Enhancing Objectivity: Advancements in AI-PROFICIENT’s Validation Methodology
Building upon our blog post on our project’s preliminary Validation Methodology, we delve further into the methodology presented in the final version of Deliverable D6.1 within the AI-PROFICIENT project. The aim of this methodology is to measure the results of AI developments in industrial processes with the utmost objectivity. In the initial version, a procedural outline was proposed, and since then, collaborative efforts have been made with project partners, as well as the Ethics team, to refine and mature the initial ideas.
This collaborative work by Laritza Limia Fernandez, Pedro de la Peña, Alexandre Voisin, Kerman Lopez de Calle , Julien Hintenoch, Alexander Vasylchenko, Sirpa Kallio, Christophe Van Loock , Katarina Stanković, Dea Pujić , Vasillis Spais, Karen Fort and Marc Anderson, has led to the development of a comprehensive methodology that successfully validates the project requirements. While the primary focus lies within the AI-PROFICIENT project, the ultimate goal is to create a model that can serve as a foundation for developing methodologies dedicated to validating industrial AI projects as a whole. Consequently, significant efforts have been made to quantify the measurement criteria, ensuring alignment with key performance indicators, functional requirements, and end-user expectations.