Work

Enhancing AI applications for European public bodies: A data quality-centric approach

Year: 2025

Type: article

Abstract: Artificial Intelligence (AI) has become a pivotal tool in the digital transformation of organisations across all sectors and types of organisations, including public bodies. For these, AI offers significant potential benefits such as improving the efficiency of internal operations, the effectiveness of policymaking, the responsiveness of public services, and enhancing transparency and accountability. The quality of the data used to train AI models is a key factor for the success or failure of an AI application. The main contribution of this work lies in the adoption of a data-centric approach, through which critical categories of data quality alerts are identified and related to inherent data quality characteristics for machine learning and analytics. In addition, current ISO standards and European regulations concerning AI and data quality are considered, highlighting both their technical foundations and existing limitations.

Source: International Journal of Engineering Business Management

DOI: 10.1177/18479790251367820

Authors: Mariano Gómez Plaza, Francisco Javier Granados Cañete, Elvis Xavier López Yajamin, David Díaz Martín, Beatriz Olalla Caballero, Antonio Manuel Palma Bautista

Institution: Ingeniería de Sistemas para la Defensa de España

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,000