RT Book, Section
T1 The pivotal role of interpretability in employee attrition prediction and decision-making
A1 Marín Díaz, Gabriel
A1 Galán Hernández, José Javier
A2 Arias Oliva, Mario
A2 Pelebrín Borondo, Jorge
A2 Murata, Kiyoshi
A2 Souto Romero, Mar
AB This article explores the evolution of machine learning (ML) algorithms, emphasizing the growing importance of interpretability in understanding automated decisions. Progress from early to advanced ML models highlights the need for better performance and adaptability. However, the inherent black-box nature of many ML algorithms raises challenges, underscoring the necessity for interpretability to improve transparency and accountability.Examining the evolution of interpretability in ML, the article showcases advancements in techniques facilitating human comprehension of decision-making processes. As ML becomes integral across domains, the article underscores the importance of interpretable models to bridge the gap between automated decisions and human understanding.The article delves into the changing role of humans in decision-making. Despite the efficiency of ML algorithms, the interpretability factor prompts a revaluation of human involvement, necessitating a balanced approach for ethical AI deployment.Furthermore, the article explores integrating decision-making methods like Analytic Hierarchy Process (AHP) to enhance interpretability. Proposing a framework that combines AHP with interpretable ML models, it suggests a structured approach for human-in-the-loop decision-making while considering feature importance.
PB Universidad de La Rioja
SN 978-84-09-58161-0
YR 2024
FD 2024-03-01
LK https://hdl.handle.net/20.500.14352/130072
UL https://hdl.handle.net/20.500.14352/130072
LA eng
NO Marín Díaz, G., & Galán Hernández, J. J. (2024). The pivotal role of interpretability in employee attrition prediction and decision-making. En J. Pelegrín-Borondo, M. Arias-Oliva, K. Murata, & M. Souto Romero (Eds.), The leading role of smart ethics in the digital world (pp. 265–275). Universidad de La Rioja.
NO SIN FINANCIACIÓN
DS Docta Complutense
RD 15 ene 2026