Le RGPD dans la pratique : un exercice d’équilibre

Buiding upon prior findings from the IPSAM project, this research aims at studying the impact of Artificial Intelligence (AI) technologies on the central role given to human perception under Intellectual Property (IP) laws anthropocentric framework.   The central research question is whether AI will drive harmonisation of IP similarities, replacing the uncertainty under current case law by machine-made rules that are not faithful to the law, hence untrue. The research focuses on the similarity assessment that must be performed according to EU trademark law, design law, and copyright law (patent law will not be covered). In the frame of this research project, an interdisciplinary team consisting of an IP expert (the Principal Investigator – PI) and a postdoc researches in AI engineering, AI law and Cognitive psychology, will verify empirically whether the development and use of those tools can embed and abide by the judge-made rules for assessing IP similarities. Empirical findings will be collected through four Working Packages (WP) dedicated to the engineering of AI-powered IP similarity assessment tools (WP1), the transparency and explainability of such tools (WP2), the automations biases in their use (WP3), and the challenged they pose to the anthropocentric approach of similarity assessment in IP laws (WP4). Results of this research are expected to fill gaps in the state of the art in an entirely innovative way and to explore new avenues in IP theory and algorithmic regulation.   More info soon.

Cette formation, organisée en collaboration entre l’UCLouvain – Saint-Louis Bruxelles et l’ULB est offre une exploration approfondie des questions juridiques liées à l’art et au marché de l’art dans le cadre du droit, principalement belge et européen. Le marché de l’art est en constante évolution : avec ses transactions complexes, ses enjeux de propriété intellectuelle, ses transferts d’œuvres, etc. Une expertise rigoureuse permet de garantir la sécurité du statut, de la propriété, de la détention, des cessions et transactions des oeuvres d’art. Au cours de cette formation, des sujets très variés seront abordés : du statut d’artiste aux obligations pour la lutte contre le blanchiment, de la numérisation des collections à la restitution d’œuvres spoliées, etc. L’équipe enseignante est composée d’académiques, notamment des membres du JurisLab, mais aussi d’expert·es et de praticien·nes de haut niveau, ce qui assure une qualité scientifique du programme tout en apportant une connaissance de terrain très pointue. Découvrez le programme de la première édition dans la brochure 2026.

T. Vandamme, Algorithmic Confusion: A Transversal Study of Computational Trade Mark Similarity, Doctoral Thesis in Engineering and Technology, ULB, 2025, 227 p. Supervisors: Julien Cabay (JurisLab), Olivier Debeir (LISA) Jury : Jean-Marc Deltorn (Université de Strasbourg), Dev Gangjee (University of Oxford), Aniket Kesari (Fordham University, NYC), Matei Mancas (Université de Mons), Andrée Puttemans (ULB), Dimitris Sacharidis (ULB) Recent advancements in Artificial Intelligence (AI) have generated significant optimism across various fields, including Trade Mark (TM) Law. In particular, AI-driven TM retrieval systems promise to streamline the registration process by accurately identifying similar prior marks. Within the European Union, this process hinges on the complex legal test known as the Likelihood of Confusion (LoC), which includes a multifactorial assessment of the similarity of the signs. This thesis undertakes a comprehensive, transversal study to evaluate how effectively current AI systems can translate this central aspect of the legal standard into functional algorithms. By conducting a comparative analysis of two closed-source TM search engines provided by public Intellectual Property offices (namely the Benelux Office for Intellectual Property – BOIP and the European Union Intellectual Property Office – EUIPO), alongside an in-depth review of the state of the art algorithm, we assess the true capabilities of these systems and expose major methodological flaws in the development of those technologies. We curate and release two new benchmark datasets rooted in case law, aimed at enhancing the relevance and precision of these AI tools. This thesis raises the confusion around those algorithms and their ability to perform the ad-hoc similarity assessment at the heart of the confusion test, while also offering insights into the challenges and limitations of algorithmic regulation and the indispensable need for AI accountability.