic review of human vs machine intelligence and ethical tensions in human resource management
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Onward and upward: strategies for overcoming challenges in algorithmic HR implementation
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Algorithmic governance at work: a legal-sociological and policy analysis of AI regulation, institutional power and labor rights
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Artificial intelligence–based employee turnover forecasting as a decision support tool for HR management
Таранюк, В. Л., & Vinogradova-Zinkevič, I. (2026) · Business and management · doi:10.3846/bm.2026.2426
Ethical AI in HRM and HR Data Analytics in Achieving Fair Recruitment Outcomes and Employee Trust at Technology Companies in Jakarta
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Virtual Influencer and User Engagement: A Systematic Review and Future Research Agenda
Priyanka, Kashyap, A. K., & Dash, S. (2026) · International Journal of Consumer Studies · doi:10.1111/ijcs.70237
Employees show greater willingness to voice toward algorithmic than human leaders in cognitive tasks through fairness perception and psychological safety
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Explainable AI for employee turnover prediction: a SHAP-based intelligent analytics approach
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“Chowdhury et al. 21 applied the LIME method for model interpretation in employee turnover prediction, but LIME is based on local linear approximation, limiting its ability to capture non-linear effects.”
Transparency and Accountability in the Use of Artificial Intelligence: Analysis of the Attitudes of the Organization’s Employees
Kocot, M., Soboń, J., Bondyra, K., & Kwasek, A. (2026) · Journal for Perspectives of Economic Political and Social Integration · doi:10.18290/pepsi-2026-0004
“This fact remains confirmed by analyses on explaining models that predict employee turnover (Chowdhury et al., 2023).”

Authors: Chowdhury, S., Joel-Edgar, S., Dey, P. K., Bhattacharya, S., & Kharlamov, A.
Affiliations: TBS Education (FR)
Northeastern University London (GB)
Aston University (GB)
Corresponding author: Chowdhury, Soumyadeb
ORCID: Chowdhury, Soumyadeb
Joel-Edgar, Sian
Dey, Prasanta Kumar
Kharlamov, Alexander
Venue: The International Journal of Human Resource Management
Publisher: Informa UK Limited
Published: 2022-04-27
Volume: 34
Issue: 14
Pages: 2732-2764
ISSN: 0958-5192 (ISSN-L)
1466-4399
DOI: 10.1080/09585192.2022.2066981
OpenAlex: W4293154704
Semantic Scholar: 5ba5e2e3223da8ec3b71ca74babb86157b80ba2b
Cited by: 140
References: 0
Citations by year: 2026: 43 · 2025: 48 · 2024: 38 · 2023: 10 · 2022: 1
FWCI: 22.92
Type: journal article
Language: en
Open access: green
nottingham-repository.worktribe.com
Licence: other-oa
Locations: The International Journal of Human Resource Management (publishedVersion)
Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
Aston Publications Explorer (Aston University) (submittedVersion)
Topics: Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords: employee turnover, artificial intelligence, machine learning, model transparency, explainability, lime, local interpretable model-agnostic explanations, algorithmic transparency, HR decision-making, trust in AI, resource-based view, competitive advantage, predictive analytics

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Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover

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