RT Conference Proceedings
T1 Decoding employee attrition: a unified approach with XAI and AHP
A1 Marín Díaz, Gabriel
A1 Galán Hernández, José Javier
A2 Kerre, Etienne E.
A2 Lu, Jie
A2 Martínez, Luis
A2 Li, Tianrui
A2 Montero, Javier
A2 Flores-Vidal, Pablo
AB 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.
SN 2972-4465
YR 2024
FD 2024-07-21
LK https://hdl.handle.net/20.500.14352/129925
UL https://hdl.handle.net/20.500.14352/129925
LA eng
NO 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
NO SIN FINANCIACIÓN
DS Docta Complutense
RD 17 ene 2026