Maximilian Gahntz

Dick A. H. Blankvoort, Harshvardhan J. Pandit, Maximilian Gahntz

The AI Act's Article 53(1)(d) requires providers of general-purpose AI (GPAI) models to publish a sufficiently detailed public summary about the content used for training based on a template provided by the AI Office. The stated goal of this obligation is to increase transparency regarding the data used for training GPAI models, and to enable relevant stakeholders to exercise their rights, especially regarding IP, copyright, and data protection. This paper provides a quality assessment framework to assess the public summary across two key dimensions: \textit{transparency} regarding information being provided in a clear, comprehensive, and sufficiently detailed manner; and \textit{usefulness} regarding whether the provision of the document and the contents can be effectively utilised by stakeholders to carry out rights related actions. This framework enables identification of key issues in public summaries, and provides a structured and research-based method to compare practices across public summaries and providers. It also enables authorities such as the AI Office to identify potential issues that could emerge and provides actionable recommendations and guidelines for providers to develop public summaries with high quality. The paper provides an assessment of 5 public summaries published as of 12th January 2026 which were found through an exhaustive search process. To disseminate these findings as a public resource, the paper also describes the development of a website where the assessments, outcomes, and methodologies will be shared.

Stefan Baack, Stella Biderman, Kasia Odrozek et al. · huggingface

Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.