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oai:docta.ucm.es:20.500.14352/1299252026-01-22T14:58:41Zcom_20.500.14352_14col_20.500.14352_20

Decoding employee attrition: a unified approach with XAI and AHP Marín Díaz, Gabriel Galán Hernández, José Javier Kerre, Etienne E. Lu, Jie Martínez, Luis Li, Tianrui Montero, Javier Flores-Vidal, Pablo 004.8 519.226 303.4 004.6 331 XAI artificial intelligence AHP human resources talent attraction Inteligencia artificial (Informática) Técnicas de Investigación Social Teoría de la decisión Trabajo 1203.04 Inteligencia Artificial 1209.04 Teoría y Proceso de decisión 5311.04 Organización de Recursos Humanos 1209.03 Análisis de Datos In the face of escalating employee attrition challenges, organizations are increasingly relying on artificial intelligence (AI) to predict and address turnover. This chapter explores the application of explainable AI (XAI) to identify potential employee turnover, analyzing its impact on organizational productivity and stability. The second section focuses on AI techniques that leverage historical data to forecast attrition, enabling proactive interventions. The third part introduces XAI to enhance model transparency, providing HR professionals with deeper insights to develop targeted retention strategies aligned with individual employee needs. Integrating the Analytic Hierarchy Process (AHP) model becomes imperative to assign weights to criteria identified by AI as significant. This incorporation aims to introduce the human factor into decision-making. SIN FINANCIACIÓN Depto. de Sistemas Informáticos y Computación Fac. de Estudios Estadísticos TRUE pub 2026-01-12T15:36:03Z 2026-01-12T15:36:03Z 2024-07-21 conference paper AM https://hdl.handle.net/20.500.14352/129925 2972-4465 10.1142/13882 eng Díaz Marín, G., & Galán Hernández, J. J. Decoding Employee Attrition: A Unified Approach with XAI and AHP. In Intelligent Management of Data and Information in Decision Making (pp. 367–375). https://doi.org/10.1142/9789811294631_0046 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ open access application/pdf