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
Brock, J. K.-U., & von Wangenheim, F. (2019) · California Management Review · doi:10.1177/1536504219865226 · Cited by 622
Transparency and reproducibility in artificial intelligence
Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature · doi:10.1038/s41586-020-2766-y · Cited by 540 · cited 1×
“In this context, transparency within AI systems is intangible because it represents the system’s quality and characteristics (Haibe-Kains et al., 2020).”
BackgroundChowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.069 · Cited by 531 · cited 1×
“The primary goal of embedding transparency within AI-based ML models is to help the decision-making authorities understand what the AI system is doing, how it is generating the output responses and why a particular response is generated (Choudhury et al., 2020).”
BackgroundSelf-citationAdoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations
Pillai, R., & Sivathanu, B. (2020) · Benchmarking: An International Journal · doi:10.1108/bij-04-2020-0186 · Cited by 422 · cited 1×
“The existing research (Pillai and Sivathanu, 2020) in this area have reported that the ease to interpret and explain the response generated by AI algorithms both at global and local level decreases with the complexity of algorithms, increasing the size of the dataset and increasing the number of…”
BackgroundMachine learning and human capital complementarities: Experimental evidence on bias mitigation
Choudhury, P., Starr, E., & Agarwal, R. (2020) · Strategic Management Journal · doi:10.1002/smj.3152 · Cited by 293
Caputo, F., Cillo, V., Candelo, E., & Liu, Y. (2019) · Management Decision · doi:10.1108/md-07-2018-0833 · Cited by 199
The science and practice of workforce analytics: Introduction to the HRM special issue
Huselid, M. A. (2018) · Human Resource Management · doi:10.1002/hrm.21916 · Cited by 179
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
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., et al. (2023) · Human Resource Management Journal · doi:10.1111/1748-8583.12524 · Cited by 908
Self-citationRezaei, M., Pironti, M., & Quaglia, R. (2025) · Management Decision · doi:10.1108/md-10-2023-2023 · Cited by 123
Gong, Q., Fan, D., & Bartram, T. (2024) · The International Journal of Human Resource Management · doi:10.1080/09585192.2024.2440065 · Cited by 156
Simón, C., Revilla, E., & Sáenz, M. J. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114783 · Cited by 139
Kim, S., Khoreva, V., & Vaiman, V. (2024) · Human Resource Management · doi:10.1002/hrm.22268 · Cited by 134
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
