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The pivotal role of interpretability in employee attrition prediction and decision-making

  • Gabriel Marín Díaz [1] ; José Javier Galán Hernández [1]
    1. [1] Universidad Complutense de Madrid

      Universidad Complutense de Madrid

      Madrid, España

  • Localización: The Leading Role of Smart Ethics in the Digital World / Mario Arias Oliva (ed. lit.) Árbol académico, Jorge Pelegrín Borondo (ed. lit.), Kiyoshi Murata (ed. lit.), Mar Souto Romero (ed. lit.), 2024, ISBN 978-84-09-58161-0, págs. 265-275
  • Idioma: inglés
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  • Referencias bibliográficas
    • Duval, A. (2019). Explainable Artificial Intelligence ( XAI ) Explainable Artificial. April. https://doi.org/10.13140/RG.2.2.24722.09929
    • Freitas, A. A. (2014). Comprehensible classification models: a position paper. ACM SIGKDD Explorations Newsletter, 15(1), 1–10.
    • Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2019). Explaining explanations: An overview of interpretability...
    • Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking Inside the Black Box: Visualizing Statistical Learning With Plots...
    • Goodrich, M. T. (2010). Data Structures and Algorithms in Python. Wiley, 53(9), 1689–1699. http://arxiv.org/abs/1011.1669%0Ahttp://dx.doi.org/10.1088/1751-8113/44/8/085201
    • Hall, P. (2022). Machine Learning for High-Risk Applications.
    • Hofeditz, L., Clausen, S., Rieß, A., Mirbabaie, M., & Stieglitz, S. (2022). Applying XAI to an AI-based system for candidate management...
    • Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications,...
    • Kaggle HR Analytic Data Set. (n.d.). https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset
    • Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 35–43. https://doi.org/10.1145/3233231
    • Marín Díaz, G., Galán Hernández, J. J., & Galdón Salvador, J. L. (2023). Analyzing Employee Attrition Using Explainable AI for Strategic...
    • Mishra, D. (2013). Review of literature on factors influencing attrition and retention. International Journal of Organizational Behaviour...
    • Molnar, C. (2019). Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Book, 247. https://christophm.github.io/interpretable-ml-book
    • Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing:...
    • Perisic, A., & Pahor, M. (2020). Extended RFM logit model for churn prediction in the mobile gaming market. Croatian Operational Research...
    • Saaty, T. L. (1980). The analytic hierarchy process : planning, priority setting, resource allocation LK - https://ucm.on.worldcat.org/oclc/911278091....
    • Sangeetha, V., & Prasad, K. J. R. (2006). Deep residual learning for image recognition. Indian Journal of Chemistry- Section B Organic...
    • Shafique, U., & Qaiser, H. (2014). A Comparative Study of Data Mining Process Models ( KDD , CRISP-DM and SEMMA ). International Journal...
    • Srivastava, P. R., & Eachempati, P. (2021). Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An...
    • Thomas L. Saaty. (2008). Decision making with the analytic hierarchy process. Journal of Manufacturing Technology Management, 26(6), 791–806....

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