LAION Round 2: Machine-Readable but Still Not Actionable — The Lack of Progress on TDM Opt-Outs - Part 1

Last week, the OLG Hamburg provided the first genuinely substantive judicial engagement with what constitutes a machine-readable rights reservation under Article 4(3) of the DSM Directive in an AI context. While EU courts have so far been consistent in treating the use of copyrighted works for AI training as a form of text and data mining, the question of how rightholders can validly opt out of the general TDM exception has largely remained underdeveloped. Until now, references to “machine readability” have appeared mostly in passing, without courts being required to articulate concrete criteria or to assess specific signalling mechanisms.

Prior to Hamburg, only two national courts had substantively engaged with the question of how a valid Article 4 opt-out must be expressed —and those decisions point in markedly different directions. In late 2024, in DPG Media v. HowardsHome, the Amsterdam District Court held that while Article 4(3) does not mandate a single technical standard, it nevertheless requires that a reservation be practically detectable and processable by automated systems. By contrast, in October 2025 the Danish Maritime and Commercial Court held that a prohibition on scraping or data mining set out in a publicly accessible HTML privacy or data policy could qualify as an “appropriate” reservation under Article 4(3), effectively equating public online accessibility with machine readability. Neither of these cases concerned the use of protected works for AI training.

The ruling of the OLG Hamburg in Kneschke v. LAION arises in a different procedural and analytical context. Deciding on appeal, the court largely confirmed the outcome reached by the Landgericht Hamburg in 2024: the use of the contested photographs by LAION in the construction of the LAION-5B dataset was covered not only by the German implementation of the Article 3 exception for TDM for scientific research (§ 60d UrhG), but also by the general TDM exception in Article 4 DSM (§ 44b UrhG). It is important to note, however, that while the LAION-5B dataset is used in the training of AI models, the contested reproductions themselves were not part of what is commonly understood as AI training. Rather, they were reproductions used as input to a trained model employed by LAION to determine whether the image descriptions in its dataset accurately describe the referenced images.

The LG Hamburg had confined its original analysis to Article 3, finding that LAION qualified as a scientific research organisation and that the computational analysis at issue constituted TDM for scientific purposes. It nevertheless suggested that Article 4 would in any event not apply, on the basis that the photographs had been published on a platform whose terms and conditions contained a reservation of rights, which the court considered could be regarded as machine-readable in light of the ability of large language models to process unstructured text.

Machine-readable = machine-actionable

Interestingly, in last week’s judgment the OLG Hamburg directly addressed the applicability of the German implementation of Article 4 and thus saw a need to establish explicit criteria for what makes a rights reservation machine-readable. At its core, the court argues that a rights reservation should be considered machine-readable if it can be machine-interpreted in such a way that an automated process can use it to block TDM operations. In other words, it needs to be both machine-readable and actionable (own translation):

When it comes to machine readability, it is not only important that the text can be captured by machine, but also that it can be interpreted by machine in such a way that, in an automated process, the content covered by the reservation is not processed

This understanding is largely based on the explanatory memorandum to the German DSM implementation law, which notes that:

The purpose of the provision is, on the one hand, to give rights holders the opportunity to prohibit use on the basis of legal permission. At the same time, the provision aims to ensure that automated processes, which are a typical criterion of text and data mining, can actually be carried out automatically in the case of content accessible online.

Although there is no equivalent justification in the recitals of the DSM Directive, this reading is fully in line with the regulatory intent behind the machine-readability requirement. That requirement only makes sense if it enables the automatic processing of opt-outs.

According to the OLG Hamburg, machine readability is not a static property but must be assessed against technological capabilities at the time of the disputed use. This is an important caveat, as it means that the court’s determination is based on the state of the art in the second half of 2021 (i.e. before the widespread availability of large language models with the capacity to automatically extract meaning from unstructured text). Keeping this in mind, it seems doubtful whether a court would reach the same conclusion for a dispute with an otherwise identical constellation of facts in 2025.

In other words, the ruling of the OLG Hamburg tells us only that plain-language reservations of rights in terms and conditions were not machine-readable in 2021, but it tells us comparatively little about what constitutes machine readability in 2025.

A question of vocabulary?

In its judgement the court also highlights the fact that the reservation of rights in this specific case did not mention TDM verbatim, but that it had to be “interpreted to determine whether it also excludes this use, which requires machine text comprehension.” This points to a deeper challenge for implementing machine-readable opt-outs at scale: For this approach to work there needs to be a shared understanding of key concepts. While the AI regulatory framework provides a clear definition of the concept of TDM(by way of the definition in Article 2(2) of the CDSM directive) many other concepts that could be used to more specifically target the scope of rights reservations currently lack definitions that have a common understanding among all stakeholders involved. 

The LAION ruling thus clarifies the legal boundary conditions for machine-readable opt-outs, but it also exposes a gap between legal requirements and their technical implementation. Part 2 turns to what happens when that gap is filled not by courts, but by competing vocabularies, standards initiatives, and platform-led signalling mechanisms.

Image by Paul Keller