What question did this study set out to answer?

Examine how data quality impacts the development and trustworthiness of AI systems through a collaborative framework.

January 17, 2026Open Access

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

Puntos clave

Examine how data quality impacts the development and trustworthiness of AI systems through a collaborative framework.
Joint contributions from five institutions within the FAIR project.
Focus on the interdisciplinary approach of data-centric methodologies.
Collaboration across various spokes of the FAIR project to gather insights.
Highlight the critical role of data quality in AI development.
Demonstrate the collaborative enhancement of AI methodologies through diverse institutional input.
Emphasize the need for robust and trustworthy AI systems supported by high-quality data.

Resumen

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.

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