Bram Rijsbosch

Lucas G. Uberti-Bona Marin, Bram Rijsbosch, Kristof Meding et al.

Technical and legal debates frequently suggest that "accuracy" is an objective, measurable, and purely technical property. We challenge this view, showing that evaluating AI performance fundamentally depends on context-dependent normative decisions. These techno-normative choices are crucial for rigorous AI deployment, as they determine which errors are prioritised, how risks are distributed, and how trade-offs between competing objectives are resolved. This paper provides a legal-technical analysis of the choices that shape how accuracy is defined, measured, and assessed, using the 2024 European Union AI Act -- which mandates an "appropriate level of accuracy" for high-risk systems -- as a primary case study. We identify and analyse four choices central to any robust performance evaluation: (1) selecting metrics, (2) balancing multiple metrics, (3) measuring metrics against representative data, and (4) determining acceptance thresholds. For each choice, we study its relationship to the AI Act's accuracy requirement and associated documentation obligations, show how its technical implementation embeds implicit or explicit assumptions about acceptable risks, errors, and trade-offs, and discuss the implications for the practical implementation of the AI Act by examples and related technical standards. By making the techno-normative dimensions of accuracy explicit, this paper contributes to broader interdisciplinary debates on AI governance and regulation, and offers specific guidance for regulators, auditors, and developers tasked with translating (legal) safety requirements into technical practice.

Bram Rijsbosch, Gijs van Dijck, Konrad Kollnig

AI-generated images have become so good in recent years that individuals often cannot distinguish them any more from "real" images. This development, combined with the rapid spread of AI-generated content online, creates a series of societal risks. Watermarking, a technique that involves embedding information within images and other content to indicate their AI-generated nature, has emerged as a primary mechanism to address the risks posed by AI-generated content. Indeed, watermarking and AI labelling measures are now becoming a legal requirement in many jurisdictions, including under the 2024 European Union AI Act. Despite the widespread use of AI image generation systems, the practical implications and the current status of implementation of these measures remain largely unexamined. The present paper therefore provides both an empirical and a legal analysis of these measures. In our legal analysis, we identify four categories of generative AI deployment scenarios and outline how the legal obligations could apply in each category. In our empirical analysis, we find that only a minority number of AI image generators currently implement adequate watermarking (38%) and deep fake labelling (18%) practices. In response, we suggest a range of avenues of how the implementation of these legally mandated techniques can be improved, and publicly share our tooling for the detection of watermarks in images.