Special issue 1
Analysing Business Data with Statistics, Data Science, and AI
scarica qui la pubblicazione
Editors
Fabio Crescenzi, Luigi Fabbris, Andrea Mazzitelli, Alessandra Righi, Alessandro Rinaldi, Maurizio Vichi
Articles
Textual Classification Explained by Counterfactual Analysis in LLMs
Mauro Sodani, Valerio De Camillis
Decostruire l’IA: Tra Paure Pubbliche e Fondamenti Scientifici
Tonio Di Battista
Learning Ontologies of Online Abusive Contents: Seeded LDA and Graph-Based Semantic Structuring of Offensive Anti-migrant Narratives in Italian
Alex Cucco, Lara Fontanella, Annalina Sarra, Sara Fontanella
Synthesizing Knowledge: An Integrated Approach for Extracting Relevant Content from Scientific Literature
Massimo Aria, Corrado Cuccurullo, Luca D’Aniello, Michelangelo Misuraca, Maria Spano
Insights from Italian Tweets: Distributions, Content, Sentiment, Multimedia, and Network Metrics
Domenica Fioredistella Iezzi, Roberto Monte, Daniele Pasquini
Data Science and AI: A Technology Proposal to Improve Statistical Innovation Processes
Francesco Altarocca, Domenico Aprile, Simonetta Cozzi, Armando D’Aniello, Annunziata Fiore, Enrico Orsini, Andrea Pagano
Responsible AI Adoption: How It’s Changing Official Statistics
Gerarda Grippo, Alessandra Righi
Analysing Business Data with Statistics, Data Science, and AI brings together a selection of original contributions that explore how statistical methodology, data science, and artificial intelligence can jointly enhance the analysis and interpretation of business and socio-economic data.
The volume stems from the scientific discussions developed during the international conference “Measuring and Interpreting World Changes with Statistics, Data Science and AI”, held in Rome from 18 to 20 September 2024. The conference was jointly organised by the Association for Applied Statistics (ASA), the Department of Statistical Sciences of Sapienza University of Rome, and the Italian National Institute of Statistics (ISTAT), with the participation of several academic and institutional partners. The event provided a multidisciplinary forum for examining the contribution of statistics, data science, and artificial intelligence to the understanding of contemporary economic and social transformations.
The Special Issue addresses both theoretical and applied perspectives, focusing on the growing availability of complex, high-dimensional, and heterogeneous data generated by firms, institutions, and digital platforms. Particular attention is devoted to the integration of traditional statistical sources with non-traditional data, including administrative records, digital traces, and textual information, as well as to the development of transparent and explainable AI models capable of supporting evidence-based decision making.
The contributions collected in this volume investigate advanced methodological frameworks, empirical applications, and interdisciplinary approaches aimed at improving data-driven strategies in areas such as business analysis, labour markets, innovation processes, and territorial dynamics. By combining classical statistical reasoning with modern machine-learning techniques, the volume highlights both the opportunities and the challenges associated with the adoption of AI-based tools, including issues related to interpretability, data quality, and responsible use of algorithms.
Intended for researchers, practitioners, and policymakers, this Special Issue provides a rigorous and coherent overview of current developments at the intersection of statistics, data science, and artificial intelligence, contributing to the ongoing scientific debate on how quantitative methods can effectively support decision-making processes in complex socio-economic contexts.
This volume is published within the TESI & TEMI editorial series, jointly promoted by Universitas Mercatorum and the Centro Studi delle Camere di Commercio G. Tagliacarne, and reflects their shared commitment—developed in cooperation with the Association for Applied Statistics—to fostering high-quality research and scientific dialogue in applied statistics and business analytics.
