# Legal frictions for data openness: Reflections from a case-study on re-use of the open web for AI training

Ramya Chandrasekhar

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Ramya Chandrasekhar. Legal frictions for data openness: Reflections from a case-study on re-use of the open web for AI training. Centre Internet et Société - CNRS; Inno3; Open Knowledge Foundation. 2025, pp.75. hal-05009616v2

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# Legal frictions for data openness

## Reflections from a case-study on re-use of the open web for AI training

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Training data is key to AI development - particularly Generative AI models and Large Language Models (LLMs). And a significant portion of this training data comes from the open web.

But despite being lauded as a digital commons, the open web is not open for all. It is difficult to 'see' data flows when data and content from the open web is reused to create training datasets, and as these training datasets then move through the various stages of AI development. Legal and policy initiatives for data governance in the AI context often understand data flows as "traceable, stable and contained", when in reality, data re-use is an "inherently entangled phenomenon".

This report investigates legal entanglements of re-use, when data and content from the open web is used to train foundation AI models. Based on conversations with AI researchers and practitioners, an online workshop, and legal analysis of a repository of 41 legal disputes relating to copyright and data protection, this report highlights tensions between legal imaginations of data flows and computational processes involved in training foundation models.

To realise the promise of the open web as *open for all*, this report argues that efforts oriented solely towards techno-legal openness of training datasets are not enough. Techno-legal openness of datasets facilitates easy re-use of data. But, certain well-resourced actors like Big Tech are able to take advantage of data flows on the open web to internet to train proprietary foundation models, while giving little to no value back to either the maintenance of shared informational resources or communities of commoners. At the same time, open licenses no longer accommodate changing community preferences of sharing and re-use of data and content.

In addition to techno-legal openness of training datasets, there is a need for certain limits on the extractive power of well-resourced actors like BigTech combined with increased recognition of community data sovereignty. Alternative licensing frameworks, such as the Nwulite Obodo License, Kaitiakitanga Licenses, the Montreal License, the OpenRAIL Licenses, the Open Data Commons License, and the AI2Impact Licenses hold valuable insights in this regard. While these licensing frameworks impose more obligations on re-users and necessitate more collective thinking on interoperability, they are nonetheless necessary for the creation of healthy digital and data commons, to realise the original promise of the open web as *open for all*.
## Authors, reviewers and other contributors

This report is authored by Ramya Chandrasekhar, and co-designed with Célya Gruson-Daniel, Mélanie Dulong de Rosnay and Renata Ávila. We are also grateful to interviewees as well as participants of the online workshop conducted on November 25, 2024, for their inputs and feedback.

## Funding acknowledgments

This report is part of ODECO - a European research project for creating a Sustainable Open Data ECOsystem.

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569.

This report also contributes to SUDACO – a collaboration between the Open Knowledge Foundation and the Centre Internet et Société of the CNRS, to create legal, technical and governance tools for sustainable data commons.

# From Open Data to Sustainable Data Commons

## License

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CC BY-SA 4.0

This report is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
# Table of contents

<table><tr><td>Table of contents</td><td>3</td></tr><tr><td>List of Figures</td><td>4</td></tr><tr><td>List of Tables</td><td>4</td></tr><tr><td>I. Introduction</td><td>5</td></tr><tr><td>A. Structure of this report</td><td>7</td></tr><tr><td>B. Methodology</td><td>7</td></tr><tr><td>II. Mining the forests of the open web for training data</td><td>8</td></tr><tr><td>A. Categorising training data sources</td><td>10</td></tr><tr><td>B. Typical training data techniques</td><td>12</td></tr><tr><td>C. Balancing quantity and quantity</td><td>12</td></tr><tr><td>D. The data-power of well-resourced actors like Big Tech</td><td>13</td></tr><tr><td>III. The law and political economy of data openness: A three-dimensional approach</td><td>16</td></tr><tr><td>A. Copyright and openness</td><td>16</td></tr><tr><td>1. Politics of the inverse logic</td><td>17</td></tr><tr><td>2. Giving back to the commons</td><td>18</td></tr><tr><td>B. The open trilemma in web scraping</td><td>20</td></tr><tr><td>C. Openness beyond copyright</td><td>22</td></tr><tr><td>D. Conceptualising a three-pronged approach to data openness</td><td>24</td></tr><tr><td>IV. Applying our data openness framework to make sense of ongoing legal controversies</td><td>26</td></tr><tr><td>A. Copyright (and it's cousin, the sui generis database right)</td><td>27</td></tr><tr><td>1. When creating a training dataset, is there any "copying"?</td><td>29</td></tr><tr><td>2. When a training dataset is created by scraping, is that legal?</td><td>30</td></tr><tr><td>3. When a model "learns" parameters from a training dataset, is this a derivative?</td><td>33</td></tr><tr><td>4. What about generative outputs? Does this trigger "copying" or "dissemination to the public of protected works"?</td><td>33</td></tr><tr><td>B. Data protection</td><td>35</td></tr><tr><td>1. Removing personal data from training datasets v. justifying the inclusion of personal data in training datasets</td><td>36</td></tr><tr><td>2. Exercising data rights, including the right to deletion</td><td>41</td></tr><tr><td>C. To sum up...</td><td>42</td></tr><tr><td>V. Licenses - enabling, reshaping and contesting data openness</td><td>44</td></tr><tr><td>A. Alternative licensing frameworks for training datasets</td><td>47</td></tr><tr><td>1. New iterations of copyleft</td><td>48</td></tr><tr><td>2. New bundle of rights, and associated use restrictions</td><td>49</td></tr><tr><td>3. Using licenses to balance techno-legal data openness with data protection</td><td>49</td></tr><tr><td>B. New licensing frameworks - license proliferation, but no enforcement?</td><td>50</td></tr><tr><td>1. Data sharing licenses and contracts...but only for the benefit of Big Tech and AI companies?</td><td>50</td></tr><tr><td>2. New institutional structures for enforcement</td><td>52</td></tr><tr><td>3. New concerns for interoperability</td><td>53</td></tr><tr><td>E. In conclusion...</td><td>54</td></tr><tr><td>VI. Towards healthy data commons, and the need for some frictions in data flows</td><td>55</td></tr><tr><td>A. Context-specific legal strategies for data openness</td><td>56</td></tr><tr><td>B. Data commons for openness of the web, and legal frictions for healthy data commons</td><td>57</td></tr><tr><td>VII. References</td><td>60</td></tr></table>
List of Figures

<table><tr><td>Figure 1: Three-dimensional framework on data openness for training data</td><td>6</td></tr><tr><td>Figure 2: OECD AI Lifecycle</td><td>8</td></tr><tr><td>Figure 3: Socio-technical AI lifecycle</td><td>9</td></tr><tr><td>Figure 4: Graphical representation of the open trilemma</td><td>20</td></tr><tr><td>Figure 5: Three-dimensional framework for data openness of training data</td><td>24</td></tr><tr><td>Figure 6: Visualisations of our repository of ongoing legal controversies around AI training</td><td>26</td></tr><tr><td>Figure 7: Screenshot of a miro board used during our workshop (zoom to read the text)</td><td>28</td></tr><tr><td>Figure 8: Visual representation of copyright concerns along the OECD AI lifecycle</td><td>28</td></tr><tr><td>Figure 9: Visual representation of data protection risks across the OECD AI lifecycle</td><td>36</td></tr><tr><td>Figure 10: Potential sources of personal data in training datasets</td><td>37</td></tr><tr><td>Figure 11: Sankey diagram of alternative licensing initiatives and their relevance for data openness framework</td><td>47</td></tr></table>

List of Tables

<table><tr><td>Table 1: 4 broad categories of data, relevant for data governance in AI</td><td>10</td></tr><tr><td>Table 2: Public information on training data sources of some foundation models</td><td>11</td></tr></table>
# I. Introduction

In November 2022, a group of anonymous open-source software developers filed a class action suit against OpenAI, GitHub and Microsoft in a district court in the United States of America. Their complaint was targeted at CoPilot (a generative AI product for software code) and Codex (a generative artificial intelligence model that is integrated into CoPilot). They argue that CoPilot and Codex are trained on large volumes of openly licensed code that is hosted by developers on GitHub, and such training is in violation of the license terms attached by the developers to their code. This case is often touted as the first class action copyright case against a generative artificial intelligence (AI) company.<fnref n="1" />

Concluding remarks from the plaintiffs’ latest amended complaint are particularly relevant –

> “Future AI products may represent a bold and innovative step forward. GitHub Copilot and OpenAI Codex, however, do not. Defendants should not have released these products until they could ensure that they did not constantly violate Plaintiffs’ and the Class’s intellectual-property rights, licenses, and other rights.

> Defendants have made no attempt to comply with the open-source licenses that are attached to much of their training data. Instead, they have pretended those licenses do not exist, and trained Codex and Copilot to do the same. By simultaneously violating the open-source licenses of tens-of-thousands—possibly millions—of software developers, Defendants have accomplished software piracy on an unprecedented scale. As Microsoft’s Co-Founder Bill Gates once said regarding software piracy: “the thing you do is theft.”

> There is no inherent limitation or constraint of AI systems that made any of this necessary. Defendants chose to build AI systems designed to enhance their own profit at the expense of a global open-source community that they had once sought to foster and protect. GitHub and OpenAI are profiting at the expense of Plaintiffs’ and the Class’s rights.”<fnref n="2" /> (emphasis added)

<footnote n="1">¹ Although, the claims in the case relate to breach of contract and alternation of copyright management information.</footnote>

<footnote n="2">² Second amended complaint at 201-203, J. Doe 1, J. Doe 2, J. Doe 3, J. Doe 4, And J. Doe 5 v. GitHub Inc and Others, Docket No. 4:22-cv-06823 (N. D. Cal. 2022).</footnote>
Training data is key to Generative AI models and Large Language Models. These types of AI models (also known as foundation models) learn patterns from large training datasets, and rely on these patterns to provide multimodal responses to diverse types of prompts. To meet their large training needs, AI developers often rely on the open web for training data.

*But despite being lauded as a digital commons, the open web is not open for all.*

The open web enables access to open datasets as well as the digital public domain, both of which are used to train foundation AI models. However, the distributional effects of these shared informational resources are not equal. Certain well-resourced actors like Big Tech are able to take advantage of data flows on the open web to internet to train proprietary foundation models, while giving little to no value back to either the maintenance of shared informational resources or communities of commoners. On the other hand, simply because an AI model is trained on the open web, the model is not automatically more representative of various realities. Training datasets, foundation models and their outputs are inherently social and political processes, where some “*normative patterns of life are assumed, supported and reproduced.*”<fnref n="3" />

It is difficult to ‘see’ data flows, when data from the open web are reused to create training datasets, and as these training datasets then move through the various stages of AI development. But, legal efforts to instantiate more openness and transparency to data collection and data use in the AI often understand data flows as “*traceable, stable and contained*”, when in reality, data re-use is an “*inherently entangled phenomenon*”.<fnref n="4" />

**Against this context, this report investigates legal strategies for data openness, when training datasets are built by re-using data from the open web.**

By “data”, this report refers to datasets and creative content accessible via the open web. Based on this broad understanding of data, this report argues that in addition to techno-legal openness of training datasets created from open web resources, it also important to place limits on data extractivism by Big Tech as well as enable community data sovereignty. To this extent, a mix of contextual legal strategies are necessary to engender more transparency and participation around data re-use for AI training.

<figure><img src="image_5.png" /><figcaption>Figure 1: Three-dimensional framework on data openness for training data</figcaption></figure>

<footnote n="3">³ Crawford and Paglen, “Excavating AI”; Orr and Crawford, “The Social Construction of Datasets.”</footnote>

<footnote n="4">⁴ Thylstrup et al., “Politics of Data Reuse in Machine Learning Systems.”</footnote>
## A. Structure of this report:

This report is divided into five parts:

<figure><img src="image_6.png" /></figure>

You can click on each tile to access the corresponding section of our report

## B. Methodology

Over the course of 2024, we conducted extensive desk research to identify ongoing legal controversies relating to copyright and data protection aspects of training datasets for foundation models, as well as new licensing initiatives. We also conducted (i) 9 semi-structured interviews conducted with 11 AI practitioners and policymakers based in Europe and North America, and (ii) a participative workshop conducted on November 25, 2024 with the participation of AI practitioners, researchers and policymakers from Europe, North America, UK, and Sub-Saharan Africa.

From our desk research and qualitative methods, we created a repository of 41 legal controversies relevant for data openness of training data, with cases from the US, Europe, United Kingdom, India, Brazil, Canada and China. Our repository is openly licensed, and available here.

We also study the use of existing open and permissive licenses (original developed for code, datasets and creative content) for data openness, as well as 5 alternative licensing frameworks – the Nwulite Obodo License, Kaitiakitanga Licenses, the Montreal License, the OpenRAIL Licenses, the Open Data Commons License, and the AI2Impact Licenses.
## II. Mining the forests of the open web for training data

This report focusses on “foundation models” – i.e. AI models that rely on large amounts of training data, as well as large quantities of computational resources for processing this training data.<fnref n="5" /> Typically through deep learning (albeit not limited to this technique), these models “learn” parameters from broad training data, and respond to prompts using these parameters. Increasingly, foundation models learn from multimodal data and thereby provide multimodal responses – ranging from text, code, audio, and visuals.

Rishi Bommasani and others use the word “foundational” to convey the general-purpose nature of these AI models – where the resources involved in building foundation models is high, but using these models requires fewer resources and as a result, they have a wide range of use-cases.<fnref n="6" /> Generative AI models and Large Language Models are some examples of foundation models.

Per the OECD, there are multiple phases in the development of foundation models.<fnref n="7" />

<figure><img src="image_7.png" /><figcaption>Figure 2: OECD AI Lifecycle</figcaption></figure>

At the data collection stage (often also referred to as the pre-training stage in technical parlance), model developers identify initial needs, and either build broad training datasets or acquire existing training datasets. Using these broad datasets, developers build a base model. At the testing stage, these base models are refined (i.e. aligned) using more specific datasets, resulting in foundation models. In many cases, foundation models are further refined (i.e. fine-tuned) using context-specific datasets, depending on the proposed use-case for such foundation models.

A large majority of the data and content that constitute datasets used at the training, aligning and fine-tuning stages of foundation AI model development come from the treasure trove that is Internet. For instance, Saffron Huang and Divya Siddarth point to two aspects of digital and data commons that are central to foundation models – the online commons of informational resources, and the collective material and decision-making infrastructures that underpin these commons.<fnref n="8" />

In terms of artefacts, foundation models are trained on curated data collections known as training datasets. These training datasets are created by locating creative works on the Internet, converting these creative works in digital data, and then organizing these digital

<footnote n="5">⁵ Bommasani et al., “On the Opportunities and Risks of Foundation Models.”</footnote>

<footnote n="6">⁶ Bommasani et al., “The Foundation Model Transparency Index,” 9</footnote>

<footnote n="7">⁷ OECD.AI, “AI Principles Overview.”</footnote>

<footnote n="8">⁸ Huang and Siddarth, “Generative AI and the Digital Commons.”</footnote>
data into categorisations that come to be known as training datasets.<fnref n="9" /> This process applies to alignment and fine-tuning datasets, and as a result, we use the phrase “training data” and “training datasets” to include all these datasets.

But beyond training datasets as artefacts, Rishi Bommasani and others note, “people are the ultimate source of training of a foundation model, but also the downstream recipients of any benefits and harms.”<fnref n="10" /> As a result, in this report we analyse training data in terms of the *what* (i.e. the data and content resources from the open web that are re-used to build training datasets) and the *who* (i.e. individuals and communities who have either a right or an interest in these data and content resources).

<figure><img src="image_8.png" /><figcaption>Figure 3: Socio-technical AI lifecycle<fnref n="11" /></figcaption></figure>

This socio-technical approach to training data is also at the heart of research from Science and Technology Studies, that sees data re-use in machine learning as an “inherently entangled phenomenon”. Nanna Bonde Thystrup and others argue that data re-use is not a linear process along the data supply chain, but that data re-use are phenomena that “emerge within and are contingent upon, the techno-scientific apparatus in which [data] appear,” and that “machine learning regimes do not simply reuse individual and intact previous data, but rather decompose and recompose data into non-linear and iterative forms.”<fnref n="12" />

This socio-technical approach to training data also reveals the human labour in training datasets. Data and content from the open-web is not directly compiled into a training dataset; there are instead multiple micro-tasks involved such as labelling images or translating bits of text – often referred to as annotation.<fnref n="13" /> Typically at the pre-training stage (often through the use of self-supervised learning techniques), model developers rely on curated collections of data, while at the training and fine-tuning stage, model

<footnote n="9">⁹ Lee, Cooper, and Grimmelmann, “Talkin’ ‘Bout AI Generation,” 5.</footnote>

<footnote n="10">¹⁰ Bommasani et al., “On the Opportunities and Risks of Foundation Models,” 7.</footnote>

<footnote n="11">¹¹ Id.</footnote>

<footnote n="12">¹² Thystrup et al., “Politics of Data Reuse in Machine Learning Systems,” 8.</footnote>

<footnote n="13">¹³ Tubaro and Casilli, “Portraits of Micro-Workers: The Real People behind AI in France.”</footnote>
developers used supervised training datasets, i.e. annotated, labelled and curated collections of data.<fnref n="14" />

A combination of large and good quality training datasets, combined with better training and more compute, has scaled foundation models to hundreds of billions of parameters.<fnref n="15" /> These foundation models are often directly licensed for consumer-use. These foundation models are also bundled into an interface (such as a chatbot) or increasingly integrated into search engines<fnref n="16" />, social networking platforms and messaging services<fnref n="17" />) and thereby put on the market.

## A. Categorising training data sources

A recent report released by Open Future and the Open Source Initiative, identifies four categories of data for which data governance models are necessary, including in relation to training foundation AI models –

<figure><img src="image_9.png" /><figcaption>Table 1: 4 broad categories of data, relevant for data governance in AI</figcaption></figure>

In our report, we are interested in training data obtained from the open web – which sits across “open data”, “public data” and “obtainable data” in the Table 1 above. This includes:

* Creative works such as books, artworks, audio and other creative content, that are protected by copyright, but accessible via the open web

* Openly licensed creative content or datasets, such as content under Creative Commons licenses or ODBL licenses, including copyleft licenses

* Open government data accessible via the open web

<footnote n="14">¹⁴ Privacy International, “Humans in the AI Loop.”</footnote>

<footnote n="15">¹⁵ Badshah and Sajjad, “Quantifying the Capabilities of LLMs across Scale and Precision.”</footnote>

<footnote n="16">¹⁶ See for example, Google’s use of transformer model BERT to power Google search and more recently, the integration of Bard into Google Search. Pichai, “An Important next Step on Our AI Journey.”</footnote>

<footnote n="17">¹⁷ See for example, Meta’s integration of Llama models into Instagram, Facebook and Whatsapp. Meta, “Meta’s AI Products Just Got Smarter and More Useful.”</footnote>
* Content in the digital public domain, i.e. content whose copyright has lapsed or that has been dedicated to the public domain

* Content from webpages accessible via the open web, but whose use may be subject to website terms of service or other contractual requirements

* Personal data, either included in or inferred from these sources of publicly-available data on the open web

**We use the word 'data' broadly in the rest of this report, to refer to all these types of data and content from the open web.** And our selection of data sources aligns well with what developers of foundation models identify as their typical training data sources.

<table><caption>Table 2: Public information on training data sources of some foundation models</caption><tr><td>GPT-4</td><td>“GPT-4 is a Transformer-style model pre-trained to predict the next token in a document, using both publicly available data (such as internet data) and data licensed from third-party providers.”<br/>“GPT-4 has learned from a variety of licensed, created, and publicly available data sources, which may include publicly available personal information.”<br/>OpenAI, GPT-4 Technical Report (2024), https://arxiv.org/abs/2303.08774</td></tr><tr><td>Meta AI</td><td>“Since it takes such a large amount of data to teach effective models, a combination of sources are used for training. We use information that is publicly available online and licensed information. We do not use the content of your private messages with friends and family to train our AIs unless you or someone in the chat chooses to share those messages with our AIs.”<br/>Meta, Privacy Center, https://www.facebook.com/privacy/genai/<br/>“We use [your] public information on Facebook and Instagram to develop and improve generative AI models for our AI at Meta features and experiences.” (Public information is defined to include your “Name, Facebook and Instagram username, Profile picture, Activity on public groups, Facebook Pages and channels, Activity on content that is public. For example, your comment, rating or review on Marketplace, or on a public Instagram account. Avatars”)<br/>“Your interactions with AI features can be used to train AI models. Examples include messages to AI chats, questions you ask and images you ask Meta AI to imagine for you.”<br/>“Here are some additional examples of sources of information we use to develop and improve AI at Meta.<ul><li>Public information - Publicly available sources on the internet like public blog posts or data bases</li><li>Information from partners - Information that comes from selected partners, like search engines</li><li>Licensed information - Content that someone else, like an author or publisher, holds the rights to and gives Meta permission to use”</li></ul>Meta, Privacy Center, https://www.facebook.com/privacy/dialog/information-used-for-ai-at-meta</td></tr><tr><td>Stable Audio Open</td><td>“Given that, our goal is to release a text-conditioned generative model for non-speech audio based on the following: Trained only on Creative Commons (CC) licensed audio.”<br/>Evans et al, Stable Audio Open (2024), https://arxiv.org/abs/2407.14358</td></tr></table>
## B. Typical training data techniques

A series of techno-legal processes are essential for identifying, sourcing and converting data from the Internet into training datasets. While of course these involve a range of computational actions (such as building algorithms for web scraping, tagging of data, etc), this report focuses on the broader techno-legal processes involved. These include, for example:

* Use of web-scraping algorithms/bots. These algorithms are required to respect text-and-data-mining opt-outs by website owners.

* Use of dataset hosting platforms/repositories, like Kaggle or Hugging Face or GitHub, which involves agreeing to the terms of use of such platforms/repositories.

* Obtaining datasets from data holders through contractual data sharing agreements.

* Building training datasets through voluntary data donations from individuals/data holders. Mozilla's *Common Voice* dataset, for instance, was created through individual donations of text/voice fragments.

While AI developers do create their own training datasets, there also exist numerous training datasets (both supervised and unsupervised) which are used by AI developers. Examples of some popular training datasets include *Books3* (digital copies of books, now defunct); The Pile (English language text dataset, now removed); *CommonCrawl* (webpages), *CommonCorpus* (words), *Common Voice* (voice samples), *ImageNet* (annotated images) and *LAION 5B* (image-text pairs).

## C. Balancing quantity and quantity

The notion of “openness” is often presented as a core characteristic of the Internet, embedded in its history, its architecture and its governance. This feature of openness was adopted by the open government, open data and open science movements as well, whose proponents want data and information to flow freely over the Internet, and thereby engender more democratic transparency.<fnref n="18" />

As a result, on the one hand, the Internet constitutes fertile land for mining data that are used to create training datasets. For instance, *ImageNet* dataset is one of the largest and earliest annotated datasets of more than 14 million images with corresponding text descriptions that is used to train AI models for image and object recognition. This dataset was created by scraping the Internet and curating the scraped data.¹⁹ Similarly, *Common Crawl* is a regularly-updated dataset of web-crawl data, containing raw web page data, metadata extracts, and text extracts.²⁰ Per a report published by Mozilla earlier this year,

<footnote n="18">¹⁸ Fung, “Infotopia: Unleashing the Democratic Power of Transparency”; Wessels et al., *Open Data and the Knowledge Society*.

¹⁹ Deng et al., “ImageNet.”

²⁰ Leetaru, “Common Crawl And Unlocking Web Archives For Research.”</footnote>
out of 47 LLMs for text generation published between 2019 and October 2023, at least 64% of these models (30) used at least one filtered version of Common Crawl for pre-training.<fnref n="21" /> Other research has revealed correlations between larger quantity of training data and improved model performance.<fnref n="22" />

The digital public domain as well as open datasets made available via the Internet also constitutes an important source of training data. For instance, PlelAs – a Paris-based AI startup - has created an open training dataset known as [Common Corpus](https://www commoncorpus org). This is a curated dataset of over 2 trillion tokens, created by combining content from the public domain, permissively licensed open government datasets, open science repositories like OpenAlex and other permissively licensed repositories like Wikipedia dataset releases on HuggingFace and Youtube Commons.

On the other hand, the rapid use of the “entire” Internet for training foundation models has resulted in speculation that the Internet has been “exhausted”, raising the need for new data sources to ensure good quality training data.<fnref n="23" /> Many of our interviewees also stated that developers often scrape and use what they can from the Internet. This has significant implications for data quality, which in turns impacts model robustness.<fnref n="24" /> One interviewee noted that many developers “...try to find stuff that works, that's available, and then assess it for quality.”²⁵Another said that “in my opinion, training is not the hardest thing. It's the data quality, data mining, data sourcing, which is the hardest.”<fnref n="26" />

## D. The data-power of well-resourced actors like Big Tech

Re-use of the open web to build training datasets and thereby train foundation models is further complicated by the monopoly of well-resourced actors like Big Tech in AI.

As highlighted above, foundation model development is extremely resource-heavy. At the training stage, large amount of human labour is required to clean and annotate training datasets. Further, training and building foundation models requires material computing resources like GPUs, datacentres, massive internet connectivity, electricity, precious minerals, and various energy resources. At present, a handful of tech giants exercise a collective monopoly over these resources, and as a result, control large-scale foundation model development.<fnref n="27" /> Big Tech developers outsource data annotation to data workers in the majority world for less than minimum wage.<fnref n="28" /> Further, with the support of conservative governments, Big Tech further consolidate the resources necessary for foundation model development. For instance, in January 2025, Donald Trump launched the Stargate Alliance – a private joint venture between OpenAI, Oracle and Software – for the development of 20 large AI datacentres in the US.<fnref n="29" />

<footnote n="21">²¹ Baack, “Training Data for the Price of a Sandwich.”</footnote>

<footnote n="22">²² See for e.g., Lee et al., “Deduplicating Training Data Makes Language Models Better.”</footnote>

<footnote n="23">²³ See for e.g., The Economist, “AI Firms Will Soon Exhaust Most of the Internet’s Data.”</footnote>

<footnote n="24">²⁴ See for e.g., Hoffmann et al., “Training Compute-Optimal Large Language Models.”</footnote>

<footnote n="25">²⁵ Interview with an AI researcher.</footnote>

<footnote n="26">²⁶ Interview with an AI practitioner.</footnote>

<footnote n="27">²⁷ Riekeles and von Thun, “AI Won’t Be Safe until We Rein in Big Tech.”</footnote>

<footnote n="28">²⁸ Browne, “AI Is Steeped in Big Tech’s ‘Digital Colonialism.’”</footnote>

<footnote n="29">²⁹ Smith-Goodson and Kimball, “The Stargate Project.”</footnote>
Given the resources necessary to re-use the open web to train foundation AI models and the concentration of these resources in the hands of well-resourced actors like Big Tech, it is clear that the open web disproportionately benefits these actors. These actors have also enabled the spread of the Silicon Valley mantra of “one-model-fits-all” to other geographies, which further compounds this problem. While more countries in the Global South integrate Big Tech foundation models into public administration, healthcare and education, these models are developed in Global North contexts and therefore, risk deepening the digital divide between the Global North and the Global South.<fnref n="30" /> For instance, researchers have identified that publicly-available datasets used to train healthcare AI models contain more data from patients in Europe, North America, and China, which limits the accuracy of these models in countries with lower incomes.<fnref n="31" /> Some countries like India have also launched projects to build their own foundation models, instead of devoting resources to small task-specific models.<fnref n="32" />

Growing concerns around the exploitation of the open web by large players like OpenAI as well as Big Tech actors, has also led to more websites imposing restrictions on scraping and use of their content, which is creating siloes on the Internet.<fnref n="33" /> Even where training datasets are released under permissive licenses / permissive terms of use, such as Common Crawl or ImageNet, Big Tech actors use these open resources to create proprietary foundation models and do not contribute any value back to the maintenance of these open resources. For instance, researchers have documented the presence of hate speech and sexually explicit and harmful content in Common Crawl<fnref n="34" /> as well as non-consensual sexual images in the ImageNet dataset<fnref n="35" />. Where AI developers engage in filtering of datasets, Stefan Baack notes that these practices are not adequate to detect problematic content (which include harmful content as well as copyright infringing content), let alone remove such content.<fnref n="36" /> Given that an overwhelming majority of foundation models developers are Big Tech actors whose content moderation policies leave a lot to be desired, the extent to which these actors are inclined to undertake comprehensive filtering of training datasets is low. And where training datasets have been decommissioned (like the Pile), it's again not clear whether AI developers have retrained their models using different datasets.

And finally, while the open web contains digital traces of social and human life, it is not representative of the complexity, diversity and multi-faceted nature of humanity. If we start from the premise that data is not neutral, then it follows that datasets created from digital traces of human life on the open web bear a certain politics. For instance, the open web enables access to open government datasets, and these datasets are used to train foundation AI models.<fnref n="37" /> However, these datasets are not neutral artefacts – instead, they

<footnote n="30">³⁰ de Souza Depieri and Msipa, "Artificial Intelligence in the Global South: Will AI Advancement Deepen Digital Divides and Inequalities?"</footnote>

<footnote n="31">³¹ Khan et al., “A Global Review of Publicly Available Datasets for Ophthalmological Imaging.”</footnote>

<footnote n="32">³² Agarwal, “AI (Model) of the Tiger: India to Back Local Language AI Foundation Model, RFP to Be Issued Soon.”</footnote>

<footnote n="33">³³ Longpre et al., “Consent in Crisis: The Rapid Decline of the AI Data Commons,” 12.</footnote>

<footnote n="34">³⁴ Luccioni and Viviano, “What’s in the Box?”</footnote>

<footnote n="35">³⁵ Prabhu and Birhane, “Large Image Datasets.”</footnote>

<footnote n="36">³⁶ Baack and Mozilla Insights, “Training Data for the Price of a Sandwich.”</footnote>

<footnote n="37">³⁷ See for e.g., Chafetz, Saxena, and Verhulst, “A Fourth Wave of Open Data?”</footnote>
make certain phenomena and populations visible, while masking others.<fnref n="38" /> For instance, researchers have identified risks of historical bias stemming from the use of open government datasets for training AI models.<fnref n="39" /> These concerns extend to crowdsourced data made available via the open web as well. For instance, Nithya Sambasivan writes that many mapping platforms/apps in India rely on crowdsourced data to identify and tag safe and unsafe neighbourhoods.<fnref n="40" /> But, given the digital and skill divide, many contributors of this crowdsourced data are often urban, middle-class users, who tend to identify slums or neighbourhoods historically occupied by Muslim or Dalits as “unsafe”.<fnref n="41" /> These data inequities can be further exacerbated when well-resourced actors like Big Tech reuse digital traces as well as datasets from the open web to train their foundation AI models, more so since Big Tech actors care very little about equity within their workplaces or about harms stemming from their digital technologies.<fnref n="42" />

These developments raise important questions for data openness and relatedly, the openness of the open web. The distributional benefits of online access to informational resources are not equal – they are disproportionately concentrated in favour of well-resourced actors like Big Tech. Further, the Internet is also becoming siloed, with many restrictions on sharing, use and open access to informational resources. Not only does this impact the capacity of actors *other than* these well-resourced actors to develop foundation models, it also impacts the inherently collective nature of (online) digital and data commons.

<footnote n="38">³⁸ See for e.g., Denis and Goëta, "Exploration, Extraction and 'Rawification'. The Shaping of Transparency in the Back Rooms of Open Data."</footnote>

<footnote n="39">³⁹ See for e.g., Austin et al., "Trustworthy Open Data for Trustworthy AI."</footnote>

<footnote n="40">⁴⁰ Sambasivan, "Seeing like a Dataset from the Global South."</footnote>

<footnote n="41">⁴¹ Id</footnote>

<footnote n="42">⁴² See for e.g., Davis, "Tech's DEI Backlash Is Here."</footnote>
### III. The law and political economy of data openness: A three-dimensional approach

Despite the libertarian vision of the open web (or cyberspace more broadly) as a space “outside the state”, legal frameworks regulate data flows within cyberspace. Legal frameworks of copyright, data protection and contract law set limits on what kinds of data from the open web can be re-used, for what purposes, by whom, and under what conditions.

But these legal frameworks, like data, are not neutral. They have been shaped by what Benjamin Loveluck calls “informational liberalism”<fnref n="43" /> (a liberal order that encourage free flow of information via the internet) or what Julie Cohen calls the “biopolitical public domain”<fnref n="44" /> (a legal construct that renders personal information into a new kind of raw material ready for the “taking” to sustain platform-based business models).

Legal frameworks meant to regulate data flows on cyberspace also encourage more datafication. And in this regard, legal frameworks selectively benefit some actors, while deprioritizing the rights and interests of others. For instance, copyright regulation in the European Union allows commercial actors to scrape the open web, but such actors must respect opt-outs by rightsholders. Well-resourced AI Actors like OpenAI engage in large scale scraping of the open web, often with wilful disregard for third-party website terms of service, but are adamant about enforcing their own terms of service.<fnref n="45" /> These actors are also not obligated or voluntarily motivated to participate in standard-setting for harmonising text-and-data-mining opt-outs.

Further, community preferences on the open sharing of data are also changing. The liberal vision of free culture underpinning Creative Commons licenses is giving way to more nuanced data sharing contracts/licensed, where datasets and creative works are shared under different terms, depending on the identity of the re-user is and the purpose for re-use.

In the sections below, we discuss certain entanglements between legal frameworks and the political economy of data re-use. From these entanglements, we develop a three-pronged analytical framework for data openness.

#### A. Copyright and openness

By-and-large, the background cultural context for digital commons was intellectual property – in particular, copyright and the *sui generis* right. Copyright protects the structure of a dataset depending on the level of creativity and novelty involved, while in

<footnote n="43">⁴³ Loveluck, “The Internet.”</footnote>

<footnote n="44">⁴⁴ Cohen, “The Biopolitical Public Domain”; Cohen, *Between Truth and Power*.</footnote>

<footnote n="45">⁴⁵ In early 2025, OpenAI accused Chinese AI developer DeepSeek of violating its terms of service, by alleging using ChatGPT outputs to train some of the DeepSeek models. See Collier and Cui, “OpenAI Says DeepSeek May Have ‘inappropriately’ Used Its Data.”</footnote>
some jurisdictions like the EU and the United Kingdom (UK) also offer *sui generis* protections to datasets if there has been a substantial investment in obtaining, verifying or presenting the data contained within a dataset. These property-based regimes grant creators a bundle of rights over creative works/datasets, limiting data re-use as well as limiting shared knowledge production (although some argue that these bundle of rights also enable equitable distribution of profits from the data economy).<fnref n="46" />

Open content and data licenses (such as Creative Commons, Open Database License, Community Data Use License Agreement) as well as open government data licenses – constitute examples of how the “inverse logic” was used to get around intellectual property and enable a remix culture.<fnref n="47" /> In other words, creators reserved copyright, *not* because they wanted to exclusively monetize the creation of derivatives, but because they wanted to *enable more re-use* by providing standardized pre-facto authorisations.

These licenses were sought to be combined with technical initiatives like metadata standards for open datasets, to make re-use of datasets from the open web easy and smooth.<fnref n="48" />

## 1. Politics of the inverse logic

The “inverse logic” has enabled more re-use of creative works and datasets, and is central to free culture. But, as Severine Dussolier notes, there is also a politics to this inverse logic. This logic demands more openness from individual creators, while it does little to disrupt extraction from the public domain by well-resourced powerful actors like Big Tech.<fnref n="49" /> This has also been referred to as the “paradox of open”.<fnref n="50" />

Data, code and content licensed under licenses requiring attribution (like the MIT License or CC-BY) are compiled into training datasets like Common Crawl and LAION-5b that are not always licensed under compatible licenses. There is no coherent theory on the transference of attribution requirements from individual data elements to datasets.<fnref n="51" /> Thereafter, the transference of the attribution obligation to the foundation model or even generative outputs of this model also remains unclear.

Content, datasets and code licensed under licenses requiring share-alike (such as GNU GPL 3.0 or CC-BY SA) are also included in training datasets.<fnref n="52" /> Share- alike/c opyleft licenses were intended to boost contributions to the digital commons, by requiring adapted materials and derivates to be shared under similar condition of openness as the licensed content/dataset. In the AI context however, there is no clarity on how copyleft obligations transfer. When a piece of data/content licensed by CC-BY-SA

<footnote n="46">⁴⁶ See Rosnay, “Peer to Party”; Zech, “Information as Property.”</footnote>

<footnote n="47">⁴⁷ See generally, Giannopoulou, “Understanding Open Data Regulation.”</footnote>

<footnote n="48">⁴⁸ See for e.g., Pollock, “Building the (Open) Data Ecosystem.”</footnote>

<footnote n="49">⁴⁹ Dusollier, “The Master’s Tools v. The Master’s House.” See also Chander and Sunder, “The Romance of the Public Domain.”</footnote>

<footnote n="50">⁵⁰ Keller and Tarkowski, “The Paradox of Open.”</footnote>

<footnote n="51">⁵¹ Villa, “Copyleft, Attribution, and Data”; Weinberg, “Is There A Coherent Theory of Attributing AI Training Data?”</footnote>

<footnote n="52">⁵² Szkalej and Senftleben, “Mapping the Impact of Share Alike/Copyleft Licensing on Machine Learning and Generative AI.”</footnote>
is included in a training dataset like LAION-5b and later ingested by a foundation AI model
like GPT, should the training dataset, the tokens, the model and/or weights respect the
copyleft? Should the generative output also respect copyleft? These questions remain
unanswered, in part due to difficulties in transposing legal imaginations of data flows (for
instance, what copyright law understands as “copying” or “dissemination to the public”)
onto computational processes involved in training foundation AI models, as we discuss
in Section IV below.

At present, open as well as permissively licensed datasets, code and content are used
by AI developers, but to create closed training datasets and proprietary foundation
models. Even where AI developers self-proclaim their models to be open source (as in
the case of Meta's Llama model), these developers do not release their training datasets
openly.

In response, some AI researchers are working to build rights-respecting training datasets.
For instance, EleutherAI announced that they were building The Pile v2 dataset – which
would include only content from the public domain and permissively licensed work.<fnref n="53" />
This follows from takedown of the original Pile dataset, on account of copyright
infringements. The Pile included books from the public domain, as well as books from
two other datasets – the Books2 and Books3 corpuses. But numerous allegations of
copyright violations have been raised by authors, journalists and copyright management
organisations in relation to the Books3 corpus as well as in The Pile.

In addition to research organisations and open science communities, public
administrations are also becoming active data contributors for training foundation AI
models. In the US, the Department of Commerce launched a public consultation in 2024,
for developing guidelines and best practices for making open government data “Al-
ready”.<fnref n="54" /> A recent report by GovLab outlines two more examples.<fnref n="55" /> The first is France’s
LLaMandement project – where a group of French administrative departments decided
to fine-tune a LLaMA model using open government datasets and administrative data
from other ministries, to enable use of this AI model by administrative agents when
analyzing and drafting summaries of legal bills developed in the French Parliament. The
second example is Wobby – a generative-AI-powered interface that seeks to expand
findability of and access to open government datasets. The AI model underpinning this
interface was itself trained on different types of open government data and statistical
data from Eurostat as well as the World Bank.

While these efforts are commendable, Big Tech AI developers have not (and are not likely to) retrain their existing foundation models solely on openly licensed training datasets.

## 2. Giving back to the commons

The other aspect of using permissively licensed datasets, code or content, is how to
ensure that re-users contribute value back to the maintenance of these digital /

<footnote n="53">⁵³ Goldman, “One of the World’s Largest AI Training Datasets Is about to Get Bigger and ‘Substantially Better.’”</footnote>

<footnote n="54">⁵⁴ US Department of Commerce, “Request for Information.”</footnote>

<footnote n="55">⁵⁵ Chafetz, Saxena, and Verhulst, “A Fourth Wave of Open Data?”</footnote>
knowledge commons resources. As the complaint by open source software developers
in the GitHub case referenced at the start of this report notes, AI developers wilfully
ignore license terms, and train Al models ignore attribution requirements of
open/permissive licenses as well. Researchers also note that carefully designed and
thoroughly tested generative AI models can improve meaningful and equitable access to
open scientific research and data, but the current practice of training AI models on open
access publications without respecting attribution leads to a chilling effect among
researchers, which has the effect of limiting open science practices.<fnref n="56" />

One interviewee drew attention to the problem of extraction from knowledge commons, in the context of language data. For foundation AI models, language data is a central resource in building multi-parameter models as well as multilingual models. Diversity of languages and the inclusion of “low-resource”<fnref n="57" /> languages serve as competitive strategies for AI developers.<fnref n="58" /> But, as our interviewee noted, some of the best sources for local language content are national libraries and archives. While governments are working with private sector to digitize these resources and use them to create multilingual AI datasets, our interviewee goes on to note that “[the] datasets themselves are very very rarely hosted and made available publicly at large.”<fnref n="59" /> In other words, knowledge commons resources like archives and libraries become raw material for AI training and thereby become “AI-ready”, but it is not clear how these commons resources are being preserved and maintained for collective benefit.

A striking example is India's Bhashini project – a public-private partnership spearheaded
by India's Ministry of Electronics and Information Technology and with national archives,
industry bodies and publishers as partners. The Bhashini ecosystem aims to offer open
source language datasets spanning India's 22 official languages, pre-trained NLP
models, and APIs to integrate these datasets and models into other products, to enable
translation services.<fnref n="60" /> While there is a publicly-accessible dashboard for the Bhashini
project, this dashboard only provides statistics of parallel sentences between English
and Indian languages within the different language datasets created under the Bhashini
project. At the moment, neither the sources of this language data
nor the actual datasets themselves are publicly accessible.

Martin Tisne argues that data is the bottleneck for AI development.<fnref n="61" /> It is important to make data from digital and knowledge commons like the open web, libraries and archives available for AI developers, to ensure high-quality multilingual AI models. But at the same time, re-users of this data (especially well-resourced actors like Big Tech) should also contribute resources (in the form of financial value, labour, etc) back into the maintenance and preservation of these commons resources.

<footnote n="56">⁵⁶ Hosseini et al., “Open Science at the Generative AI Turn.”</footnote>

<footnote n="57">⁵⁷ Low-resource languages lack large monolingual or parallel corpora (collections of linguistic data in the form of written text or transcriptions of recorded speech) and/or manually crafted linguistic resources sufficient for building statistical NLP applications.</footnote>

<footnote n="58">⁵⁸ See for e.g., the race between Google and Meta on LLM research. Vincent, “Google Plans Giant AI Language Model Supporting World’s 1,000 Most Spoken Languages”; Vincent, “Meta Open Sources Early-Stage AI Translation Tool That Works across 200 Languages.”</footnote>

<footnote n="59">⁵⁹ Interview with an AI researcher.</footnote>

<footnote n="60">⁶⁰ https://bhashini.gov.in/about-bhashini</footnote>

<footnote n="61">⁶¹ Tisne, “What If the Bottleneck in AI Isn’t Compute Power, but Data?”</footnote>
## B. The open trilemma in web scraping

<figure><img src="image_10.png" /><figcaption>Figure 4: Graphical representation of the open trilemma</figcaption></figure>

In the AI training context, the paradox of open is also stark in the context of web scraping. As mentioned above, the open web in a fertile source of training data, and much of this is obtained by web scraping.<fnref n="62" />

Many copyright laws provide exceptions to text-and-data-mining (TDM) if undertaken for scientific research and knowledge.<fnref n="63" /> For example, UK's copyright legislation was amended in 2014 to grant a copyright exception for “computational analysis of anything recorded in the work for the sole purpose of research for a non-commercial purpose”.<fnref n="64" /> In the European Union, the 2019 Directive on Copyright and Related Rights in the Digital Single Market (EU Copyright Directive)<fnref n="65" />, contains two copyright exceptions – *one*, for TDM undertaken by research organisations and cultural heritage institutions for scientific research purposes<fnref n="66" />, and *two*, for TDM undertaken by all entities (including commercial entities), subject to the condition that such actors respect opt-outs by copyright holders<fnref n="67" />. Singapore's copyright law includes a specific exception for some categories of TDM, as well as the applicability of fair use to other categories.<fnref n="68" /> And finally in most other

<footnote n="62">⁶² We use the word 'scraping' to refer to text and data mining. We note however, the differences between scraping and crawling. See for e.g., Xue, "Common Crawl - Blog - Balancing Discovery and Privacy."</footnote>

<footnote n="63">⁶³ See generally, Fiil-Flynn et al., "Legal Reform to Enhance Global Text and Data Mining Research."</footnote>

<footnote n="64">⁶⁴ Copyright, Designs and Patents Act 1988, Section 29(A) (UK).</footnote>

<footnote n="65">⁶⁵ Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC, 2019 O. J. (L 130)</footnote>

<footnote n="66">⁶⁶ Id, Article 3(1)</footnote>

<footnote n="67">⁶⁷ Id, Article 4(1), 4(3).</footnote>

<footnote n="68">⁶⁸ Loon, "Copyright Exceptions for Text and Data Mining." In the United Kingdom, the government set out plans in 2022 to extend the text and data mining (TDM) exception to copyright to support AI development.</footnote>
common law countries, such as the US, India and Kenya, TDM for research purposes is permitted if it passes the legal test for fair use.<fnref n="69" />

At the same time, legal frameworks also try to balance the rights of creators, by allowing them to reserve their rights and thereby object to TDM in relation to their content. This is not the inverse logic, but the exercise of copyright as it was originally intended. Under the EU Copyright Directive for instance, rightsholders can reserve their copyright in an “appropriate manner”, including through machine-readable means.<fnref n="70" /> Further, all countries that are signatories to the Berne Convention for the Protection of Literary and Artistic Works, the Agreement on Trade-Related Aspects of Intellectual Property Rights and the WIPO Copyright Treaty must ensure that limitations or exceptions to exclusive rights (i) apply to certain special cases, (ii) that such special cases do not conflict with a normal exploitation of the work, and (iii) that the exception/limitation do not unreasonably prejudice the legitimate interests of the right holder.<fnref n="71" />

Legal frameworks like the EU Copyright Directive and Singapore's copyright law which provide broad exceptions for TDM, seek to convert the exception into the norm. But a select set of actors have benefited from these exceptions. On the one hand, non-profit research actors like CommonCrawl are able to create and maintain vast web-crawl datasets which is particularly useful for research on child sexual abuse material on the Internet<fnref n="72" /> as well as research on how to remove harmful content from content corpuses<fnref n="73" />. On the other hand, commercial TDM by Big Tech platforms and resource-rich AI actors like OpenAI has (and continues to) alarm content creators – evident in the suit of legal cases being filed by authors, musicians, artists, and publishing houses against these actors in the US, UK and EU. In this context, more media outlets and creative websites are using robot.txt files to block OpenAI's GPT crawler.<fnref n="74" /> But the effectiveness of these types of targeted opt-outs is not fully clear – with growing concerns of how robot.txt or other similar blockers effectively prevent all types of crawling – commercial as well as non-commercial.<fnref n="75" />

In light of this, civil society organisations like Open Future argue for more standardization in the vocabulary and architecture of machine-readable opt-out protocols that can

Under the proposals, AI developers would have been able to engage in TDM of copyright content for commercial purposes, provided they had lawful access to the works via, for example, a licence, subscription or permission in terms and conditions. However, the proposals were abandoned in early 2023 following pushback from the creative industries, meaning the TDM exception remains limited to cases where TDM is undertaken for non-commercial purposes only.

<footnote n="69">⁶⁹ See for e.g., Karanja and Koros, “The Role of Copyright Law in Text and Data Mining Research”; Rajeev and Bharadwaj Singh, “Training GenAI.”</footnote>

<footnote n="70">⁷⁰ Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC, 2019 O. J. (L 130), Article 4(3).</footnote>

<footnote n="71">⁷¹ See generally, Senftleben, “Compliance of National TDM Rules with International Copyright Law.”</footnote>

<footnote n="72">⁷² See for e.g., Pereira et al., “Metadata-Based Detection of Child Sexual Abuse Material.”</footnote>

<footnote n="73">⁷³ See for e.g., Luccioni and Viviano, “What’s in the Box?”</footnote>

<footnote n="74">⁷⁴ Bogle, “New York Times, CNN and Australia’s ABC Block OpenAI’s GPTBot Web Crawler from Accessing Content.”</footnote>

<footnote n="75">⁷⁵ See for e.g., Keller and Zuzanna, “Defining Best Practices for Opting out of ML Training”; Longpre et al., “Consent in Crisis: The Rapid Decline of the AI Data Commons.”</footnote>
enable creators to enforce opt-outs, as opposed to relying on platforms that host content to register the opt-outs.<fnref n="76" />

Copyright scholars Kacper Szkalej and Martin Senftleben also argue that in the EU context, rightsholders should reserve copyright under Article 4(3) of the EU Copyright Directive and thereby conditionally allow AI developers to engage in TDM subject to certain conditions of responsible data re-use, such as share-alike.<fnref n="77" /> In the European context, this would allow rightsholders to initiate copyright violations against AI developers who do not respect such share-alike conditions.

### C. Openness beyond copyright

Additionally, the re-use of data is no longer shaped only by copyright. The “old” concerns continue to remain relevant – in terms of uptake of open data licenses, proliferation of licenses, combability between licenses, as well as enforcement of license terms. But there are also new concerns.

One set of concerns relate to the inclusion of personally identifiable data into training datasets, or subsequently being inferred by generative AI models. This is particularly concerning in the context of web scraping.<fnref n="78" />

Italy's the data protection authority temporarily banned ChatGPT, and then went on to fine OpenAI 15 million euros for violations of the Italy's data protection law by ChatGPT.<fnref n="79" /> The legal issues related to lack of a legal basis for processing personal data, when OpenAI trained GPT on web-scraped data, non-adherence to transparency principles, non-compliance with data rights provisions making it difficult for individuals to exercise their right to rectification or deletion for instance, and the absence of age verification measures for users of ChatGPT. The Italian data protection authority also recently blocked access to the Chinese-LLM DeepSeek, following a request for information by the authority from the company on compliance with Italy's data protection law.<fnref n="80" /> The company responded saying it was not required to comply with this law, or EU's General Data Protection Directive (GDPR) on which the Italian law is modelled, which prompted the blocking order. The Irish Data Protection Commissioner has also launched a probe against DeepSeek, while data protection authorities in France and Croatia are seeking more information from the company on GDPR compliance.<fnref n="81" /> There have also been litigations in the US and Brazil on data protection violations in Big Tech AI models like ChatGPT and Llama.

<footnote n="76">⁷⁶ Keller, “Considerations for Implementing Rightholder Opt-Outs by AI Model Developers.”</footnote>

<footnote n="77">⁷⁷ Szkalej and Senftleben, “Mapping the Impact of Share Alike/Copyleft Licensing on Machine Learning and Generative AI.”</footnote>

<footnote n="78">⁷⁸ See for e.g., Solove and Hartzog, “The Great Scrape.”</footnote>

<footnote n="79">⁷⁹ Garante Per La Protezione Dei Dati Personalı, 30 marzo 2023, 9870832, n. 112, Registro dei provvedimenti, 2023; Garante Per La Protezione Dei Dati Personalı, 2 novembre 2024, 10085455, n. 755, Registro dei provvedimenti, 2024.</footnote>

<footnote n="80">⁸⁰ Garante Per La Protezione Dei Dati Personalı, 30 gennaio 2025, 10098477, n. 33, Registro dei provvedimenti, 2025.</footnote>

<footnote n="81">⁸¹ Moreau, “DeepSeek Making a Splash with EU Data Protection Bodies.”</footnote>
Data protection tensions (and privacy tensions more broadly) are not new to AI training datasets, but were present in the context of open datasets and government archives as well.<fnref n="82" /> There are now efforts by international organisations to clarify and harmonize data protection frameworks in the AI context, to overcome popular but misleading framing of data protection as a hindrance to innovation.<fnref n="83" />

Another set of concerns relate to ethical restrictions on data re-use in the context of AI. The Responsible AI community for instance, supports behavioural use restrictions on training data as well as AI systems to control the unique harms arising from AI. Another example is the UsageRights License developed by a group of researchers from France.<fnref n="84" /> This license allows creators of scientific outputs to restrict use of their works to individuals and organizations that have publicly declared their commitment to ecological sustainability.

And sitting on top of all these issues, is data colonialism.<fnref n="85" /> Data colonialism is visible in the continuous extraction of user-behaviour data by Big Tech platforms to now train AI models and the subsequent use of these AI models for decision-making in different public and private domains.<fnref n="86" /> Scholars of law and political economy have analysed the ways in which legal frameworks of intellectual property, consumer privacy and competition law continue to enable this kind of datafication by platforms, which can be extended to the AI context as well.<fnref n="87" /> The Brazilian data protection authority for instance, initiated an investigation against Meta for changing its terms of service to use all user-behaviour data across Meta products and services for training its Llama models.<fnref n="88" /> Data colonialism also manifests in the extraction of data, knowledge and labour from the majority world by resource-rich actors in the Global North to create foundation AI models. Big Tech developers often create multilingual AI models by relying on machine-translations of English language datasets into other languages.<fnref n="89" /> This results in extremely inaccurate results, when such AI models are used for text translation. Further, training datasets involve numerous human-hours for scrubbing and annotation of these datasets, and a lot of this labour is outsourced to data annotators in Africa to lower costs.<fnref n="90" /> To combat these issues, as well as challenge the dominant Silicon Valley approach to AI development (which is resource, labour and data-intense), many researchers in Africa and New Zealand are coming together to create small community-contributed language datasets, which are then used to create small context-specific NLP models for low-resource languages.<fnref n="91" />

<footnote n="82">⁸² See for e.g., Botero Arcila, “Future-Proofing Transparency”; Dalla Corte, “The European Right to Data Protection in Relation to Open Data”; Scassa, “Open Data & Privacy.”</footnote>

<footnote n="83">⁸³ OECD, “AI, Data Governance and Privacy: Synergies and Areas of International Co-Operation.”</footnote>

<footnote n="84">⁸⁴ Thomas, Monnin, and Tannier, “UsageRight.”</footnote>

<footnote n="85">⁸⁵ See generally, Couldry and Mejias, *The Costs of Connection*.</footnote>

<footnote n="86">⁸⁶ See for e.g., Harvard Law Review, “Data Colonialism and Data Sets.”</footnote>

<footnote n="87">⁸⁷ See for e.g., Bietti, “A Genealogy of Digital Platform Regulation”; Griffin, “The Law and Political Economy of Online Visibility.”</footnote>

<footnote n="88">⁸⁸ Badillo, “Processing of Personal Data for AI Training in Brazil.”</footnote>

<footnote n="89">⁸⁹ Nicholas and Bhatia, “Lost in Translation: Large Language Models in Non-English Content Analysis,” 10.</footnote>

<footnote n="90">⁹⁰ Casilli, *Waiting for Robots*; Orr and Crawford, “The Social Construction of Datasets.”</footnote>

<footnote n="91">⁹¹ See for e.g., Ravindran, “AI Often Mangles African Languages. Local Scientists and Volunteers Are Taking It Back to School”; Alliance, “Open-Source AI Data Sharing.”</footnote>
D. Conceptualising a three-pronged approach to data openness

From the analysis above, we develop a three-dimensional framework of data openness for training datasets, as illustrated in Figure 5 below:

<figure><img src="image_11.png" /><figcaption>Figure 5: Three-dimensional framework for data openness of training data</figcaption></figure>

In 2023, Open Future mapped the different “fields of openness” that constitute the multi-faceted open movement of today.<fnref n="92" /> Multiple clusters of openness were identified – open government, open data, open software, open access and open science, open culture, free culture and copyright reform, open internet. The binding factor among these clusters, was that the open movement “consists of people, communities, and organizations who (1) contribute to shared resources online that are available for everyone to use and reuse, (2) and/or advocate for non-exclusive access and use of information resources.”

In the context of training datasets for AI, data openness can certainly be analysed through this lens, by probing the extent to which training datasets are made available for non-exclusive access and re-use. Here, legal frameworks of copyright that limit the remix culture and the ways in which copyright can be subverted or inverted through licenses are relevant. On the flip side, it is also relevant to probe how training datasets (and the processes behind these datasets) can be made open, while preserving data protection as well as ensuring compliance with license and contract terms. At the heart of this type of inquiry, are the tensions between computational processes involved in creating and using training datasets on the one hand, and legal imaginations of how data, information and knowledge should flow from one actor to another. This form of enquiry can be understood as the techno-legal aspect of data openness, and it seeks to make the process of data re-use standardized and “easy”. Techno-legal data openness, in the form of removing technical and legal barriers to re-use of datasets, is necessary (particularly in the context of open government data, to engender more transparency and accountability in the functioning of public administrations).

But as highlighted above, the politics of the inverse logic combined with data colonialism means that certain actors benefit from openly licensed training datasets, at the cost of

<footnote n="92">⁹² Tarkowski et al., “Fields of Open. Mapping the Open Movement.”</footnote>
others. Well-resourced powerful actors like Big Tech acquire the means to extract data, information and knowledge from the individuals and from the majority world, structure this into training datasets, and use these datasets to train (in most cases) closed proprietary foundation AI models. And where efforts are made to release AI foundation models under open licenses, these often result in openwashing as openness obligations are rarely extended to the training datasets. While counterintuitive, data openness also requires the imposition of certain limits on the extractive power of well-resourced actors like Big Tech. These limits can be understood as enabling conditions for data openness.

Finally, growing focus on community data sovereignty adds another lens to data openness – the lens of participation. For instance, in response to LLM research that is dominated by the English language, there are growing communities creating training datasets and resources for non-English language LLMs – such as the Masakhane Community in Africa, the AmericasNLP Community for indigenous languages in the Americas.<fnref n="93" /> The indigenous data sovereignty movement developed in the context of open science, is also expanding to data governance in the context of AI.<fnref n="94" /> In indigenous data sovereignty, data is considered as a resource to be guarded and stewarded for citizens, communities and future generations, as opposed to a resource to be ‘owned’ – contesting the property logics underpinning the bundle of rights historically attached to data. Within the growing discourse on data as a global public good / a global commons, particularly in the context of civic tech, there is a similar focus on sustainable community-driven governance of data.<fnref n="95" /> Data openness for training datasets should also be probed from this perspective of community participation in the generation and maintenance of these datasets.

**In this report, we combine these elements to conceptualise a three-dimensional approach to data openness.<fnref n="96" /> Data openness requires a balance between: (i) techno-legal data openness, (ii) limits on data extractivism by well-resourced actors like Big Tech, and (iii) community data sovereignty.**

Techno-legal data openness seeks to *smoothen* data re-use, by removing technical and legal constraints on data re-use. But focusing solely on this type of data openness ignores the *politics of openness* (or the *political economy of data re-use*), as illustrated in this section. To account for these politics/political economy, there is a need for legal and policy strategies that place some limits on the extractive data power of well-resourced actors like Big Tech as well as recognize community data sovereignty. Framed differently, in some contexts, there is a need to (re)introduce some friction to data re-use. There are some ongoing legal controversies as well as alternative licensing frameworks that advance our three-pronged approach to data openness, but as the sections below illustrate, they come with complexities.

<footnote n="93">⁹³ Radiya-Dixit, “Local AI Research Groups Are Preserving Non-English Languages in the Digital Age | TechPolicy.Press.”</footnote>

<footnote n="94">⁹⁴ Roberts and Montoya, “In Consideration of Indigenous Data Sovereignty.”</footnote>

<footnote n="95">⁹⁵ See for e.g., Avila and Weress, “Citynetics in the South: A Blueprint for a City Data Commons.”</footnote>

<footnote n="96">⁹⁶ We draw from existing initiatives that have conceptualized gradient approaches to data openness. See for e.g., Tarkowski, “Data Governance in Open Source AI”; OECD, *Enhancing Access to and Sharing of Data*; Digital Public Goods Alliance Community of Practice, “Core Considerations for Exploring AI Systems as Digital Public Goods.”</footnote>
#### IV. Applying our data openness framework to make sense of ongoing legal controversies

In this section, we use our three-pronged framework on data openness to a set of ongoing legal controversies. We focus on two types of legal disputes – copyright and database right on the one hand, and data protection on the other hand. We analyse our repository of 41 legal controversies from the US, UK, EU, Brazil, India, Canada and China.

<figure><img src="image_12.png" /><figcaption>Figure 6: Visualisations of our repository of ongoing legal controversies around AI training</figcaption></figure>

Our analysis highlights certain tensions between legal imaginations of data flows and computational processes involved in reusing the open web to train foundation AI models. Our analysis sheds more light on the politics of techno-legal data openness. Our analysis also reveal the complexity of adopting legal interpretations to copyright and data protection laws that place limits on the data-extractive power of well-resourced actors like BigTech while making space for community data sovereignty.

However, the legal issues discussed in this section are not exhaustive. We do not discuss, for instance, tortious claims of negligence, contractual damages arising from unjust enrichment, anti-competitive practices and unfair business practices, misinformation/disinformation risks, or cybersecurity risks – which have been recognised in other taxonomies of risks / harms of foundation models.<fnref n="97" /> We also limit discussion on intellectual property to copyright and sui generis right, while recognizing that data openness is impacted by moral rights and publicity rights, as well as other intellectual property frameworks such as patents, trademarks and trade secrets.

<footnote n="97">⁹⁷ See for e.g., Atkinson and Morrison, “A Legal Risk Taxonomy for Generative Artificial Intelligence”; Derner et al., “A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models”; Gupta et al., “From ChatGPT to ThreatGPT”; Suresh and Guttag, “A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle.”</footnote>
## A. Copyright (and it's cousin, the sui generis database right)

Copyright and the sui generis right grant creators a bundle of rights over their creative works as if such creators were owners of property – such as the right to possess, use and dispose their creative works.<fnref n="98" /> Legal frameworks for copyright (in the context of creative works) and the sui generis right (in the context of substantial investment in the creation of databases) grant creators a limited monopoly of exploitation – allowing creators to control reproductions/copying of their creative works, making their work publicly available or distributing/disseminating their work to the public, and transformations of their work.<fnref n="99" /> This monopoly is limited – either by way of specific exceptions as listed in most continental laws, or the form of fair use limitations as in most common laws.

Tensions arise, because of difficulties in transposing the legal bundle of rights contained within copyright, onto computational processes of data flows in the context of foundation AI models.

### How do courts understand the process of foundation model development?

Researchers have identified three broad stages prior to deployment of foundation AI models – pre-training i.e. the collection and preprocessing of data from the internet; training models on the training datasets obtained through the pre-training phase; and then fine-tuning and evaluating the trained model.<fnref n="100" />

From a legal perspective, a recent ruling by a German District Court in a copyright infringement case against LAION provided an overview of the steps involved in training a foundation model –

* "the creation of a data set (which is the sole subject of the dispute here) that can also be used for AI training,

* on the other hand, the subsequent training of the artificial neural network with this data set and

* thirdly, the subsequent use of the trained AI for the purpose of creating new image content."<fnref n="101" />

<footnote n="98">⁹⁸ See generally, Merges, “What Kind of Rights Are Intellectual Property Rights?”; Dusollier, “Intellectual Property and the Bundle-of-Rights Metaphor.”</footnote>

<footnote n="99">⁹⁹ Rosnay, “Peer to Party.”, pp 10. Laws use different words to capture these concepts. For instance, the 2001 EU Directive on the harmonisation of certain aspects of copyright and related rights in the information society grants creators exclusive right to “authorise or prohibit any communication to the public of their works” (Article 3(1)) as well as to “authorise or prohibit any form of distribution to the public by sale or otherwise.” (Article 4(1)). The Copyright, Designs and Patents Act 1988 of the UK instead uses the phrases “issue copies of the work to the public”...., “communicate the work to the public”, or “make an adaptation of the work or do any of the [aforementioned actions] in relation to an adaption.” (Section 16(1)). The Copyright Act, 1957 of India largely follows the UK law, but also grants creators the exclusive right to authorize or refuse any translation of their works. (Section 14(a)(v)).</footnote>

<footnote n="100">¹⁰⁰ Zhou and et al, “Training and Serving System of Foundation Models: A Comprehensive Survey”; Liu et al., “Datasets for Large Language Models.”</footnote>

<footnote n="101">¹⁰¹ Landgericht Hamburg [LH], Sep. 27, 2024, openJUR 2024, 9199 (Ger). See also Guadamuz, “LAION Wins Copyright Infringement Lawsuit in German Court.”</footnote>
During our online workshop, we asked our participants to imagine data from the open web flowing through the AI lifecycle, and identify the different copyright and sui generis issues that arise at each stage of the AI lifecycle.

<figure><img src="image_13.png" /><figcaption>Figure 7: Screenshot of a miro board used during our workshop (zoom to read the text)</figcaption></figure>

By synthesizing inputs from our workshop, together with our desk research, we identify 5 copyright and sui generis issues that arise at 3 stages of the AI lifecycle:

<figure><img src="image_14.png" /><figcaption>Figure 8: Visual representation of copyright concerns along the OECD AI lifecycle</figcaption></figure>
1. When creating a training dataset, is there any “copying”?

Ofcourse, to the extent the training datasets contain data from the digital public domain, copyright usually does not apply. The exception is in jurisdictions like Chile, where certain acts of the private appropriation of public domain works are penalised.<fnref n="102" /> Further, to the training datasets include facts, statistics and ideas that cannot be copyrighted, copyright issues do not arise.

But, when training datasets like LAION 5b or Common Crawl include creative works licensed under open/permissive licenses, as well as creative works made accessible via the open web but whose use requires compliance with copyright laws, there is a question of whether there is any “copying” of protected content.

Most copyright frameworks grant authors a monopoly over any act of reproduction. But with the rise of digital computing technologies and the Internet, this create issues with regard to temporary copies – such as storage of temporary copies on a computers Random Access Memory when a media file is played, or a website is accessed.<fnref n="103" /> In the EU, the 2001 Directive on Harmonisation of certain aspects of Copyright and related rights in the Information Society<fnref n="104" /> includes a copyright exception for temporary copies. The European Court of Justice has provided clarity on how to interpret this exception, by clarifying how “temporary” and “transient” copies as used in the text of the legal directive should be interpreted.<fnref n="105" /> Per this judgment, the temporary copying exception applies if the reproduction in question will be deleted, destroyed or will otherwise disappear within some time, typically with the deletion being undertaken by an automated process, and where the reproduction is integral and essential for a technological process, and where the reproduction itself has not independent economic significance (among other conditions).

As Andres Guadamuz notes, the issue of copying is complicated in the case of datasets like LAION-440M and LAION-5b.<fnref n="106" /> These are large datasets of image-text pairs created by crawling the open web. But these datasets do not house actual copies of the images obtained by crawling the open web – rather, as noted in an FAQ – “LAION datasets are simply indexes to the internet, i.e. lists of URLs to the original images together with the ALT texts found linked to those images. While we downloaded and calculated CLIP embeddings of the pictures to compute similarity scores between pictures and texts, we subsequently discarded all the photos. Any researcher using the datasets must reconstruct the images data by downloading the subset they are interested in.”<fnref n="107" />

<footnote n="102">¹⁰² Law No. 17.336 on Intellectual Property, 28-AGO-1970, Article 80 (Chile).</footnote>

<footnote n="103">¹⁰³ See for e.g., Aaron Perzanowski, Fixing RAM Copies, 104 NW. U. L. REV. 1067 (2010).</footnote>

<footnote n="104">¹⁰⁴ Directive (EU) 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society, 2001 O. J. 167, Article 5(1).</footnote>

<footnote n="105">¹⁰⁵ See generally, C-5/08, Infopaq International A/S v Danske Dagblades Forening, ECLI:EU:C:2009:465 (EU); C-360/13, Public Relations Consultants Association Ltd v Newspaper Licensing Agency Ltd and Others, ECLI:EU:C:2014:1195 (EU).</footnote>

<footnote n="106">¹⁰⁶ Guadamuz, “Photographer Sues LAION for Copyright Infringement.”</footnote>

<footnote n="107">¹⁰⁷ “FAQ | LAION.”</footnote>
In the EU, a ruling by the District Court of Hamburg in 2024, provides some clarifications but also raises some questions.<fnref n="108" /> The plaintiff in the case was a photographer, who sued LAION for copyright violations in its LAION-5B dataset. The plaintiff argued that LAION copied the plaintiff's photographers in the process of creating the LAION-5b dataset, and that this act of copying is not covered by any copyright exceptions. LAION argued that such copying was only temporary, to verify accuracy of image descriptions against the actual source images, and after such verification, the source images were deleted.

The court rejected LAION's argument that the copying in question was only temporary, based on precedent of the European Court of Justice. The court also went on to note that deletion of the images would qualify for temporary copying only if the deletion was undertaken automatically, without any human intervention, which was not the case with LAION.

## How much copying is contemplated within data openness?

There is a body of legal jurisprudence, on the difference between copying or reproductions as part of the protected bundle of copyright protections, and the copyright exception of temporary copying. As Andres Guadamuz notes, "*the Internet is a huge infringement machine; we're making transient copies of things all the time, so this exception has become quite powerful. We would not be able to exist online without it.*"<fnref n="109" />

Legal jurisprudence has evolved in many countries, to provide clarity on the legal requirements for benefiting from the temporary copying exception, in the context of Internet technologies. While this legal jurisprudence is useful in advancing technological data openness, the unique risks to creative workers emanating because of the specific computational processes of training datasets and foundation model development need to be factored in. For instance, some legal scholars argue that because the LAION-5b dataset is searchable and through this, full-size pictures can be retrieved, this constitutes "actionable acts of reproduction."<fnref n="110" /> Other scholars argue that tokenisation (the process by which raw text and visual input is converted into token bearing numerical values which allows a foundation AI model to "perceive" and learn from these inputs) involves reproductions that are *not* transient in nature.<fnref n="111" />

## 2. When a training dataset is created by scraping, is that legal?

In most jurisdictions, there are copyright exceptions for the reproduction and compilation of web-scraped data – either in the form of TDM exceptions as in the EU and the UK, or in the form of fair use in the US.

<footnote n="108">¹⁰⁸ Landgericht Hamburg [LH], Sep. 27, 2024, openJUR 2024, 9199 (Ger).</footnote>

<footnote n="109">¹⁰⁹ Guadamuz, “Photographer Sues LAION for Copyright Infringement”. See also Chen et al., “Providing an Incidental Copies Exemption for Service Providers and End-Users.”</footnote>

<footnote n="110">¹¹⁰ Rosati, “The German LAION Decision.”</footnote>

<footnote n="111">¹¹¹ Gervais et al., “The Heart of the Matter.”</footnote>
In the EU, extraction and reproduction of protected content as part of TDM is permitted, while in the UK, only reproduction is permitted. In the US, copying as well as compilation of extracted information through text-and-data mining to advance non-commercial research purposes is permitted as part of the flexible test of fair use.<fnref n="112" />

Rightsholders can prohibit TDM through website terms of service or other technical measures. But this is a double-edged sword. On the one hand, these opt-outs are particularly relevant for journalists and creative workers, who face new risks on account of generative AI models that produce strikingly similar results as the protected works of these journalists and creative workers. On the other hand, many proprietary databases, particularly scientific databases, use restrictive website terms of service and licensing agreements, to limit TDM on their corpuses. This forces researchers to rely on websites like Sci-Hub – which provides access to scientific materials that are behind paywalls. Sci-Hub has been accused of engaging in online piracy<fnref n="113" />, but this website continues to enable more access to paywalled scientific content, and thereby enables TDM on this content. Pamela Samuelson argues that running TDM on Sci-Hub corpuses hosted outside the US could be protected under fair use provisions of US copyright law; but this may not be protected under EU law, as the text-and-data-mining permission under EU law is predicated on researchers having lawful access to the content they mine/scrape.<fnref n="114" />

The other issue is *how* to interpret the provisions of specific legal exceptions, as under EU law. In the EU, TDM is permitted by both research and cultural heritage organisations, as well as commercial actors. But in the case of TDM by commercial actors, rightsholders can register opt-outs which must be respected by these commercial actors. The German court in the LAION-5b case discussed above, provided some clarifications on both these aspects.<fnref n="115" /> *First*, it clarified that the TDM exception for research institutions can be availed by LAION. For the court, the main determining factor was whether LAION engages in non-commercial research or not. In this case, the court held that LAION makes its dataset publicly available free of charge, and that this dataset constitutes a step in the process of acquiring scientific knowledge, thereby rendering the activity within the ambit of non-commercial scientific research. The fact that LAION also uses this dataset to train its commercial AI models was irrelevant. *Second*, the court clarified how rightsholders can register opt-outs for TDM in the context of training AI models. The court holds that opt-outs in the form of natural language text – such as contractual terms and conditions of use – also count as machine-readable opt-outs which must be respected by actors like LAION.

The appropriate manner to register opt-outs was also discussed in a ruling by an Amsterdam Court (albeit not in the context of AI training).<fnref n="116" /> In this case, the court held that for the opt-out to be effective, it must clearly and explicitly identify the target (i.e. the

<footnote n="112">¹¹² See for e.g., Samuelson, “Text and Data Mining of In-Copyright Works: Is It Legal?”</footnote>

<footnote n="113">¹¹³ See for e.g., Elsevier Ltd and Ors v. Alexandra Elbakayan and Ors., CS (COMM) 572/2020 (an ongoing lawsuit in the Delhi High Court in India, initiated by a group of publishers against SciHub and another similar website known as LibGen)</footnote>

<footnote n="114">¹¹⁴ Samuelson, “Text and Data Mining of In-Copyright Works: Is It Legal?”</footnote>

<footnote n="115">¹¹⁵ See Guadamuz, “LAION Wins Copyright Infringement Lawsuit in German Court”; Goldstein, Stuetzle, and Bischoff, “Kneschke vs. LAION - Landmark Ruling on TDM Exceptions for AI Training Data – Part 1.”</footnote>

<footnote n="116">¹¹⁶ See Valk and Toepoel, “DPG Media et al vs. HowardsHome – A National Ruling on DSM’s Press Publishers’ Rights and TDM Exceptions.”</footnote>
bots) it wants to block. To the extent rightsholders use generic robot.txt opt-outs, these
are targeted at blocking Al bots such as GPTBot, ChatGPT-User, CCBOT and anthropie-
ai, and did not target the bot in question (i.e. the bot used by the defendant to aggregate
news articles).

This ruling is a win for techno-legal data openness, particularly to the extent that it interprets TDM for research purposes in a broad fashion. But, there are some concerns when it comes to the ability of rightsholders to limit commercial TDM, particularly where well-resourced actors like Big Tech companies rely on this to build generative AI models.

Striking the right balance on openness of web-scraping:

Generative AI brings forth new risks for creative workers. Creative content is scraped from the open web to train generative AI models that produce substantially similar results to the protected works of rightsholders, and thereby create generative products that compete with these protected works. To this extent, rightsholders should be able to register opt-outs to web-scraping, particularly in jurisdictions like the EU where legal frameworks explicitly require these opt-outs to be respected. Here, techno-legal data openness needs to be balanced against the extractive data-power of well-resourced actors like Big Tech.

But, the way in which these opt-outs are expressed is important to ensure that there is
no chilling effect, in the form of spillover restrictions on other necessary (and
historically uncontroversial) forms of TDM, as undertaken by researchers or
undertaken by online platforms as part of search and discovery services.

To this extent, Open Future has created a nested conceptual vocabulary for opt-outs – ranging from the broadest opt-out against all forms of TDM, to more narrow opt-outs limited to TDM for training AI models that either have the objective of producing generative outputs or have the capability to do so.<fnref n="117" /> Translating this vocabulary into legal contracts like website-terms-of-service or standards such as the Robot Exclusion Protocol is necessary to strike the right balance between techno-legal data openness in the context of web-scraping, and the need to limit the extractive data-power of well-resourced actors like Big Tech who scrape the open web but give little to no value back to the digital and data commons.

By contrast, in countries like Canada, there are no upfront exceptions for TDM. To the extent creative works are scraped, such actions must be justified under fair dealing provisions of Canada's copyright law – similar to fair use provisions in the US. An ongoing legal case provide some clarity. The case has been filed by Canadian Legal Information Institute, a non-profit organization that provides open online access to judicial decisions and legislative documents, against an AI company named Caseway AI. In its complaint, the plaintiff argues that Caseway engages in bulk download and scraping of the plaintiff's legal database, which amounts to a copyright violation as well as violation of the

<footnote n="117">¹¹⁷ Keller, “A Vocabulary for Opting out of AI Training and Other Forms of TDM.”</footnote>
plaintiff's website terms of use which prohibit bulk downloads.<fnref n="118" /> The extent to which
Caseway's actions will be considered fair dealing remain to be seen.

3. When a model “learns” parameters from a training dataset, is this a derivative?

A training dataset records data in the form of tokens. A trained model learns from these
tokens and exhibits a set of parameters. Many foundation models are parameter-
intensive, as these parameters determine the diversity of tasks and objectives that can
be performed by the model.

The question here, is whether parameters constitute “transformative uses” or
“derivatives” of a training dataset (or going one step back, of individual data elements
that constitute a training dataset). If yes, then parameters can benefit from copyright. If
not, then the act of creating parameters can be considered a violation of exclusive
economic rights protected by copyright.

Some scholars like Pamela Samuelson have argued that when an AI mode “ingests” a
training dataset to learn its parameters, “the model is extracting and analyzing precise
facts about, and correlations between, discrete elements of the works to ascertain which
other discrete elements either do or do not follow or are proximate to these elements and
the frequency with which the correlations do or do not exist in varying contexts.”<fnref n="119" /> She
rightfully argues that copyright in a creative work, like a photograph of a cat, does not
attach itself to characteristic features of cats or to inferences drawn from multiple
photographs of cats about their shared features.<fnref n="120" /> In other words, copyright in creative
works that have been compiled into a training dataset, does not prohibit the creation of
parameters when this training dataset is used to train an AI model.

Recognising parameters as derivatives of training datasets, to encourage more
techno-legal openness

There can be a positive benefit to recognizing that parameters of a foundation model
are “derivatives” of the training dataset used to build this model. To the extent the
training dataset is licensed under copyleft, this legal interpretation can require model
developers to apply share-alike to other components as well, as such the parameters
and weights.<fnref n="121" />

4. What about generative outputs? Does this trigger “copying” or “dissemination to the public of protected works”?

<footnote n="118">¹¹⁸ Complaint, Canadian Legal Information Institute v. Clearway Management Ltd and Others, Court File No. VLC-S-S-247574 (Supreme Court of British Columbia, 2024).</footnote>

<footnote n="119">¹¹⁹ Samuelson, “Generative AI Meets Copyright.”</footnote>

<footnote n="120">¹²⁰ Id.</footnote>

<footnote n="121">¹²¹ See for e.g., Benhamou, “Open Source AI: Does The Copyleft Clause Propagate To Proprietary AI Models? Revisiting The Definition Of Derivatives In The AI-Context.”</footnote>
And finally, there is the issue of “plagiaristic outputs” of AI systems, particularly those created using generative AI models. This is the subject matter of increasing litigation by creative workers and their representatives against AI developers in the US, UK, Germany and China.

In the US, visual artists filed a complaint against Stability AI, DeviantArt, Midjourney and Runway AI, in relation to image generation AI system - Stable Diffusion.<fnref n="122" /> They alleged removal or alteration of copyright management information (such as the author's name) from a protected work, as the generative AI products developed and placed on the market by the defendant companies produce almost-identical outputs as the protected work without attributing the creative workers. This claim has been dismissed. A similar claim was brought by the media company Raw Story against Open AI, but this claim was also dismissed.<fnref n="123" />

Interestingly, another media company Intercept also sued OpenAI for removal or alteration of copyright management information, and this claim has been admitted.<fnref n="124" /> In its complaint, Intercept hired a data scientist to reconstruct the training dataset used to train GPT-4, and revealed that this proxy dataset contained 5,026 distinct URLs from Intercept's domain.<fnref n="125" />

The New York Times Company also brought a case against Open AI and Microsoft in the US, alleging removal alteration of copyright management information, as well as direct and vicarious copyright infringement by ChatGPT.<fnref n="126" /> The defendants sought to dismiss both claims. At present, the court has not issued any preliminary rulings, but both parties have agreed on a discovery protocol as part of which OpenAI and Microsoft have agreed to provide access to training data used to train ChatGPT to lawyers of the plaintiffs.<fnref n="127" /> The New York Times case is particularly relevant, as the plaintiffs were able to show proof that when prompted, ChatGPT reproduced parts of NYT articles.<fnref n="128" /> Visual artists in the case against Stability AI, DeviantArt, Midjourney and Runway AI have alleged direct and induced copyright infringement in the use of protected artwork to train Stable Diffusion, as result of which Stable Diffusion produces outputs that are substantially-similar to copyright works of the plaintiffs. These claims will now be heard on merits.

In the UK, Getty Images filed a suit against Stability AI alleging the use of its repository for training Stable Diffusion, and for copyright infringement by Stable Diffusion's outputs.<fnref n="129" />

<footnote n="122">¹²² Second amended complaint, Sarah Anderson and Others v. Stability AI Ltd., Stability AI, Inc., DeviantArt, Inc., Midjourney, Inc., and Runway AI, Inc., Docket No: No. 3:23-cv-00201 (N. D. Cal. 2023).</footnote>

<footnote n="123">¹²³ Decision and Order dated November 7, 2024, Raw Story Media Inc., and Alternet Media Inc. v. OpenAI Inc., and others, Docket No. 1:24-cv-01514 (S.D.N.Y, 2024).</footnote>

<footnote n="124">¹²⁴ See Salik, “Judge Rakoff’s Decision in Intercept Media v. OpenAI Copyright Case Creates SDNY Split.”</footnote>

<footnote n="125">¹²⁵ First amended complaint at 43, The Intercept Media Inc., v. OpenAI Inc., and others, Docket No. 1:24-cv-01515 (S.D.N.Y, 2024).</footnote>

<footnote n="126">¹²⁶ First amended complaint, The New York Times Company v. Microsoft Corporation, OpenAI, Inc., and others, Docket No. 1:23-cv-11195 (S.D.N.Y 2023).</footnote>

<footnote n="127">¹²⁷ Stipulation and Order dated October 3, 2024, The New York Times Company v. Microsoft Corporation, OpenAI, Inc., and others, Docket No. 1:23-cv-11195 (S.D.N.Y 2023). For an overview of ‘direct copyright infringement’ and ‘vicarious copyright infringement’, see Atkinson and Morrison, “A Legal Risk Taxonomy for Generative Artificial Intelligence.”, pp 4-5.</footnote>

<footnote n="128">¹²⁸ See generally, Guadamuz, “The New York Times Lawsuit.”</footnote>

<footnote n="129">¹²⁹ See Skrein and Kacprzak, “Getty v. Stability AI Case Goes to Trial in the UK – What We Learned.”</footnote>
In Germany, the Germany copyright collecting society GEMA has filed lawsuits against OpenAI for ChatGPT and Suno AI, alleging that the generative AI models in both cases reproduce copyrights song lyrics without remunerating the artists.<fnref n="130" />

In China however, there is already some precedent on the question of plagiaristic outputs, in a ruling by the Guangzhou Internet Court.<fnref n="131" /> The case was brought by Shanghai Character License Administrative Co. Ltd – the exclusive Chinese licensee of Ultraman, a character from a Japanese science fiction television series. The defendant operated a chatbot capable of creating AI-generated images upon user request. When a user requested an Ultraman-related image, the chatbot produced an image that was claimed to be significantly similar to the licensed version of Ultraman. The court ruled in favour of the licensee, arguing that there was both copyright infringement by way of full/partial reproduction as well as adaptation. The court awarded damages to the claimant. The claimant also requested the court to order the defendant to remove the disputed Ultraman materials from the defendant’s training dataset, but this relief was not granted.

In some ways, copyright issues at the output stage may be easier to resolve by applying existing jurisprudence on reproductions and transformative use, such as the Authors Guild v. Google in the US (which discussed reproductions of copyrighted books by Google as part of its Google Books project)<fnref n="132" /> or Infopaq International A/S v Danske Dagblades Forening heard by the European Court of Justice (which discussed reproductions in the creation of summaries of newspaper articles)<fnref n="133" /> or even the aforementioned Ultraman case from China. At the input stage, many copyright claims turn on specifics of the computational processes involved, suggesting an urgent need for collaborations between lawyers, policymakers and AI data scientists.

## B. Data protection

When it comes to data protection (and privacy)<fnref n="134" />, there are again mismatches between the way legal frameworks *understand* data flows and impose obligations on actors who use personal data on the one hand, and the *actual* data flows on account of computational processes in training foundation AI models on the other hand.

Most legal frameworks for personal data protection regulate the generation, use, sharing, storage, security and deletion of “personal data.” And most legal frameworks define “personal data” broadly – to include any information that relates to an identified natural

<footnote n="130">¹³⁰ “GEMA Files Model Action to Clarify AI Providers’ Remuneration Obligations in Europe”; “Fair Remuneration Demanded.”</footnote>

<footnote n="131">¹³¹ Foerg, “‘Awaken, the Warrior of Light’ What Lessons Can We Learn from Ultraman?”</footnote>

<footnote n="132">¹³² Authors Guild v Google, Inc. 770 F. Supp. 2d 666 - Dist. Court, SD New York 2011 (District level); Authors Guild v Google, Inc. 804 F.3d 202 (2d Cir. 2015) (Federal appeal level).</footnote>

<footnote n="133">¹³³ Case C-302/10 Infopaq International A/S v Danske Dagblades Forening (Infopaq II) ECLI:EU:C:2012:16.</footnote>

<footnote n="134">¹³⁴ On the distinction between data protection and privacy, see generally, Kokott, J. & C. Sobotta (2013). The distinction between privacy and data protection in the jurisprudence of the CJEU and the ECtHR: International Data Privacy Law, 3, 222–28; Gellert, R. & S. Gutwirth (2013). The legal construction of privacy and data protection: Computer Law & Security Review, 29, 522–30.</footnote>
person or can reasonably render a natural person identifiable.<fnref n="135" /> On the one hand, modern day technologies like Big Data analytics and generative AI render “full” anonymization impossible, while on the other hand, legal frameworks for personal data are being interpreted to have a broad scope of applicability.

Against this context, we asked participants in our online workshop to identify the different data protection issues that arise at each stage of the AI lifecycle.

By synthesizing inputs from our workshop, together with our desk research, we identify 6 data protection issues that arise at 4 stages of the AI lifecycle:

<figure><img src="image_15.png" /><figcaption>Figure 9: Visual representation of data protection risks across the OECD AI lifecycle</figcaption></figure>

1. Removing personal data from training datasets v. justifying the inclusion of personal data in training datasets

One crucial data protection arises due to memorization of personal data by AI models.<fnref n="136" /> At the level of creating training datasets, it is important to ensure that either these datasets are thoroughly anonymized (which is practically difficult), or that they are created in compliance with data protection laws.

For instance, in a technical report released by OpenAI in the context of GPT-4, OpenAI states that training datasets for this model may include “*publicly-available personal information*”.<fnref n="137" /> The report goes on to elaborate the strategies adopted by OpenAI to

<footnote n="135">¹³⁵ See for e.g., Schwartz and Solove, “The PII Problem: Privacy And A New Concept Of Personally Identifiable Information”; Purtova, “The Law of Everything. Broad Concept of Personal Data and Future of EU Data Protection Law”; Makulilo, “‘A Person Is a Person through Other Persons’—A Critical Analysis of Privacy and Culture in Africa.”</footnote>

<footnote n="136">¹³⁶ See generally, Carlini et al., “Quantifying Memorization Across Neural Language Models.”</footnote>

<footnote n="137">¹³⁷ OpenAI et al., “GPT-4 Technical Report.”, pp 53.</footnote>
prevent misuse of personal information – including removing such information from
training datasets, fine-tuning the model to reject requests that reveal personal
information, and creating monitoring and reporting mechanisms for users.<fnref n="138" /> But these
measures do not fully engage with the risk of *inference* of personal data by foundation
models such as LLMs.<fnref n="139" />

Further, data protection rights are usually not waived in the case of “publicly available
personal information”. In the EU for instance, the GDPR does not envisage waiver of rights
over personal data even if such data is publicly available. As a result, any re-use of such
data, including through web-scraping, needs to comply with the GDPR’s requirements on
legal basis of processing. This was echoed by the Italian Data Protection Authority in April
2023, when it issued an emergency order banning the use of ChatGPT in Italy.<fnref n="140" />

When data from the open web is re-used to for training foundation models, data protection issues attach themselves differently, depending on *how* personal data is obtained.

<figure><img src="image_16.png" /><figcaption>Figure 10: Potential sources of personal data in training datasets</figcaption></figure>

For instance, personal data such as an individual's browsing history or even an
individual's use of a foundation AI system like ChatGPT can be voluntarily donated by this
individual for AI research – inspired by projects like NYU's Ad Observatory, where
Facebook Users could voluntarily donate their feed data to enable research on
advertising algorithms.<fnref n="141" /> On the other hand, personal data can also be obtained
'indirectly'. Given that many well-resourced actors like Big Tech actors operate large
platforms as well as develop foundation models, they often repurpose personal data
flows from interactions on their platform, to train their foundation models. Or personal
data can be inadvertently included in training datasets created through web scraping, or
training datasets obtained from third parties.

<footnote n="138">¹³⁸ Id.</footnote>

<footnote n="139">¹³⁹ See Staab et al., "Beyond Memorization."</footnote>

<footnote n="140">¹⁴⁰ Garante Per La Protezione Dei Dati Personalı, 30 marzo 2023, 9870832, n. 112, Registro dei provvedimenti, 2023.</footnote>

<footnote n="141">¹⁴¹ See for e.g., Sanderson and Tucker, "Beyond Red Teaming."</footnote>
This has led to the issuance of guidelines by data protection authorities in France and UK, on the most suitable legal basis for the use of personal data in training AI models.<fnref n="142" /> In the EU, the GDPR envisages 6 legal justification for processing of personal data – *consent*, contract, legal obligation, vital interests, public task, and *legitimate interests*. The use of personal data in training AI models often triggers debate on the *first* and *last* legal justification. In some states in the US, consumer privacy laws also require collection and sharing of sensitive personal data (like biometric data) to be undertaken with consent of individuals.<fnref n="143" />

## Consent

When it comes to consent, operationalizing the concept of free and informed consent contained within many data protection laws<fnref n="144" /> is tricky. There is growing recognition of the need to understand and account for what Solon Barocas and Helen Nissenbaum describe as the background social tapestry of rights, obligations, and expectations within which consent functions, as opposed to rational choice-approaches to consent.<fnref n="145" />

A class action suit recently initiated in Colorado, US may hold interesting insights on consent tensions.<fnref n="146" /> This case concerns Photobucket – a private company that operates an online photo storage platform. In their complaint, the plaintiffs argue that Photobucket has licensed images hosted by it to generative AI companies, in violation of consumer privacy laws such as the Illinois Biometric Privacy Act – a legislation that requires companies that collect biometric identifiers or biometric information from Illinois residents to obtain informed consent from such consumers prior to collecting their biometric data, and publicly disclose to consumers the uses, retention of, and schedule for destruction of the biometric data collected.

In terms of consent-based data flows for foundation model training, structures like data altruism in the EU with clear guidelines for informed consent,<fnref n="147" /> or other forms of data stewardship through the use of data trusts piloted in the US, Switzerland and India<fnref n="148" />,

<footnote n="142">¹⁴² See for e.g., CNIL, “AI How-to sheets”; Information Commissioner’s Office, “Guidance on AI and Data Protection.” See also a resolution by the Global Privacy Assembly, passed with the assent of data protection authorities from Europe, Mexico, Argentina, Morocco, UK, Japan, Korea and Hong Kong, among others. Global Privacy Assembly, Resolution on Generative Artificial Intelligence Systems (October 2023).</footnote>

<footnote n="143">¹⁴³ See for e.g., The Biometric Information Privacy Act (BIPA), § 15(b), 740 ILCS 14/1 (2008); The Washington My Health My Data Act, § 19.373.030, Revised Code of Washington, Title 19 (2024).</footnote>

<footnote n="144">¹⁴⁴ See for e.g., Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC, 2016, O. J. (L 119), Articles 7 and 9(2)(a) (GDPR); Cal. Civ. Code. Title 1.81.5, § 1798.140(h) read with § 1798.135 (2018) (California Consumer Privacy Act); The Digital Personal Data Protection Act, 2023, § 4(1)(a) § 6 (India); The Data Protection Act, 2019, KENYA GAZETTE SUPPLEMENT NO. 181, § 30(1)(a) read with § 32 (Kenya); Lei Nº 13.709, de 14 de Agosto de 2018, Article 7(I) read with Article 8 (for personal data) and Article 11(I) (for sensitive personal data) (Brazil).</footnote>

<footnote n="145">¹⁴⁵ Solon Barocas and Helen Nissenbaum, ‘Big Data’s End Run around Anonymity and Consent’ in Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum (eds), Privacy, Big Data, and the Public Good (Cambridge University Press 2014) 64-65.</footnote>

<footnote n="146">¹⁴⁶ Complaint, Mac Pierce and Others v. Photobucket Inc and Unknown Defendants, Docket No. 1:24-cv-03432 (Col D. C, 2024).</footnote>

<footnote n="147">¹⁴⁷ Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European data governance and amending Regulation (EU) 2018/1724, 2022, O.J. (L 152), Chapter IV.</footnote>

<footnote n="148">¹⁴⁸ See generally, Institute and Institute, “Enabling Data Sharing for Social Benefit through Data Trusts.”</footnote>
could be useful in accounting for the background social tapestry within which consent
functions. Other researchers have tried to synthesise data governance practices for AI
training, by drawing parallels from archives.<fnref n="149" />

Consent however, is complicated when generative AI companies obtain personal data
from third parties (as opposed to directly from individuals). To this extent, the Italian Data
Protection Authority required OpenAI to publicly disclose information about data sources
for training their GPT model, as well as provide tools for individuals to exercise the right
of opposition with respect to the processing of their personal data, obtained from third
parties.<fnref n="150" />

The other complicated aspect of consent, is the feedback loop – where user behaviour
data when a use interacts with an AI product such as ChatGPT is fed-back to fine-tune
the GPT model underpinning ChatGPT. Further, Big Tech players like Meta routinely
modify their terms of use to allow user-data gathered from an individual’s interactions
across Meta platforms to train Meta’s generative AI model Llama.<fnref n="151" />

Legitimate interest

AI developers also often rely on legitimate interest to justify the processing of personal
data, when sourcing training data through web scraping.

While the legal basis of legitimate interest is perhaps the most flexible, data controllers
seeking to rely on this legal basis must prove three conditions - identify a legitimate
interest relating either to their own activities or to third party-activities; show that the
processing is necessary to achieve it; and balance the processing against the individual's
interests, rights and freedoms. <fnref n="152" /> For instance, Meta justified it's decision to process user
data for AI training by arguing that it has “legitimate interests in processing data to build
these services and this means that people can object using a form found in [Meta's]
Privacy Centre if they wish” and that this “approach is consistent with how other tech
companies are developing and improving their AI experiences in Europe.” <fnref n="153" /> But in June
2024, the NGO noyb filed complaints before 11 Data Protection Authorities in Europe,
asking them to intervene in Meta's use of user-behaviour data for training AI on the basis
that such processing cannot be justified under the legitimate-interest legal basis. <fnref n="154" />

On the same issue, Brazil's Data Protection Authority issued a notice to Meta in July 2024,
temporarily suspending the Meta's repurposing of user data to train its generative AI
model, until the authority evaluated Meta's compliance with the legitimate-interest

<footnote n="149">¹⁴⁹ See for e.g., Jo and Gebru, “Lessons from Archives.”</footnote>

<footnote n="150">¹⁵⁰ Garante Per La Protezione Dei Dati Personali, 2 novembre 2024, 10085455, n. 755, Registro dei provvedimenti, 2024.</footnote>

<footnote n="151">¹⁵¹ See Meta, “Bringing Generative AI Experiences to People in Europe.”</footnote>

<footnote n="152">¹⁵² See for e.g., Mukuki and Assenga, “Comparative Study Of Data Protection Legislation Frameworks Across The East African Community,” 22–23; Kamara and De Hert, “Understanding the Balancing Act Behind the Legitimate Interest of the Controller Ground.”</footnote>

<footnote n="153">¹⁵³ Meta, “Bringing Generative AI Experiences to People in Europe.”</footnote>

<footnote n="154">¹⁵⁴ noyb, “Noyb Urges 11 DPAs to Immediately Stop Meta’s Abuse of Personal Data for AI.”</footnote>
justification under the Brazilian data protection law.<fnref n="155" /> After two rounds of review, Meta provided a compliance plan to the authority detailing how it would improve transparency and allowed for the exercise of data subjects rights, which resulted in the authority revoking the suspension in August 2024. Per Meta’s compliance plan, some actions to be undertaken by Meta are “sending email and app notifications to users at least 30 days before the beginning of the new processing, and providing a link with easy access to the form to opt out of the processing for both users and non-users of Meta’s platforms.”<fnref n="156" />

These discussions on the appropriate legal basis for processing personal data in training Al models also raise the issue of who is responsible to comply with these obligations. In the EU, entities known as data controllers, i.e. entities who determine the purposes and means of the processing of personal data in a given context, are required to find an appropriate legal basis for processing personal data.<fnref n="157" /> In the EU, this has been extended to cover joint data controllers – i.e. when more two or more entities make common or converging decisions about what personal data to process and for what purposes.<fnref n="158" /> Data Protection Authorities like the UK’s ICO are undertaking public consultations on how these concepts of ‘data controller’ should be transposed into the Al ecosystem, particularly in the context of open-source Al models where multiple actors can maintain, modify, study and deploy these models.<fnref n="159" />

These data protection issues have also resulted in increasing demands for the use of synthetic data for training Al models and in the use of privacy-enhancing techniques to remove/mask personal data from training datasets.<fnref n="160" />

## Revisiting old debates: Data openness v. data protection

Following from the Open Government Partnership launched in 2011, there was growing focus and funding for the release of open government datasets, as well as open datasets from other actors (commercial actors, research actors, etc). The objective was to increase transparency and accountability of government, as well as increase citizen participation in data-driven innovation and data-driven policymaking. But, many open datasets also contain information about identifiable individuals. The use of Big Data analytics to open datasets bringing forth tensions relating to identifiability of individuals, and therefore, tensions between data openness and data protection.<fnref n="161" />

<footnote n="155">¹⁵⁵ ANPD, “Meta cumpre exigências da ANPD e poderá retomar, com restrições, o uso de dados pessoais para treinamento de inteligência artificial.”</footnote>

<footnote n="156">¹⁵⁶ Badillo, “Processing of Personal Data for AI Training in Brazil.”</footnote>

<footnote n="157">¹⁵⁷ Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC, 2016, O. J. (L 119), Article 4(7).</footnote>

<footnote n="158">¹⁵⁸ See generally, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC, 2016, O. J. (L 119), Article 4; European Data Protection Board, “Guidelines 07/2020 on the Concepts of Controller and Processor in the GDPR”; Millard, “At This Rate, Everyone Will Be a [Joint] Controller of Personal Data!”</footnote>

<footnote n="159">¹⁵⁹ ICO, “Generative AI Fifth Call for Evidence.”</footnote>

<footnote n="160">¹⁶⁰ See for e.g., Commission Nationale de l’Informatique et des Libertés, “Relying on the Legal Basis of Legitimate Interests to Develop an AI System.”</footnote>

<footnote n="161">¹⁶¹ See for e.g., Scassa, “Open Data & Privacy.”</footnote>
This led to the development of legal strategies as well as technical tools, for ensuring
that open datasets comply with data protection requirements. For instance, Lorenzo
Dalla Corte identifies three stages of open government data production, where the
inclusion of personal data should be justified by a legal basis:

“1. when the data is initially gathered by the initial data controller, e.g. a local government agency;

2. when the initial data controller disseminates the information to third parties (reusers, e.g. a software company) as open data;

3. when a data reuser acquires and processes the information released."<fnref n="162" />

Along these stages, Beatriz Botero Arcila argues for the use of techno-legal approaches to balance transparency against data protection, such as introducing certain frictions into decisions about what information is revealed in open datasets, and through what modalities.<fnref n="163" /> This frictions can include administrative processes to determine the need for personal data in an open dataset, as well as technical tools like file formats and secure processing rooms to access and re-use open data. In doing so, “pure” techno-legal data openness can be replaced with a more purpose-driven approach, to balance the need for open datasets against data protection.

This body of work is becoming relevant again, when the open web is re-used to build
training datasets and when these training datasets are made available on open terms.
The release of open training datasets is necessary to disrupt the current Big Tech-
controlled political economy of foundation model development. At the same time,
data protection authorities must continue to issue guidances as well as investigate the
practices of these well-resourced actors, when the open web is re-used to build
training datasets, and personally identifiable information is either included in these
training datasets or revealed in generative outputs.

2. Exercising data rights, including the right to deletion

The other set of data protection tensions arise once a model has been trained on certain
datasets, and learns a set of parameters. Unless personal data is regurgitated by the
model as an output (where such regurgitation is evidence of memorization), it's difficult
to 'see' the traces of personal data within the model.<fnref n="164" />

This brings forth challenges in enforcing data rights – such as the right of an individual to
seek deletion of personal data. Researchers have documented how AI developers like
OpenAI and Mid Journey have *not* complied with data rights requests, such as the right to
information and the right to seek erasure of personal data.<fnref n="165" /> In 2024, the Italian Data
Protection Authority fined OpenAI 15 Million Euros and required OpenAI to launch a 6-

<footnote n="162">¹⁶² Dalla Corte, “The European Right to Data Protection in Relation to Open Data.”</footnote>

<footnote n="163">¹⁶³ Botero Arcila, “Future-Proofing Transparency.”</footnote>

<footnote n="164">¹⁶⁴ See for e.g., Carlini et al., “Quantifying Memorization Across Neural Language Models”; Rus schemeier, “Generative AI and Data Protection.”</footnote>

<footnote n="165">¹⁶⁵ Harlan and Brunner, “We Are All Raw Material for AI.”</footnote>
month informational campaign to “promote public understanding and awareness of the functioning of ChatGPT, in particular on the collection of user and non-user data for the training of generative artificial intelligence and the rights exercised by data subjects, including the rights to object, rectify and delete their data.”<fnref n="166" />

Data protection authorities – like the European Data Protection Board in the EU, CNIL in France and the ICO in the UK – have issued guidances on facilitating exercise of data rights in relation to AI systems. The CNIL recommends use of the data minimization principles when building training datasets, while also ensuring that training datasets are built with findability and indexability in mind to ensure that data rights such as the right of access, rectification and deletion can actually be undertaken.<fnref n="167" /> The ICO has undertaken public consultations for the use of computational techniques that preserve and enable data rights – such as the use of input and output filters when an individual interacts with an AI model to filter out personal data from prompts and results respectively, as well as the use of techniques like machine unlearning.<fnref n="168" />

One another way out of this quandary could be to strengthen privacy-preserving techniques (like differential privacy) at training and fine-tuning stages, but these computational techniques as well as sustainability considerations surrounding these techniques are still evolving.<fnref n="169" />

**Collectivising the exercise of data rights, to ensure techno-legal data openness as well as community data sovereignty**

Collective data governance approaches in the context of personal data are becoming popular. For instance, personaldata.io is a Geneva-based public interest association that assists individuals in exercising their data rights against platforms and Big Tech actors. They have been instrumental in gig workers using data rights to obtain information about algorithmic decision-making systems by platforms like Uber. Other researchers have studied how civil society actors (such as non-profit organizations, journalists and individuals) have used the right of access under the EU GDPR to achieve collective goals.<fnref n="170" /> Similar collective exercises of data rights against AI developers – particularly well-resourced actors like Big Tech – can aid in opening up the AI development black box, as well as enable more study into the inclusion and flow of personal data across the AI lifecycle of a foundation model.

### C. To sum up...

<footnote n="166">¹⁶⁶ Garante per la protezione dei dati personali, “COMUNICATO STAMPA - ChatGPT.”</footnote>

<footnote n="167">¹⁶⁷ Commission Nationale de l'Informatique et des Libertés, “Respect and Facilitate the Exercise of Data Subjects' Rights.”</footnote>

<footnote n="168">¹⁶⁸ Information Commissioner's Office, “Generative AI Fourth Call for Evidence.”</footnote>

<footnote n="169">¹⁶⁹ See for e.g., Novelli et al., “Generative AI in EU Law.”</footnote>

<footnote n="170">¹⁷⁰ Mahieu and Ausloos, “Harnessing the Collective Potential of GDPR Access Rights.”</footnote>
The discussions above illustrate how legal frameworks for copyright, the sui generis database right and data protection come into tension with the pursuit of data openness.

On the one hand, copyright and sui generis right – as exclusive economic rights granted to creators – seek to incentivize knowledge production and innovation by restricting data openness. At the same time, these legal frameworks have also evolved beyond strict utilitarian approaches to intellectual property, as evident in fair use jurisprudence in common law countries and in the text-and-data mining exception in EU law.

On the other hand, legally-mandated or legally-enabled data openness comes into tension with data protection. Even where legal frameworks enable TDM or even the release of training datasets as open data, there are complex data protection issues involved that are yet to be fully resolved. And even if these issues are resolved, the distributional benefits of data openness are not equal – meaning that increased availability, findability and quality of training datasets can continue to benefit well-resourced actors like Big Tech, while contributing little to no value to digital and data commons that are at the heart of training datasets.
## V. Licenses – enabling, reshaping and contesting data openness

Another set of ongoing legal controversies relate to the use of openly/permissively licensed creative works and datasets for training foundation AI models. Here, licenses enable techno-legal data openness, by making these artefacts legally available for re-use. But, it is difficult to track the traces of a licensed artefact across the AI lifecycle. And as a result, it's also difficult to track and enforce compliance of open/permissive licenses, as licensed artefacts are compiled into training datasets which are then used to train foundation AI models.

This issue is sharply in focus in the class action suit filed by a group of software coders in California in 2022, against GitHub, Microsoft and OpenAI, referenced at the start of this report.<fnref n="171" /> As stated above, this case concerns Github CoPilot – a subscription-based AI product to support pair programming software. Github CoPilot is powered by a foundation model developed by OpenAI known as Codex, which has been trained and fine-tuned on free and open-source software hosted on GitHub.

The plaintiffs in this case are the licensors of open-source code hosted on GitHub. In their original complaint, the petitioners alleged that the training of Codex on their openly licensed code hosted on Github and the subsequent deployment of Codex via the CoPilot violates copyright and involves tampering of copyright management information. Both these claims were dismissed.<fnref n="172" /> However, the plaintiffs modified their complaints to include claims relating to breach of contract. The court decided to hear, in the instances where openly licensed code was used to train Codex.<fnref n="173" />

Across all their complaints, the petitioners argue that Codex does not write code like a human, but ingests pieces of code from the training data and produce outputs that are near-verbatim replicas of the petitioners' openly licensed code without any attribution.

On the one hand, licensors can sue creators of derivatives for copyright infringement, if the derivative-use violates the conditions of the license.<fnref n="174" /> One way out of this, is for creators of derivatives to argues that they engage in fair use. This was the argument advanced by Google and upheld by the US Supreme Court in the famous *Google LLC v. Oracle America Inc* case in 2021.<fnref n="175" /> The case dealt with the alleged copying of more than 11,000 lines of declaring code by Google from the Java API owned by and openly licensed by Oracle under a GNU GPL 2.0 license. This license required derivate code to be freely shared, which was not done by Google. But, the US Supreme Court held that Google's

<footnote n="171">¹⁷¹ See also, Emanuilov and Margoni, “Forget Me Not.”</footnote>

<footnote n="172">¹⁷² Order granting in part denying in part motion to dismiss dated January 3, 2024, *J. Doe 1*, *J. Doe 2*, *J. Doe 3*, *J. Doe 4*, And *J. Doe 5 v. GitHub Inc and Others*, Docket No. 4:22-cv-06823 (N. D. Cal. 2022).</footnote>

<footnote n="173">¹⁷³ Order granting in part denying in part motion to dismiss dated June 24, 2024, *J. Doe 1*, *J. Doe 2*, *J. Doe 3*, *J. Doe 4*, And *J. Doe 5 v. GitHub Inc and Others*, Docket No. 4:22-cv-06823 (N. D. Cal. 2022).</footnote>

<footnote n="174">¹⁷⁴ See for e.g., Jaeger, “Enforcement of the GNU GPL in Germany and Europe.”</footnote>

<footnote n="175">¹⁷⁵ *Google LLC v. Oracle America Inc*, 593 U.S. 1 (2021).</footnote>
actions qualified as fair use (without definitively ruling on whether APIs and their declaring code is copyrightable).

On the other hand, licensors can also sue creators of derivatives for breach of contract. This is especially useful in cases where dual-licensing schemes are adopted by licensors, i.e. where re-users can either sign on to a commercial license for re-use or are bound by an open license like GNU GPL which allows royalty-free re-use but subject to compliance with conditions of openness specified by the licensor in the GNU GPL.176 As in the CoPilot complaint, this is often used by licensors to claim damages for use of their openly licensed content/data without complying with license terms of attribution or share-alike. There is some precedent in the US on this topic – particularly from the ruling of a California District Court in 2017 in the case of Artifex Software Inc v. Hancom Inc.177 In this case, the court ruled that non-compliance by a re-user with the terms of an Affero GPL license can entitle the licensor to claim breach of contract. A similar ruling was also issued by the Court of Appeals in France.178

By and large however, the ability of creators to open the black box of foundation AI models and unearth the extent to which their copyrighted/licensed works have been ingested by these models is low. This is further compounded by the fact that there are very few legal requirements on model developers to either openly license training datasets, or share detailed information about training data. The AI Act in the EU is the first legislation to require developers of foundation AI models to publicly disclose a “sufficiently detailed summary” of their training data.179 To ensure implementation of this requirement in a manner that enables comprehensive transparency, as well as copyright holders to contest the use of their copyrighted/licensed material in training AI models, Open Future and the Mozilla Foundation released a draft blueprint of the training data disclosure.180 This blueprint differentiates between training datasets and data sources of constitutive elements of each training dataset, requiring developers to share detailed information about both.

And when it comes to the use of existing open source or open content licenses, these are also beset with issues. To the extent training datasets are licensed under CC, GNU GPL or other existing open content / permissive licenses, it is not clear whether attribution and share-alike obligations attached to the dataset transfer to the model and to the outputs of an AI system. This goes back to the issue of whether model weights, parameters and outputs are considered derivatives or not. While ongoing legal

<footnote n="176">176 The choice of petitioners to advance either copyright claims, breach of contract claims or both is determined by litigation strategy. For instance, when both copyright and breach of contract claims are advanced, defendants will try to dismiss the breach of contract claim by arguing that it is an issue of state law and is therefore preempted by the copyright claim, which is an issue of federal law. There is no consistent precedent on this issue. See for e.g., Madison, “Reconstructing the Software License.”</footnote>

<footnote n="177">177 Artifex Software, Inc. v. Hancom, Inc., Case No.16-cv-06982-JSC (N.D. Cal. Sep. 12, 2017).</footnote>

<footnote n="178">178 See for e.g., Stevenson and Lee, “Wake-up Call for Open Source Users.”</footnote>

<footnote n="179">179 Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828, 2024, O. J. (L 2024/1689), Article 53(1)(d) (EU AI Act).</footnote>

<footnote n="180">180 Warso, Gahntz, and Keller, “‘Sufficiently Detailed Summary’ — v2.0 of the Blueprint for GPAI Training Data.”</footnote>
controversies against generative AI companies could settle the last issue with regard to
outputs, the remaining aspects still remain unclear.

On the flip side, when AI models, weights and/or parameters are released under open-source licenses, while the training dataset remains closed, the freedoms of free-and-open-source software cannot *actually* be realized. This issue has come into sharp debate in the context of the Open Source AI definition released by the Open Source Initiative. This definition extends all four freedom of free software – the freedom to copy, share, study and create derivatives – to AI systems.<fnref n="181" /> While this definition has some endorsements, it has also come under criticism for enabling openwashing. This is because critics argue that in order to realise the four freedoms of the Open Source AI definition, access to training data for an AI system is necessary. Without training data, the benefits of openly licensed models, weights, parameters and other components do not resemble the benefits of openly-licensed code. To this extent, other civil society actors like the Open Source Alliance have created the Open Weights definition, to distinguish between truly open AI systems (where all components, including training data, are openly licensed) and the current status-quo of partially open AI system where only model weights are released under open licenses.<fnref n="182" />

The relevance of existing open and permissive licenses in the AI training context

Open source, open content and open databases licenses emerged in response to
restrictive intellectual property frameworks that limited remixing. Open-source
software licenses also enabled transparency, by allowing third parties to study
software, identify any vulnerabilities, and collectively fix these vulnerabilities. In other
words, these licenses are key to ensuring techno-legal data openness. These legal
instruments continue to remain relevant, to ensure that more actors can re-use data
from the open web.

On the one hand, the objectives and legal logics of open-source software licenses
cannot be directly transposed to AI systems. As illustrated by the controversies around
the Open Source AI definition, the ability to copy, share, study and create derivatives of
AI systems depends on openness of *all* its components – the training data, model
weights, parameters and code.

On the other hand, the inclusion of openly licensed artefacts into training datasets
does not automatically ensure transference of openness obligations either to the
training datasets themselves, or to AI models trained on these datasets.

This reinforces the current political economy of data re-use, particularly in the AI
context – where individual volunteers, socially conscious communities, and non-profit
organisation create the informational resources at the heart of the digital and data
commons (such as openly licensed creative works, openly licensed code, as well as
openly licensed/publicly-available training datasets) and drive the agenda of techno-

<footnote n="181">¹⁸¹ The Open Source Initiative, “The Open Source AI Definition v.1.0.”</footnote>

<footnote n="182">¹⁸² Open Source Alliance, “Open Weight Definition (OWD).”</footnote>
legal data openness, but well-resourced actors like OpenAI extract value from these informational resources without contributing value back to the digital and commons, and without releasing their training datasets and AI system components under open licenses.

## A. Alternative licensing frameworks for training datasets

Against this context, open data, open software and open science communities are creating new licensing frameworks for training datasets.

Together with existing open and permissive licenses, we discuss 6 alternative licensing frameworks – the Nwulite Obodo License, Kaitiakitanga Licenses, the Montreal License, the OpenRAIL Licenses, the Open Data Commons License, and the AI2Impact Licenses.

Each licensing framework valourises certain elements of data openness, but in different proportions as illustrated in Figure 11 below:

<figure><img src="image_17.png" /><figcaption>Figure 11: Sankey diagram of alternative licensing initiatives and their relevance for data openness framework</figcaption></figure>

In addition to these licensing initiatives, we also analyse data sharing contracts that are relied on by certain actors, which also reconfigure data openness.
# 1. New iterations of copyleft

Language data is one area in which new licensing efforts are ongoing, particularly from the Global South. One such effort relates to African language datasets. To the extent such datasets can be copyrighted or subject to the sui generis right, a team of researchers from the Data Science Law Lab of the University of Pretoria, South Africa released their Nwulite Obodo Open Data License.<fnref n="183" /> The creators ground their efforts in Nwulite Obodo – “building/raising community” in the Igbo language. The creators of this license identify their objective as “equitable open licensing”, where they recognize the benefits of traditional open data licenses, but also recognize the limits of these traditional licenses in ensuring benefit-sharing where African language datasets are used for AI training and development.

To this extent, the Nwulite Obodo license recognizes multiple licensors of a single dataset, and introduces a framework for identifying (as well as quantifying) contributions of various individuals/actors in the creation of an African language dataset.<fnref n="184" /> Thereafter, the Nwulite Obodo license binds licensees of an African language dataset to a dual-regime – licensees from developing countries received a royalty-free copyleft license, whereas licensees from countries *other than* developing countries need to pay for a license to use the dataset. The payment can be in the form of royalty paid to the dataset creators or in the form of a non-monetary benefit like a commitment to share-alike.

Community-created language datasets from the Global South – such as the Common Voice dataset – are in the process of adopting the Nwulite Obodo license, which can hold useful insights on the enforcement of these alternative licenses.<fnref n="185" />

In the context of research data and open science, researchers have long drawn attention to issues of extractivism, and the need to create standards, legal instruments and a research culture that indigenous communities do not remain “subjects” of research, but have a more active voice and role in the use of data about them in research and in value from such data re-use flowing back to these communities. Globally, these perspectives coalesced under the indigenous data sovereignty movement.<fnref n="186" /> Indigenous groups like the Te Hiku collective in New Zealand went on to create an indigenous data license known as the Kaitiakitanga License, based on the Maori data sovereignty principles.<fnref n="187" /> This license moves away from ownership and recognizes a community or a non-profit entity representing a community as the steward of data about that community. Further, the license can either prohibit commercial use, or can be modified to allow commercial use provided that profits from the use of community data is paid back to the community as royalties. In fact, Te Hiku Media recommends the latter when language data (like the Hawaiian language) is used by for-profit tech companies like Duolingo or by for-profit LLM developers – where the profits obtained by the companies from the use of their app or AI

<footnote n="183">¹⁸³ Data Science Law Lab, “LicensingAfricanDatasets.”</footnote>

<footnote n="184">¹⁸⁴ https://licensingafricandatasets.com/pre-licensing-guidelines?q=licensors</footnote>

<footnote n="185">¹⁸⁵ Heikkilä, “How This Grassroots Effort Could Make AI Voices More Diverse.”</footnote>

<footnote n="186">¹⁸⁶ See generally, Carroll et al., “The CARE Principles for Indigenous Data Governance”; Hudson et al., “Indigenous Peoples’ Rights in Data”; Carroll et al., “Operationalizing the CARE and FAIR Principles for Indigenous Data Futures.”</footnote>

<footnote n="187">¹⁸⁷ Te Hiku, “Data Sovereignty and the Kaitiakitanga License.”</footnote>
system is not paid to the companies' shareholders, but given back to the communities whose language enabled the creation of these profits.<fnref n="188" />

## 2. New bundle of rights, and associated use restrictions

Another set of have emerged from the Responsible AI movement. The Montreal License is one such initiative, where the creators of this licensing framework propose a new bundle of rights based on common data use practices in AI and ML communities - such as the right to use data/content for benchmarking, research, publication, internal use, model commercialization, or output commercialization.<fnref n="189" /> The creators of the Montreal license argue that at present, there is significant uncertainty on the ability to use data and software artefacts for AI development and deployment, which in turn is leading to issues in license compliance. Their proposed taxonomy therefore attempts to address license compliance through standardization – by linking openness obligations under a license, to data flows specific to AI training and development.

Another initiative is the OpenRAIL licensing frameworks, whose creators argue that licensors of code and model weights can include ethical use restrictions.<fnref n="190" /> The premise here, is that AI models are *inherently different artefacts* from software and a result, open-source software licenses cannot be the de-facto licensing framework for model code and weights. The Open-RAIL License generator includes an option to create a RAIL license for “data used to pre-train, train or evaluate a model” but this template is not public yet.<fnref n="191" />

## 3. Using licenses to balance techno-legal data openness with data protection

When it comes to the inclusion of personal data in training datasets, scrubbing, anonymization and privacy-enhancing techniques are more important than ever. But on the other hand, there are growing initiatives to expand the logic of copyright licenses to include data protection obligations on downstream re-users.

A useful initiative in this regard is the Open Data Commons License devised by Yaniv Benhamou and Melanie Dulong de Rosnay.<fnref n="192" /> While not specifically designed in the context of AI training data, the researchers create a template for a new license which contains additional preferences for licensors to respond to copyright as well as data protection concerns stemming from re-use of mixed datasets, as well as growing concerns of data extractivism. This license introduces a set of compulsory obligations, together with an expanded set of preferences for licensors. Compulsory obligations include the classic share-alike together with a privacy pledge (where licensees commit to comply with applicable data protection obligations triggered during re-use) and a data deletion pledge (where licensees commit to comply with an individual's right to seek deletion of personal data, if the individual exercises such right). Preferences for licensors include the classic attribution and derivatives/no derivatives of CC licenses. In addition,

<footnote n="188">¹⁸⁸ Te Hiku Media, “A Practical Guide to Creating Your Own Stewardship License.”</footnote>

<footnote n="189">¹⁸⁹ Benjamin et al., “Towards Standardization of Data Licenses.”</footnote>

<footnote n="190">¹⁹⁰ Muñoz Ferrandis, “OpenRAIL.”</footnote>

<footnote n="191">¹⁹¹ https://www.licenses.ai/rail-license-generator</footnote>

<footnote n="192">¹⁹² Benhamou and Dulong de Rosnay, “Open Data Commons Licences (ODCL).”</footnote>
the authors introduce other preferences – such as degrees of anonymity in the dataset, modality of data access (whether the dataset is released for public use under open standards, or released for controlled use), and financial conditions (where the dataset is made available for free, or for a fee that is used by licensors to manage the resource).

## B. New licensing frameworks – license proliferation, but no enforcement?

Enforcement of existing open source, open content and open database licenses were already an issue. As highlighted above, it is not clear if breach of an open license will be enforced as copyright violation or as a contract violation in different jurisdictions. Against this context, the proliferation of new licensing frameworks further complicates standardization when it comes to data re-use. Many researchers and practitioners note that open-source licenses work well, because there is a shared understanding among the software community on what uses are permitted and what aren't. But with the alternative licensing frameworks, there is no shared understanding, or even a baseline of permitted conduct. And instead of having a chilling effect, license proliferation enables more data extractivism.

### 1. Data sharing licenses and contracts...but only for the benefit of well-resourced actors like Big Tech?

AI developers – including Big Tech actors – rely increasingly on data licensing / sharing contracts to obtain access to proprietary data for training their foundation models.<fnref n="193" /> For example, OpenAI signed licensing agreements with news outlets like The Associated Press and Axel Springer, social media platforms like Reddit, and media libraries like Shutterstock.<fnref n="194" /> Mistral AI – which releases most of its models under open source licenses (and some benchmarking datasets as well) – has entered into a licensing agreement with Agence France-Presse.<fnref n="195" /> Academic publishers are also selling scholarly content to AI companies, to meet the latter's' training data requirements.<fnref n="196" /> A group of media companies and publishers recently filed a complaint against a Canadian Generative AI company in New York, USA.<fnref n="197" /> This case could hold interesting insights on the validity of these types of licensing agreements for AI training.

Data brokers are another popular data source for AI companies. AI companies rely heavily on data brokers for access to “bad” data, like images of nudity or violence – data which are necessary to train models that either filter out such content, as well as to train

<footnote n="193">¹⁹³ See for example, a tracker maintained by Toolkits, of licensing deals between generative AI companies and media companies/publishers - https://toolkits.com/publisher-monetization/ai-licensing/</footnote>

<footnote n="194">¹⁹⁴ Wiggers, “OpenAI’s Deals with Publishers Could Spell Trouble for Rivals”; Wiggers, “OpenAI Inks Deal to Train AI on Reddit Data | TechCrunch.”</footnote>

<footnote n="195">¹⁹⁵ Dillet, “Mistral Signs Deal with AFP to Offer Up-to-Date Answers in Le Chat.”</footnote>

<footnote n="196">¹⁹⁶ See for e.g., Gibney, “Has Your Paper Been Used to Train an AI Model?”; Kwon, “Publishers Are Selling Papers to Train AIs — and Making Millions of Dollars.”</footnote>

<footnote n="197">¹⁹⁷ Complaint, *Advance Local Media LLC and Others v. Cohere Inc.*, Docket No. 1:25-cv-01305 (S.D.N.Y. 2025).</footnote>
models to respond ethically to prompts for nudity or violence.<fnref n="198" /> And these data brokers
are often located in and obtain data from Global South countries, where regulatory
environments around data protection, data handling and data work are weaker.<fnref n="199" />

There are ongoing legal controversies that shed some light on the nature and content of
licensing contracts between AI companies and third-party data providers. One such
controversy is the case filed by Thomson Reuters against ROSS Intelligence – an AI and
legal research company - in Delaware, US in 2020.<fnref n="200" /> The case deals with violations of
Service Agreements between Thomson Reuters and a third party named LegalEase
Solutions, on the grounds that LegalEase unlawfully accessed, copied and shared
copyright-protected content from Thomson Reuters' databases with ROSS for AI training
and development. In a summary judgment issued in February 2025, the judge ruled in
favour of Thomson Reuters, confirming copyright infringement and absence of fair use.<fnref n="201" />

To the extent AI companies scrape data from the open web, they also open themselves
up to potential violations of website terms-of-service. In the LAION case discussed in
Section IV above, a German court held that content creators can register opt-outs to text-
and-data mining via website terms-of-service. This means that if AI companies do not
respect these opt-outs, they can be liable for copyright violation. In other ongoing legal
claims, such as those brought by Getty Images against Stability AI in the US and the UK,
Getty Images relies on website terms-and-conditions to advance its argument that
Stability AI used images from Getty's website without authorization. But these legal
controversies have not, in any way, stemmed the use of web-scraped data by AI
companies, or resulted in any meaningful changes to their data collection processes.

In fact, big AI developers often rely on one-sided contracts to make datasets available to
other actors in the AI ecosystem, as part of their (marketing) agenda to be considered
altruistic actors. In some cases, Big Tech actors do release some datasets under
permissive licenses like CC-BY – as in the case of the Open Materials 2024 dataset
released by Meta on Hugging Face in 2024.<fnref n="202" /> But these actors are also creating legal
instruments, as part of industry efforts to self-regulate data sharing. For instance,
Microsoft created a suite of standardised contracts for data sharing in the context of
training AI models.<fnref n="203" /> One of these contracts – the Computational Use of Data Agreement
(C-UDA) 1.0 - is specifically designed for datasets created using publicly-available
data.<fnref n="204" /> This contract states that the dataset can be used *only* for “computational
purposes”, which is defined to mean “use of the data for analysis by a computer.” The

<footnote n="198">¹⁹⁸ Paul, Tong, and Tong, “Inside Big Tech’s Underground Race to Buy AI Training Data.”</footnote>

<footnote n="199">¹⁹⁹ See generally, Perrigo, “Exclusive”; Casilli, *Waiting for Robots*.</footnote>

<footnote n="200">200 Complaint, *Thomson Reuters Enter. Ctr. GmbH and West Publishing Corp.*, v. *Ross Intelligence Inc.*, Docket No. 1:20-cv-613-SB (D. Del. 2020).</footnote>

<footnote n="201">201 *Thomson Reuters Enterprise Centre GmbH and West Publishing Corp. v. Ross Intelligence Inc.*, No. 1:20-cv-613-SB.</footnote>

<footnote n="202">202 Heikkilä, “The Race to Find New Materials with AI Needs More Data. Meta Is Giving Massive Amounts Away for Free.”</footnote>

<footnote n="203">203 In July 2019, Microsoft published the following templates: (i) the Open Use of Data Agreement (O-UDA), (ii) the Computational Use of Data Agreement (C-UDA) and (iii) the Data Use Agreement for Open AI Model Development (DUA-OAI). These were complemented by a fourth one in November 2019: (iv) the Data Use Agreement for Data Commons (DUA-DC). See “Open Data Collaboration and Sharing | Microsoft CSR.”</footnote>

<footnote n="204">204 https://github.com/microsoft/Computational-Use-of-Data-Agreement/blob/master/C-UDA-1.0_annotated.md</footnote>
rationale is explained in the annotated agreement released by Microsoft, which states that – “[this] agreement allows data provided under this C-UDA to be used for computational use (e.g., to train AI models), while not permitting other uses that might conflict with rights that may be held by third parties in the material within a database, such as broad rights to copy and distribute expressive works.” In doing so, Microsoft creates a distinction between data (which it seems to understand in a purely computational sense as numerical representations of facts to which copyright does not apply) and creative works (which could be represented as data, but for Microsoft, these are a distinct category of informational resources to which copyright applies). This agreement also pre-dates recent cases on copyright infringement by GenAI products where the distinction between data and creative works that constitute a dataset is not as sharp.

These examples illustrate the ways in which private legal instruments can be used / co-opted by Big Tech actors to feed data extractivism. At the same time however, there are initiatives to harmonise private law principles, to ensure a more equitable data economy. One such initiative is the Principles for a Data Economy, released jointly by the American Law Institute and the European Law Institute in 2021.<fnref n="205" /> These principles recognize the diversity of actors in data supply chains. They specify guidances for data supply and sharing contracts that accounts for this multiplicity of actors, as well as for different claims in data arising from privacy, copyright and trademarks.

## 2. New institutional structures for enforcement

Researcher and practitioners have also offered other institutional suggestions for the problem of license and contract enforcement.

The authors of the Open Data Commons License for instance, argue that similar to copyright management institutions, data trusts should be created to serve three objectives - pool datasets as trust property, monitor license compliance for downstream use of each datasets, and enforce the license on behalf of each of the data contributors, i.e. the trustees of the data trust.<fnref n="206" /> To this extent, the authors also argue for new metadata standards that can boost traceability of mixed datasets.

Another initiative was launched by the Allen Institute for AI in 2023. Allen Institute for AI is the creator of the DOLMA dataset – “a three-trillion-token English corpus built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopaedic materials”.<fnref n="207" /> Allen Institute for AI decided to license this dataset under a new responsible AI license known as AI2ImpACT.<fnref n="208" /> Allen Institute created a risk assessment framework for datasets based on which datasets can be classified as high-risk, medium-risk or low-risk. The AI2ImpACT licensing framework imposes different sets of obligations on downstream re-use depending on the risk categorization of the licensed dataset. In terms of enforceability, Allen Institute rightfully recognised the challenges associated with strict legal enforceability of licenses. As a

<footnote n="205">²⁰⁵ ALI and ELI, Principles for a Data Economy - Data Transactions and Data Rights.</footnote>

<footnote n="206">²⁰⁶ Benhamou and Dulong de Rosnay, “Open Data Commons Licences (ODCL),” 19–23.</footnote>

<footnote n="207">²⁰⁷ Soldaini et al., “Dolma.”</footnote>

<footnote n="208">²⁰⁸ Dumas, “The AI2 ImpACT License — A New Way to Think about AI Licensing.”</footnote>
result, they also created other technical tools to ensure compliance, inspired by
community norms. As Jen Dumas writes – “[e]ach AI2ImpACT license authorizes AI2 to
post notices on its website publicly identifying any licensee who violates the use-based
restrictions of the license”. Further, each AI2ImpACT license also requires licensees to
make public disclosures about the “inputs used to develop derivatives based on the
underlying artifacts (including identifying intended uses, funding sources, energy
consumption, and data provenance for the derivatives)”. However, the AI2Impact
licenses saw very little uptake. And in April 2024, the DOLMA dataset was
relicensed under the Open Database License – Attribution (ODbL – BY), to enable
more creative uses of the dataset.<fnref n="209" />

3. New concerns for interoperability

The flip side of license enforcement is license interoperability.

"Interoperability" itself has different meaning in different contexts.<fnref n="210" /> From a computational perspective, interoperability is the ability of different data and information systems to work with each other, even if the individual elements are different.<fnref n="211" /> Rob Kitchin identifies this type of technical interoperability as a step to create relationality among datasets, which in turn enables more remixing and value creation as part of the "data revolution".<fnref n="212" /> This form of interoperability involves interoperability between software and hardware components, interoperability between data formats (i.e. syntactic interoperability) and interoperability in the meaning of exchanged information (i.e. sematic interoperability).<fnref n="213" /> Efforts to achieve this type of interoperability typically involves standard-setting, and is starkly visible in certain domains – such as Linked Open- web data<fnref n="214" />, open geospatial data<fnref n="215" /> and open government data<fnref n="216" />.

From a legal perspective, legal interoperability involves the ability to combine
data/content/software without conflicts among restrictions imposed by the
creators/licensors and without having to seek authorization on a case-by-case basis.<fnref n="217" />
Concerns relating to compatibility of licenses were prevalent open source as well as
open content licenses.<fnref n="218" /> In the context of Creative Commons for instance, Melanie
Dulong de Rosnay identified two broad types of incompatibilities – one set of
incompatibilities relating to the legal nature of Creative Commons as licenses or
contracts, which impacts the conditions determining their validity and their

<footnote n="209">²⁰⁹ Allen Institute for AI, “Making a Switch — Dolma Moves to ODC-BY | Ai2.”</footnote>

<footnote n="210">210 See for e.g., Ali, Alexopoulos, and Charalabidis, “DATA INTEROPERABILITY AND THE OPEN DATA ECOSYSTEM.”</footnote>

<footnote n="211">211 See generally, Kubicek, Cimander, and Scholl, “Layers of Interoperability.”</footnote>

<footnote n="212">212 Kitchin, *The Data Revolution*.</footnote>

<footnote n="213">213 Doldirina et al., “Legal Approaches for Open Access to Research Data”; Maratsi, Charalabidis, and Alexopoulos, “A Categorization of Cross-Domain Semantic Interoperability Challenges for Open (Government) Data.”</footnote>

<footnote n="214">214 See for eg., Berners-Lee, “Linked Data - Design Issues.”</footnote>

<footnote n="215">215 See for e.g., Open Geospatial Consortium, “OGC Standards | Geospatial Standards and Resources.”</footnote>

<footnote n="216">216 See for e.g., Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information, 2019, O. J. (L 172), Recitals 13, 14, 32, 36.</footnote>

<footnote n="217">217 Doldirina et al., “Legal Approaches for Open Access to Research Data.”</footnote>

<footnote n="218">218 See for e.g., de Rosnay, “Creative Commons Licenses Legal Pitfalls: Incompatibilities and Solutions.”</footnote>
enforceability, and another set of intra-license incompatibilities such as incompatibilities among different CC options, different CC versions, and the use of CC in different jurisdictions.<fnref n="219" /> Bodies such as Creative Commons, Open Knowledge Foundation, OSI and the Linux Foundation issued reciprocity declarations of compatibility between some of their licenses as well as between their licenses and some open government data licenses. The extent to which similar declarations of reciprocity can and will be issued for alternative licensing frameworks like Open RAIL, the Montreal License or the Nwulite Obodo license is unclear, but such interoperability is necessary to ensure these licenses collectively serve to strengthen the digital and data commons.

## E. In conclusion...

The alternative licensing initiatives discussed in this section indicate a growing desire about researchers, practitioners and communities to use legal instruments are means to record different data sharing preferences, impose new openness obligations, and respond to the current political economy of AI. Framed differently, these licensing initiatives reveal a desire to move beyond techno-legal data openness, and engage more carefully with the desire to enable data re-use but with limits on re-use by well-resourced actors and with due regard for evolving community preferences on data sharing.

At the same time, it's difficult to track uptake of licenses, especially when uptake depends on voluntary actions of licensors. Some proxy metrics to track license uptake are available. For instance, for datasets hosted on platforms like Hugging Face, the platform records license information for each dataset, which can be extracted and studied to ascertain uptake of different licenses.<fnref n="220" /> The Data Provenance Initiative also undertook an audit of licensing conditions and provenance of more than 1800 text datasets<fnref n="221" />, and compiled their findings in an open dataset available on Hugging Face<fnref n="222" />. But outside of this, license creators have little insight into the uptake of their licenses – a point echoed by some of our interviewees as well.

In addition to uptake, alternative licensing frameworks also present new challenges for interoperability. Their ability to serve as nested legal strategies that collectively advance more openness of the open web remains to be seen. As a result, there is a need for more empirical as well as ethnographic research on these alternative licensing frameworks.

<footnote n="219">²¹⁹ Id.</footnote>

<footnote n="220">²²⁰ Hugging Face, “Licenses.”</footnote>

<footnote n="221">²²¹ Longpre et al., “A Large-Scale Audit of Dataset Licensing and Attribution in AI.”</footnote>

<footnote n="222">²²² Data Provenance Initiative, “DataProvenanceInitiative/Commercially-Verified-Licenses · Datasets at Hugging Face.”</footnote>
# VI. Towards healthy data commons, and the need for some frictions in data flows

By disentangling (part of) the law and political economy of data re-use from the open web to train foundation AI models, we have developed a three-pronged framework for data openness of training datasets. Per this framework, it is not enough to focus only on the techno-legal openness of training datasets, but also place some limits on the ability of well-resourced actors like Big Tech to extract from the open web as well as enable community data sovereignty.

Our three-dimensional framework sits within a rich tapestry of data governance discourse that moves beyond a binary approach of data openness, towards a more gradated approach to data openness.

* For example, in 2019, the OECD released a report on “Enhancing Access to and Sharing of Data”.<fnref n="223" /> This report recognizes that data access exists on a continuum, ranging from closed data (with access limited only to the data owner/controller), to data with limited access, to open or data (with access to the public at large). The report goes to advocate that instead of one-size-fit-all data policies, regulators and policymakers within OECD member countries should adopt a mix of strategies that enable data access and sharing across the data openness continuum.

* In 2023, the Digital Public Goods Alliance's Community of Practice on AI released a report outlining a gradient approach to openness of AI models.<fnref n="224" /> This report identified the intersections between “open AI” and “responsible AI”, and advocated for a non-binary approach to openness across components of an AI system when classifying such system as a digital public good.

* In 2024, the Columbia Convening – a group of civil society and research actors – released a similar openness framework specifically for foundation models.<fnref n="225" /> This framework again adopts a gradient approach to openness, and argues that an AI system can be situated along this spectrum depending on the degree of openness of its training data, its model and its code.

* Also in 2024, a group of open source and open science researchers released a model openness framework.<fnref n="226" /> This framework identifies three classes of openness – the open science class with the most openness (where datasets, model, research papers, parameters, model metadata and outputs are released under open licensed), the open tooling class being intermediate, and the open model class being the least open (with only model architecture, parameters, evaluation results, data cards, and technical reports being released under open licenses).

<footnote n="223">²²³ OECD, *Enhancing Access to and Sharing of Data*.</footnote>

<footnote n="224">²²⁴ Digital Public Goods Alliance Community of Practice, “Core Considerations for Exploring AI Systems as Digital Public Goods.”</footnote>

<footnote n="225">²²⁵ Basdevant et al., “Towards a Framework for Openness in Foundation Models.”</footnote>

<footnote n="226">²²⁶ White et al., “The Model Openness Framework.”</footnote>
* And in early 2025, the Open Source Imitative and Open Future released a report outlining a data commons approach to data governance for open source AI. The report “acknowledges the varied nature of data, from fully open to restricted datasets, and promotes innovative licensing models, such as data trusts and cooperatives.”<fnref n="227" />

## A. Context-specific legal strategies for data openness

Our analysis of ongoing legal controversies reveals the complexities involved in devising legal interpretations that further our three-pronged approach to data openness. On the other hand, alternative licensing frameworks hold valuable insights for data openness.

From our analysis, it is clear that there are multiple legal and quasi-legal strategies for data openness – ranging from standardized vocabulary/standards for text-and-data-mining opt-outs, to design-based strategies for data protection in training datasets, and alternative licensing frameworks beyond open source, open content and open database licenses. Ultimately, there is no one-size-fit-all data governance strategy for training datasets, and each of these legal strategies are important for data openness.

For a specific training dataset, we argue that a context-specific legal strategy is required. For instance, the choice of license for a training dataset depends on the type of informational resource in a training dataset, the actors involved in building this dataset, and the actors involved in re-using this training dataset to train a foundation AI model.

In addition to these context-specific legal strategies, we also need structural legal changes. For instance, copyright and data protection regulators need to consistently issue guidances on how to transpose legal imaginations of data flows onto the computational processes involved in training foundation AI models using data from the open web. At the same time, these regulators must also be attuned to and investigate the role of well-resourced actors like Big Tech in extracting from the open web.

We also argue that new legal institutions are necessary, for enforcement of these legal strategies. For instance, the proliferation of licenses recognizes shifting community preferences on data sharing, but these licenses need to be enforced as well. Here, rather than individual enforcement, collective enforcement through institutional structures like copyright management organisations and data trusts could be useful.

Legal strategies also need to be combined with participative design approaches. For instance, Creative Commons conducted a series of public consultations, which revealed that creators often have different perspectives on re-use of their works for AI training, depending on the type of AI model and the type of output created by the AI product.<fnref n="228" /> While these preferences are difficult to standardize, they nonetheless represent the wishes of creators. To this extent, Creative Commons developed the idea of preference signals – public indications by creators of their preferences on data sharing. While

<footnote n="227">²²⁷ Tarkowski, “Data Governance in Open Source AI.”</footnote>

<footnote n="228">²²⁸ See generally, Stihler, “Exploring Preference Signals for AI Training”; Tumadóttir, “Update and Next Steps on CC’s AI Community Consultation.”</footnote>
preference signals cannot be enforced on their own, they could potentially be combined
with other licensing frameworks to collectively pave the way for a new social contract for
the digital commons. This could also be a means to disrupt the political economy of AI,
where large commercial actors set industry standards for data re-use.<fnref n="229" />

A similar initiative is being undertaken by the GovLab, in collaboration with the Agence Française de Développement – as part of a project known as Social Licenses for Responsible Data Re-use.<fnref n="230" /> The idea here is the use participative techniques to create citizen assemblies where communities (from the Global South) collectively deliberate on their preferences with regard to data re-use in a particular context, and then record these preferences in an appropriate medium such as a data sharing agreement.

B. Data commons for openness of the web, and legal frictions for healthy data commons

At a more abstract level, what emerges from our analysis and recommendations is that data governance is necessary at two levels – *one*, at the level of data as property, where data artefacts like datasets and creative works are governed through licenses, and *two*, at the level of socio-technical relations through which data comes into existence and which are shaped by data flows, where community preferences around the generation and sharing of data are recognized within legal instruments.

To realise the promise of the open web as open *for all*, it is important to retain the shared nature of informational resources as well as the collective labour and social relations relating to these informational resources. In other words, the openness of the Internet depends on the health of its digital and data commons.

Melanie Dulong de Rosnay and Felix Stalder articulated a definition of digital commons as “*a subset of the commons, where the resources are data, information, culture and knowledge which are created and/or maintained online..[that are] shared in ways that avoid their enclosure and allow everyone to access and build upon them. The notion of the digital commons lies at the heart of digital rights, the political fight to expand, rather than restrict, access to information, culture and knowledge.*”<fnref n="231" /> Gijs van Maanen, Charlotte Ducuing and Tomasso Fia further articulated a definition for data commons as “*communities that collectively and sustainably govern data and their relationships.*”<fnref n="232" />

Because of the centrality of communities to the digital and data commons, legal
strategies need to be dynamic and contextual. Techno-legal data openness is focussed
on removing legal and technical barriers to dataset re-use – legal barriers like copyright,
and technical barriers like fragmented metadata standards or low findability of datasets
on the open web. But strategies that focus solely on techno-legal openness ignore the
politics of data re-use – where already-powerful actors like Big Tech benefit, while other
actors are missed out. Framed differently, techno-legal openness (while necessary in

<footnote n="229">²²⁹ Ross, “Six Insights on Preference Signals for AI Training.”</footnote>

<footnote n="230">²³⁰ Verhulst et al., “Responsible Data Re-Use in Developing Countries.”</footnote>

<footnote n="231">²³¹ Dulong de Rosnay and Stalder, “Digital Commons.”</footnote>

<footnote n="232">²³² Maanen, Ducuing, and Fia, “Data Commons.”</footnote>
some cases, such as open government data), seeks to make data re-use as smooth as
possible, when in fact, some degree of friction to data re-use to limit data extractivism by
Big Tech as well as recognize community data sovereignty is necessary.

As a first step to realizing this vision, we need to move past the dominant rhetoric that characterizes law as a negative inhibiting friction to data flows, and instead embrace the role of law as a necessary friction to data flows. In the AI context, Silicon Valley empowered by the current Trump administration casts all legal regulation as inhibitors to innovation.<fnref n="233" /> Legal regulations are also identified as potential roadblocks to European competitiveness in the AI race, trailing behind US and China – a position echoed by European politicians in the recently-concluded 2025 AI Action Summit in Paris.<fnref n="234" /> For instance, the European Commission recently revoked a draft legislation on AI product liability; likely a result of pressure from industry lobbyists who see these types of product liability regulations as a threat to their business models.<fnref n="235" />

Abeba Birhane notes that the history of computing is one that valourises frictionless
technologies, where friction is formulated as part of the problem, “when, in fact, friction
is the human condition, part of the human condition, we need friction in our daily
interaction, to make sense of the world, it's through friction, we realise our, you know,
common ground, we realise our differences, and we realise, you know, our own values.”<fnref n="236" />

Similarly, legal scholars Brett Frischmann and Susan Benesch argue that in an increasingly networked society where data flows are used to “re-engineer humanity” to make it more calculable and predictable, there is a need to “deliberately engineer friction into and between rather than out of various techno-social systems. Society could embrace politically, technologically, and socially engineered transaction and interaction costs.”<fnref n="237" /> They go on to propose the concept of “friction-in-design regulation” – legal mandates for speed bumps in data flows.<fnref n="238" /> Akin to legal mandates for speed bumps in residential neighbourhoods, they argue that friction-in-design regulations have the functional purpose of inducing behavioural changes among designers and developers of digital technologies for a social end.

We argue (perhaps provocatively) that one way to instantiate the importance of legal
frameworks for data flows and data openness, is to channel law's role as an enabler of
friction. The legal strategies analysed in this report – particularly the alternative licensing
frameworks and new institutional structures for license enforcement – can be created
and implemented using this friction-in-design approach. These legal strategies
instantiate certain necessary frictions to the re-use of data from the open web, but by
doing so, they enable the creation of healthy digital and data commons.

<footnote n="233">²³³ See for e.g., Chapman, “Elon Musk at Cato Conference.”</footnote>

<footnote n="234">²³⁴ See for e.g., Dastin et al., “Paris AI Summit.”</footnote>

<footnote n="235">²³⁵ Andrews, “European Commission Withdraws AI Liability Directive from Consideration.”</footnote>

<footnote n="236">²³⁶ Mackereth, “Abeba Birhane on Changing Computing Cultures.”</footnote>

<footnote n="237">²³⁷ Frischmann and Benesch, “Friction-In-Design Regulation as 21st Century Time, Place, and Manner Restriction,” 388–89. See also Bates, “The Politics of Data Friction.”</footnote>

<footnote n="238">²³⁸ Id.</footnote>
A draft proposal by the Spanish government serves as another example. In late 2024, the Spanish Ministry for Culture released a draft legislative proposal to “regulate the granting of extended collective licenses for the massive exploitation of works and other subject matter protected by intellectual property rights for the development of general-purpose artificial intelligence models.”<fnref n="239" /> Drawing from collective management of copyright, the draft proposal discusses the use of collective licensing as a legal instrument by which creators can collectively deliberate, authorize and set the terms for use of their creative works for AI training.

Together with diversity of legal strategies, we argue that for the collective goal of openness, these legal strategies must be interoperable. To this extent, interoperability of licensing frameworks as well as harmonized interpretation of national legal framework are necessary.

In conclusion, we believe that **participative tools to gather community preferences** on data sharing, **alternative licensing initiatives** that record these diverse data sharing preferences in legal/quasi-legal instruments, **new institutions** that can monitor license compliance and enforcement, and **new techno-legal norms** in the form of metadata standards, data formats and system-level interoperability requirements **collectively constitute the necessary legal frictions for data openness**. These discrete legal strategies also require for structural changes at the level of intellectual property frameworks, antitrust regulation and industrial policy. Conceiving of these strategies as frictions can allow us to realise data openness in more contextual ways, which in turn can help us realise the promise of the open web as *open for all*.

<footnote n="239">²³⁹ Nobre, “A First Look at the Spanish Proposal to Introduce ECL for AI Training.”</footnote>
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