Data-Centric AI Manifesto: How Data Quality Drives Modern AI

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Authors: Donato Malerba, Antonella Poggi, Mario Alviano, Tommaso Boccali, Maria Teresa Camerlingo, Roberto Maria Delfino, Domenico Diacono, D. Elia, Vincenzo Pasquadibisceglie, Mara Sangiovanni, V. Spinoso, Gioacchino Vino
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Abstract

This white paper represents the joint outcome of five partners within the FAIR project, spanning four different spokes, who collectively contributed to the activities of Transversal Project 7 (FAIR TP7 – Data-Centric AI and Infrastructures). The contributing institutions are: University of Bari Aldo Moro (Spoke 6), University of Calabria (Spoke 9), University of Naples Federico II (Spoke 3), University Sapienza of Rome (Spoke 5), and INFN – National Institute of Nuclear Physics (Spoke 6). Their coordinated effort reflects the interdisciplinary and multi-institutional nature of FAIR, reinforcing the central role of data-centric methodologies in advancing trustworthy and robust AI systems.

Fields of Study & Subjects

Data Quality and Management Ethics and Social Impacts of AI Machine Learning and Data Classification

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 January 2026
Language
EN