The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Haenlein, M., & Kaplan, A. (2019) · California Management Review · doi:10.1177/0008125619864925 · Cited by 2,655
Ananny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1871398 · Cited by 1,134 · cited 1×
“Therefore, from the viewpoint of a HR manager, they will be keen on recall value to focus on employees who are highly likely to leave the organisation, based on the prediction made by the model.”
BackgroundBrougham, D., & Haar, J. (2018) · Journal of Management & Organization · doi:10.1017/jmo.2016.55 · Cited by 1,117
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100899 · Cited by 927
Self-citationLeakage in data mining: Formulation, detection, and avoidance
Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2382577.2382579 · Cited by 791
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.07.045 · Cited by 780
Research works printed but not linked: 104
- Addis C., 2020, General Data Protection Regulation (GDPR), Artificial Intelligence (AI) and UK organisations: a year of implementation of GDPR. In UK Academy for Information Systems Conference Proceedings 2020 (p. 24). UKAIS–UK Academy for Information Systems.
- Agarrwal. (2020). How to win with machine learning. Harvard Business Review .
- Alalie H. M., 2018, Journal of Advance Management Research,, 6, 1
- 10.1016/j.sbspro.2014.03.646 · DOI
- Allen D. G., 2008, Retaining talent: A guide to analyzing and managing employee turnover
- Amabile T. M., 2019, Academy of Management Discoveries, 6
- 10.1145/2702123.2702509 · DOI
- 10.1287/orsc.1080.0406 · DOI · Paper page
- Baird B., 2016, Understanding pressures in general practice
- 10.1177/014920630102700602 · DOI · Paper page
- Barro S., 2019, MIT Sloan Management Review,, 60, 22
- Belizón M. J., 2021, Human Resource Management Journal, 1
- Bieda, L. (2020). How organizations can build analytics agility. MIT Sloan Management Review .
- Blitzer, J., Dredze, M. & Pereira, F. (2007, June). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification . Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (pp. 440–447).
- 10.1111/j.1748-8583.1996.tb00412.x · DOI
- 10.1016/j.jom.2015.11.003 · DOI
- 10.1080/09585192.2022.2035161 · DOI · Paper page
- Budhwar, P. & Malik, A. (2020). Special issue: Leveraging artificial and human intelligence through human resource management. Call for paper. Human Resource Management Review .
- 10.5465/ame.1999.2570557 · DOI
- 10.1038/nmeth.4526 · DOI · Paper page
- 10.1007/978-1-4899-7138-8_16 · DOI
- Charlwood A., Human Resource Management Journal, 1
- 10.5465/AMBPP.2019.18018abstract · DOI · Paper page
- Choo C. W., 1991, The Canadian Journal of Information Science,, 16, 32
- Chowdhury, R. (2020). Putting responsible AI into practice. MIT Sloan Management Review .
- 10.1016/j.jbusres.2020.06.012 · DOI
- 10.1080/09585192.2019.1711442 · DOI
- 10.2307/3665543 · DOI
- 10.1177/0008125620934864 · DOI · Paper page
- Cowgill B., 2020, Columbia Business School Research Paper. Availiable at https://ssrn.com/abstract=3361280
- 10.1177/1461444816657096 · DOI · Paper page
- Daugherty P. R., 2019, MIT Sloan Management Review, 60, 1
- Davenport T. H., 2016, MIT Sloan Management Review,, 58, 29
- Davenport, T. H. & Bean, R. (2017). How P&G and American Express are approaching AI. Harvard Business Review . Retrieved September 20, 2020, from https://tinyurl.com/sf9uvuv4
- Davenport T. H., 2018, Harvard Business Review,, 96, 108
- 10.1145/1143844.1143874 · DOI · Paper page
- 10.1016/j.hrmr.2020.100745 · DOI
- 10.1186/1755-8794-4-31 · DOI
- 10.1108/PR-05-2016-0108 · DOI
- Edwards M., 2022, Human Resource Management Journal, 1
- 10.1037/0021-9010.87.3.565 · DOI
- Farrington R. E., 2008, Revenue-Cycle Strategist, 5, 6
- Fountaine T., 2019, Harvard Business Review,, 97, 62
- Guenole N., 2018, With Insights and Tips on Getting Started. Armonk: IBM Smarter Workforce Institute, IBM Corporation.
- 10.1108/SHR-12-2018-0104 · DOI · Paper page
- 10.1016/j.jbusres.2016.08.004 · DOI
- Gusnadi Y., 2020, bit-Tech,, 2, 19
- 10.1037/apl0000103 · DOI
- 10.1007/978-3-319-18552-1_11 · DOI
- 10.1016/S0378-7206(02)00003-4 · DOI
- 10.1016/j.jbusres.2020.08.025 · DOI
- Iansiti, M. & Lakhani, K. R. (2020). Competing in the Age of AI. Harvard Business Review .
- 10.1080/09585192.2021.1891114 · DOI · Paper page
- 10.1016/j.bushor.2018.03.007 · DOI · Paper page
- 10.1175/WAF-D-19-0170.1 · DOI
- 10.1108/JTF-02-2020-0013 · DOI
- 10.1108/EJTD-05-2018-0045 · DOI
- 10.1016/j.bushor.2019.09.003 · DOI · Paper page
- 10.1007/s11301-020-00181-x · DOI · Paper page
- 10.1007/s13218-017-0523-7 · DOI
- Kiron, D. & Schrage, M. (2019). Strategy for and with AI. MIT Sloan Management Review .
- 10.1145/2858036.2858529 · DOI · Paper page
- 10.1177/0275074017715322 · DOI
- 10.1177/2046147X17694937 · DOI
- 10.1007/978-3-642-33863-2_39 · DOI
- 10.1109/VAST.2014.7042494 · DOI
- 10.1016/j.visinf.2017.01.006 · DOI
- 10.1080/09585192.2020.1859582 · DOI
- 10.1080/09585192.2017.1325388 · DOI
- Malone T. W., 2018, MIT Sloan Management Review,, 59, 34
- 10.1016/j.hrmr.2020.100795 · DOI · Paper page
- Martin, R. L. (2009). The design of business: Why design thinking is the next competitive advantage . Harvard Business Press.
- 10.1016/j.im.2021.103434 · DOI · Paper page
- 10.1111/1748-8583.12393 · DOI
- Morse, G. (2020). Harnessing artificial intelligence. Harvard Business Review .
- 10.1002/widm.53 · DOI
- O’Connell M., 2007, Industrial Management,, 49, 14
- O’Neil C., 2016, Weapons of math destruction: How big data increases inequality and threatens democracy
- 10.1109/TVCG.2014.2331979 · DOI
- 10.1525/cmr.2016.58.3.5 · DOI · Paper page
- 10.1007/s11747-019-00710-5 · DOI · Paper page
- 10.1016/j.jbusres.2020.05.019 · DOI · Paper page
- Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B. & Kiron, D. (2019). Winning with AI. MIT Sloan Management Review, 61180.
- 10.1145/2939672.2939778 · DOI · Paper page
- Satell, G. & Sutton, J. (2019). We need AI that is explainable, auditable and transparent. Harvard Business Review . Retrieved January 10, 2021, from https://tinyurl.com/s5pyn7a
- 10.1089/pop.2012.0114 · DOI
- 10.5465/amj.2005.17843940 · DOI
- 10.1016/j.chb.2019.04.019 · DOI · Paper page
- 10.1177/0008125619862257 · DOI · Paper page
- 10.1016/j.jbusres.2020.09.068 · DOI · Paper page
- Silvernam, K. (2020). Why your board needs a plan for AI oversight. MIT Sloan Management Review .
- 10.6000/1927-5129.2017.13.76 · DOI
- Stevens, J. P.et al. (2020). How one Boston hospital built a COVID-19 forecasting system. Harvard Business Review . Retrieved December 1, 2020.
- 10.1016/j.chb.2019.04.012 · DOI
- 10.1080/07421222.2002.11045736 · DOI
- 10.1080/09585192.2011.637058 · DOI
- 10.1007/s10994-019-05855-6 · DOI · Paper page
- 10.5465/amd.2018.0084 · DOI · Paper page
- 10.1111/j.2044-8325.1978.tb00413.x · DOI
- Weick K. E., 1995, Sensemaking in organizations, 3
- 10.1002/smj.4250050207 · DOI · Paper page
- Wilson H. J., 2018, Harvard Business Review, 96, 114
- Wilson H. J., 2017, MIT Sloan Management Review, 58, 14
- Zhang Y., 2019, arXiv Preprint arXiv:1904.12991 · arXiv
Cited by
Sorted by citations
Items 101–122 of 122
Azzahra, F., Putra, R. E., Aznedra, A., et al. (2026) · GLOBAL DIGITAL · doi:10.33373/gd.v2i1.9259
Artificial Intelligence in Human Resource Management: A Systematic Literature Review (SLR)
Alawiyah, I. R., Suharto, S., & Burhan, U. (2026) · RIGGS Journal of Artificial Intelligence and Digital Business · doi:10.31004/riggs.v5i2.10011
“Furthermore, organizations increasingly utilize AI to improve decision quality and optimize workforce management processes, thereby enhancing organizational performance and employee support [22], [32].”
Estiri, M., Dahooie, J. H., Mohammadi, N., et al. (2026) · Decision Analytics Journal · doi:10.1016/j.dajour.2026.100704
Kunze, A., Torre, D. L., & Rocca, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01285-1
“For example, related to business and management, Chowdhury et al. [13] seek to predict and explain employee turnover.”
Research on Employee Turnover Prediction using Deep Q Network - Light Gradient Boosting Model
He, C.-S., & Wang, L. (2026) · 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn67954.2026.11566522
All's well that begins just: organizational justice as precursor of trust in AI-enabled HRM
Shukla, Y., Pandey, J., & Pereira, V. E. (2026) · Personnel Review · doi:10.1108/pr-05-2025-0508 · Cited by 5
Employee Retention Analysis: Insights and Predictive Modeling with Machine Learning Techniques
Prajapati, J., & Agarwal, K. (2026) · 2026 International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11577001
Ningsih, S., Syahputra, D., Husainah, N., Wibowo, A., & Hakim, C. (2026) · West Science Social and Humanities Studies · doi:10.58812/wsshs.v4i03.2702
“Among these technological developments, artificial intelligence (AI) has emerged as one of the most influential innovations shaping modern organizational management [1], [2].”
Srinivas, P., & Anupama, B. (2026) · International Journal of Economic Social Science and Management LAW · doi:10.64751/x4r8ft40
“ML is used to analyse large volume of data(Ben Jabeur et al., 2023), make predictions, data patterns and data-driven decisions(Chowdhury et al., 2023).”
Song, S., Shi, Y., Ge, X., Yu, H., Wang, W., & Tian, A. W. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2670478
“…organizational systems depends heavily on employees’ trust in intelligent machines (Glikson & Woolley, 2020), which refers to employees’ positive perceptions toward intelligent machines and their potential impacts on employees’ roles and responsibilities (Chowdhury et al., 2023; Kong et al., 2023).”
Ma, M. (2026) · Proceedings of the 2nd International Conference on Digital Management and Information Technology · doi:10.1145/3808707.3808815
“The data processing module completes the ingestion, cleansing, and feature generation of multi-source HR data through a unified interface, providing standardized inputs for upper-layer models [10].”
Haryanti, T., & Supriandi, S. (2026) · RIGGS: Journal of Artificial Intelligence and Digital Business · doi:10.31004/riggs.v4i4.5420
“This result aligns with the augmentation perspective of AI, which emphasizes the complementary role of intelligent systems in improving work processes, supporting decision-making, and strengthening organizational performance without necessarily displacing workers [19], [20].”
Bei, J., Liu, Z.-Q., Huang, J.-Y., et al. (2025) · Proceedings of the 2025 6th International Conference on Computer Science and Management Technology · doi:10.1145/3795154.3795166
“Employee promotion decisions are influenced by multifaceted organizational factors spanning demographic characteristics, performance history, and institutional affiliations [9].”
Jin, S., Li, R., & Jiang, X. (2025) · Proceedings of the 2025 6th International Conference on Computer Science and Management Technology · doi:10.1145/3795154.3795194
“Although significant progress has been made in applying machine learning (ML) to human resource analytics, existing approaches often suffer from limited interpretability and generalizability across organizational contexts [6], [7].”
Bibliometric Mapping of Competency-Based Recruitment
Judijanto, L. (2025) · Sustainable Development Insights · doi:10.58812/sdi.v1i04.2459
The Impact of Predictive Robotics on the Labor Economy Through the Lens of Artificial Intelligence
Ghai, S., Choudhary, S., Mukuntharaj, C., Arora, M., Amudha, A., & Ramachandran, T. (2025) · International Conference on Electrical Engineering and Informatics · doi:10.1109/iceei68459.2025.11330969
“Lastly, AIPRF is a process of utilization [28], not an end to thinking, moving towards directions that foster technological progress and are labor-intensive sustainably [29].”
A Bibliometric Analysis of Employee Attrition Prediction using Machine Learning Methods
Alharbi, F., & Alshammari, M. (2025) · 2025 6th International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi67967.2025.11547559
Support from Superiors and Employee Turnover at a Manufacturing Company in Poland
Stolarska-Szeląg, E. (2025) · Krakow Review of Economics and Management/Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie · doi:10.15678/krem.18731
AI-Driven Employee Performance Prediction in the Public Sector with GAs and SHAP Interpretability
Alsobaey, T. M., & Alkhateeb, F. Q. (2025) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce66530.2025.11474052
“As predictive models become integral to HR decisions, explainable AI methods like SHAP are increasingly necessary to ensure transparency, fairness, and stakeholder trust [16], [17].”
Alghamdi, S. (2025) · Global · doi:10.1109/temsconglobal64363.2025.11238325
“The erosion of user trust due to opaque smart contract operations, inaccessible audit trails, and explainability deficits in AI models further constrains widespread adoption [14], [15].”
Enhancing Automated Real-Time Data Analysis Systems with Machine Learning
Ravivarman, G., V, R., Futane, P. R., et al. (2023) · 2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS) · doi:10.1109/icpsitiags59213.2023.10527747
“First of all, the paper talks about using gadget getting to know to detect anomalies in big-scale records from real-time structures[10-11].”
BackgroundDifusión pública de información sobre inteligencia artificial en las empresas
Palomino, M. J. G., Abelaira, T. A., & Ariza, L. R. (2026) · RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação · doi:10.20396/rdbci.v24i00.8679450
Metadata
- 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
- 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
- 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
- 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)
- Physical Sciences › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
