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

This record corresponds to the accepted manuscript (post-print) of the following conference book chapter:

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

This chapter analyzes the growing importance of interpretability in machine learning models applied to employee attrition prediction and organizational decision-making. It highlights the limitations of black-box models in human-centered contexts and emphasizes the role of Explainable Artificial Intelligence (XAI) in enhancing transparency, accountability, and trust. The study further explores the integration of decision-making methods, such as the Analytic Hierarchy Process (AHP), to support ethical and human-in-the-loop decision frameworks.

The final published version is available at the publisher’s website:
https://dialnet.unirioja.es/servlet/articulo?codigo=9537214

This deposit is made for open access and dissemination purposes, in accordance with the publisher’s self-archiving policy.