Machine learning with real-world HR data: mitigating the trade-off between predictive performance and transparency

References

• Apley, D. W., & Zhu, J. (2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4), 1059–1086. https://doi.org/10.1111/rssb.12377
   Web of Science ®Google Scholar
• Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
   Web of Science ®Google Scholar
• Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 205395171562251. https://doi.org/10.1177/2053951715622512
   Web of Science ®Google Scholar
• Charlwood, A., & Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal, 32(4), 729–742. https://doi.org/10.1111/1748-8583.12433
   Web of Science ®Google Scholar
• Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. https://doi.org/10.1016/j.hrmr.2019.100698
   Web of Science ®Google Scholar
• Choudhury, P., Allen, R. T., & Endres, M. G. (2021). Machine learning for pattern discovery in management research. Strategic Management Journal, 42(1), 30–57. https://doi.org/10.1002/smj.3215
   Web of Science ®Google Scholar
• Chowdhury, S., Joel-Edgar, S., Dey, P. K., Bhattacharya, S., & Kharlamov, A. (2022). Embedding transparency in artificial intelligence machine learning models: Managerial implications on predicting and explaining employee turnover. The International Journal of Human Resource Management, 34(14), 2732–2764. https://doi.org/10.1080/09585192.2022.2066981
   Web of Science ®Google Scholar
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
   Web of Science ®Google Scholar
• Edwards, M. R., Charlwood, A., Guenole, N., & Marler, J. (2022). HR analytics: An emerging field finding its place in the world alongside simmering ethical challenges. Human Resource Management Journal. 1-11. https://doi.org/10.1111/1748-8583.12435
   Web of Science ®Google Scholar
• Erel, I., Stern, L. H., Tan, C., & Weisbach, M. S. (2021). Selecting directors using machine learning. The Review of Financial Studies, 34(7), 3226–3264. https://doi.org/10.1093/rfs/hhab050
   Web of Science ®Google Scholar
• Gal, U., Jensen, T. B., & Stein, M.-K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
   Web of Science ®Google Scholar
• Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet. Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9
   PubMedGoogle Scholar
• Gray, A. M., & Phillips, V. L. (1994). Turnover, age and length of service: A comparison of nurses and other staff in the National Health Service. Journal of Advanced Nursing, 19(4), 819–827. https://doi.org/10.1111/j.1365-2648.1994.tb01155.x
   PubMed Web of Science ®Google Scholar
• Grissom, J. A., Viano, S. L., & Selin, J. L. (2016). Understanding employee turnover in the public sector: Insights from research on teacher mobility. Public Administration Review, 76(2), 241–251. https://doi.org/10.1111/puar.12435
   Web of Science ®Google Scholar
• Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M. B. (2008). 5 turnover and retention research: A glance at the past, a closer review of the present, and a venture into the future. Academy of Management Annals, 2(1), 231–274. https://doi.org/10.1080/19416520802211552
   Web of Science ®Google Scholar
• Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
   Web of Science ®Google Scholar
• King, K. G. (2016). Data analytics in human resources. Human Resource Development Review, 15(4), 487–495. https://doi.org/10.1177/1534484316675818
   Web of Science ®Google Scholar
• Kuhn, M. (2019). Building predictive models in R using the caret package. Available online at https://topepo.github.io/caret/index.html, updated on 3/27/2019, checked on 12/30/2021.
   Google Scholar
• Landis, J. R., & Koch, G. G. (1977). An application of hierarchical Kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33(2), 363. https://doi.org/10.2307/2529786
   PubMed Web of Science ®Google Scholar
• Langer, M., & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881
   Web of Science ®Google Scholar
• Leavitt, K., Schabram, K., Hariharan, P., & Barnes, C. M. (2021). Ghost in the machine: On organizational theory in the age of machine learning. Academy of Management Review, 46(4), 750–777. https://doi.org/10.5465/amr.2019.0247
   Web of Science ®Google Scholar
• Lin, L., Bai, Y., Mo, C., Liu, D., & Li, X. (2021). Does pay raise decrease temporary agency workers’ voluntary turnover over time in China? Understanding the moderating role of demographics. The International Journal of Human Resource Management, 32(7), 1537–1565. https://doi.org/10.1080/09585192.2018.1539861
   Web of Science ®Google Scholar
• Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing System. Available online at http://arxiv.org/pdf/1705.07874v2.
   Google Scholar
• Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021). Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. The International Journal of Human Resource Management, 32(12), 2545–2562. https://doi.org/10.1080/09585192.2021.1925326
   Web of Science ®Google Scholar
• Molnar, C. (2022). Interpretable machine learning. A guide for making Black Box Models interpretable. (2nd ed.). Lulu.
   Google Scholar
• Molnar, C., Casalicchio, G., & Bischl, B. (2018). iml: An R package for interpretable machine learning. Journal of Open Source Software, 3(26), 786. https://doi.org/10.21105/joss.00786
   Google Scholar
• Putka, D. J., Beatty, A. S., & Reeder, M. C. (2018). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 21(3), 689–732. https://doi.org/10.1177/1094428117697041
   Web of Science ®Google Scholar
• Rombaut, E., & Guerry, M.-A. (2018). Predicting voluntary turnover through human resources database analysis. Management Research Review, 41(1), 96–112. https://doi.org/10.1108/MRR-04-2017-0098
   Google Scholar
• Rombaut, E., & Guerry, M.-A. (2021). Determinants of voluntary turnover: A data-driven analysis for blue and white collar workers. Work (Reading, Mass.), 69(3), 1083–1101. https://doi.org/10.3233/WOR-213538
   PubMedGoogle Scholar
• Rubenstein, A. L., Eberly, M. B., Lee, T. W., & Mitchell, T. R. (2018). Surveying the forest: A meta-analysis, moderator investigation, and future-oriented discussion of the antecedents of voluntary employee turnover. Personnel Psychology, 71(1), 23–65. https://doi.org/10.1111/peps.12226
   Web of Science ®Google Scholar
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
   PubMedGoogle Scholar
• Russell, C. J., & Sell, M. V. (2012). A closer look at decisions to quit. Organizational Behavior and Human Decision Processes, 117(1), 125–137. https://doi.org/10.1016/j.obhdp.2011.09.002
   Web of Science ®Google Scholar
• Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81, 84–90. https://doi.org/10.1016/j.inffus.2021.11.011
   Web of Science ®Google Scholar
• Somers, M. J., Birnbaum, D., & Casal, J. (2021). Supervisor support, control over work methods and employee well-being: New insights into nonlinearity from artificial neural networks. International Journal of Human Resource Management, 32(7), 1620–1642. https://doi.org/10.1080/09585192.2018.1540442
   Web of Science ®Google Scholar
• Speer, A. B. (2021). Empirical attrition modelling and discrimination: Balancing validity and group differences. Human Resource Management Journal, 34(1), 1–19. https://doi.org/10.1111/1748-8583.12355
   Web of Science ®Google Scholar
• Vale, D., El-Sharif, A., & Ali, M. (2022). Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law. AI and Ethics, 2(4), 815–826. https://doi.org/10.1007/s43681-022-00142-y
   Google Scholar
• Valizade, D., Schulz, F., & Nicoara, C. (2024). Towards a paradigm shift: How can machine learning extend the boundaries of quantitative management scholarship? British Journal of Management, 35(1), 99–114. https://doi.org/10.1111/1467-8551.12678
   Web of Science ®Google Scholar
• van den Broek, E., Sergeeva, A., & Huysman Vrije, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580. https://doi.org/10.25300/MISQ/2021/16559
   Web of Science ®Google Scholar
• Yakusheva, O., Bang, J. T., Hughes, R. G., Bobay, K. L., Costa, L., & Weiss, M. E. (2022). Nonlinear association of nurse staffing and readmissions uncovered in machine learning analysis. Health Services Research, 57(2), 311–321. https://doi.org/10.1111/1475-6773.13695
   PubMed Web of Science ®Google Scholar
• Yin, R. K. (2013). Validity and generalization in future case study evaluations. Evaluation, 19(3), 321–332. https://doi.org/10.1177/1356389013497081
   Web of Science ®Google Scholar
• Yuan, S., Kroon, B., & Kramer, A. (2021). Building prediction models with grouped data: A case study on the prediction of turnover intention. Human Resource Management Journal, 34(1), 20–38. https://doi.org/10.1111/1748-8583.12396
   Web of Science ®Google Scholar