Applied Stochastic Models in Business and Industry has just published a Special Issue on Explainable Data Science Techniques, guest edited by Rosanna Verde, Paola Cerchiello, Antonella Plaia and Silvia Salini. Their Foreword is free to read here.

This Special Issue of  ASMBI originated from the conference of the Italian Group for Statistics and Data Science of the Italian Statistical Society, held in Palermo on 11–12 April 2024. The conference brought together researchers from academia and industry to discuss recent challenges and emerging perspectives in Statistics and Data Science, highlighting the central role of statistical methodologies within the rapidly evolving data science landscape. Particular emphasis was placed on the strong synergy among the diverse scientific disciplines that contribute to the advancement of data science and its applications.

While inspired by the main themes addressed during the conference, this Special Issue was intentionally conceived to include a broader spectrum of contributions, encompassing innovative methodological developments and interdisciplinary applications extending beyond the topics specifically covered during the event.

The papers collected in this volume highlight the growing convergence between advanced statistical learning, explainable artificial intelligence, and data-driven decision-making across a wide range of application domains. Together, they reflect the increasing need for analytical methodologies that are not only accurate and efficient, but also interpretable, transparent, and capable of supporting responsible and trustworthy AI systems.

Overall, the papers in this collection provide a rich and multidisciplinary overview of current advances in statistics, machine learning, explainable AI, and applied data science. The volume emphasizes the importance of combining methodological innovation with interpretability, fairness, and practical relevance in order to support informed and trustworthy decision-making in increasingly complex real-world environments.