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

Training data is key to foundation 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”.

Over the course of 2024, Ramya Chandrasekhar from CIS (as part of the ODECO project), collaborated with Inno3 and the Open Knowledge Foundation to investigate 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, the research report highlights tensions between legal imaginations of data flows and computational processes involved in training foundation models.

Three takeaways from the research report:

A three-dimensional framework for data openness of training datasets.

While techno-legal openness is necessary, this report argues that the political economy of data re-use also necessitates legal strategies that impose certain limits on data extractivism by well-resourced actors like Big Tech on the one hand, and enable community data sovereignty on the other hand.

A repository of 41 ongoing legal controversies relating to copyright and data protection related to training foundation AI models.

The report contains this repository, as well as a detailed analysis of how these legal controversies either impact or advance three-dimensional data openness of training datasets.

A critical analysis of existing open licenses, permissive licenses, as well as certain alternative licensing frameworks for training datasets.

While these licensing frameworks impose more obligations on re-users and necessitate more collective thinking on interoperability, these licensing frameworks together with other legal and institutional changes 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.

Read the full report on HAL.

Or download the full report here:

 

CIS-CNRS @ Workshop on data governance for Open Source AI

Ramya Chandrasekhar and Renata Avila were invited by the Open Source Initiative, to a workshop on Open Data and Open Source AI.

The workshop was held in Paris, from October 10-11, 2024. It was organised by the Open Source Initiative and Open Future, and it was hosted by Linagora’s Villa Good Tech.

The workshop brought together participants from research, civil society and technologists – including from Creative Commons, the Open Knowledge Foundation, Common Crawl, Mozilla Foundation, GovLab and GIZ Fair Forward.

The objectives were to discuss data governance challenges in open source AI, and solutions to address data extractivism while preserving openness.

More information about the workshop can be found in a blogpost by the Open Source Initiative.

The workshop resulted in a white paper, authored by Alek Tarkowski of Open Future and co-designed with all workshop participants.

The white paper identifies the challenges in governing data that fuels open source AI. The white paper also offers a blueprint for a data ecosystem rooted in fairness, inclusivity and sustainability. Access the white paper here.

The Open Web As AI’s New Playground: Legal Frictions

Reflections from a virtual workshop conducted by Ramya Chandrasekhar

As foundation AI models (such as large language models and generative AI) are becoming more commonplace, it is also important to study the data sources used to build and train these data-intensive technologies. The quality of training data is significant determiner of the quality and accuracy of the outputs of these AI models. And one important source of training data is the open web – which includes data in the digital public domain, openly licensed data and content, as well as data that is publicly-accessible but may be subject to certain legal rights (like contracts, copyright and data protection).

Despite being the ‘open’ web, data and content on the open web is not ‘open’ for full and free re-use by all. Data and content from the open web is re-used by big technology companies to train proprietary AI models, with little to no value being provided by these actors back into the open web ecosystem or for maintaining open data resources. Cultural resources made available via the open web are also being appropriated by these actors, exacerbating digital colonialism. At the same time, content creators are increasingly adopting restrictive interpretations of intellectual property rights to limit techniques like web scraping, which can be helpful in limiting extraction from the open web by large technology companies, but also adversely impacts other stakeholders such as researchers who rely on web-scraped data. In other words, the ‘openness’ of the open web is facing new risks and challenges.

The Centre for Internet and Society of the CNRS (CIS-CNRS), together with support of Inno3 (an ODECO Partner Organisation) and the Open Knowledge Foundation, organised a 3-hour virtual workshop on November 23, 2024. The objective was to bring together practitioners, researchers and civil-society organisations working on AI, open data and open-source to discuss two questions: (i) what are the legal frictions involved in the re-use of data from the open-web to train foundation AI models, and (ii) what data governance strategies (including legal, technical and social measures) can help address these frictions and enable responsible re-use of open web data. This workshop builds on research undertaken by Ramya Chandrasekhar (ESR 4) during her ODECO professional secondment with Inno3 in 2024.

The workshop saw participation from individuals working in South Africa, USA, Canada, France, Germany, Singapore, Poland, Italy and United Kingdom. Some of these individuals were also involved in developing new licensing frameworks for AI training datasets. The workshop was very interactive, with participants undertaking discussions in both break-out rooms and during plenary. The break-out rooms were facilitated by Ramya Chandrasekhar of CIS-CNRS and Celya Gruson-Daniel of Inno3.

Participants identified many types of legal frictions – relating to copyright, data protection, website terms of use, compliance with open licenses, proliferation of open licenses, and training data transparency. The participants used a miro board, to identify the specific stages in the AI lifecycle where each legal frictions manifests.

Figure 1: Miro board of a break-out room facilitated by Ramya Chandrasekhar, displaying different legal frictions and the point at which these legal frictions manifest in the AI lifecycle


The workshop also yielded several illustrations of data governance initiatives – ranging from new licensing frameworks, new institutional structures for data re-use, and new technical measures such as web protocols for registering opt-outs from web-scraping.

Figure 2: Miro board of a plenary discussion, where participants hierarchised legal frictions based on urgency, and discussed on-going data governance initiatives

The inputs from the workshop will feed into a public report authored by Ramya Chandrasekhar. The report will be co-published by CIS-CNRS, Open Knowledge Foundation and Inno3. Stay tuned for more!


Cross-posted from the ODECO blog.

Open data and commons literature

Some ruminations on why I turn to commons literature to answer the questions of ODECO.

Commons can yield productive insights from an ecosystemic perspective

Central to ODECO is an ecosystemic perspective. ODECO starts from the premise that the generation and use of open data is not linear, but depends on multiple intersecting forces of competition and collaboration between various actors. In this regard, ODECO identifies 8 actor groups, and studies the roles played by these actors – non-specialised users, journalists, students, local government, regional/national government, NGOs, companies and open data intermediaries.

Literature and research from the digital commons lend themselves well to this ecosystemic approach. Digital commons research has yielded useful empirical research methods as well as a rich analytical framework to ‘see’ the different technical, social and institutional components of a complex resource ecosystem.

The realization of value from open data is a collective action problem

The other central aspect of ODECO is the question of how to realise value from open data.

The realization of value from open data as a common goal of all actor groups in an open data ecosystem. The challenge however, is in establishing a common understanding of what ‘value’ is, rendering the goal of value realization a shared but obfuscated goal. Collective action theory allows us to study why actors attribute different meanings to a common goal, and propose the incentives for heterogenous actors to act in the interest of long-term sustainability as opposed to short-term individual gains.

Further, the inherent nature of open data as a ‘constructed’ good is also important here.  Comparing open data to other information systems is useful here. The “good” in question is not open data per se, but “the functionalities that it affords, and the willingness (interests) and capabilities (resources) of the users to take advantage of those affordances.” (Constantinides and Barrett, 2015). This means that the generation, use and maintenance of open data is more sociotechnically dependent on the “heterogeneity of interests and resources of a distributed user base.” (Id.)

Commons literature is again well-suited to studying collective action problems, and proposing institutional mechanisms for resource management that are not centralized in either the state or in market-actors. Decentralised governance from open access commons (particularly commons-based peer production) can yield especially insightful research in this regard.

Commons-based governance recognizes the relationality of open data

Here, a quote from Purtova and van Maanen (2024) resonates strongly with me –

“The core strength of the commons literature in our view lies in its problem analysis. What distinguishes the commons from other classifications of data as an economic good is the ecological thinking that acknowledges the complexity of the data-related problems and draws attention to the broader societal, technical and economic context of production and use of data in connection to broader societal goals. Data commons push us to think about data-related problems and solutions in terms that are beyond data. This feature is observable to some extent in all versions of the data commons we reviewed but is especially apparent in Ostrom-inspired analyses reviewed under ‘Information – or data commons for broader societal goals’ which employ ecological thinking about resources to be governed and problems to be solved.”

This is crucial for the study of open data not as an end in itself, but as a means to something else – a more just, open, equitable society. The focus should not only be the generation of more open data or the turn towards more open data-driven decision making. Instead, the focus should be on who benefits from open data, who is missed out, and who decides.

Bibliography

Constantinides, P., & Barrett, M. (2015). Information Infrastructure Development and Governance as Collective Action. Information Systems Research, 26(1), 40–56. doi:10.1287/isre.2014.0542

Purtova, N., & van Maanen, G. (2023). Data as an economic good, data as a commons, and data governance. Law, Innovation and Technology, 16(1), 1–42. Doi:10.1080/17579961.2023.2265270


Translating research into impact. What can a critical data researcher do?

What is the role of researchers in technological studies? How can we collaborate across different fields and stakeholder groups? Together with 45 other researchers, journalists, and NGO professionals, we sought to answer these fundamental questions in a workshop hosted by the Center for Tracking and Society at Copenhagen University. In this blogpost, we would like to share some of our discussions and a list of actions we plan to implement.

The central theme of this workshop was impact. The role of researchers in data-related studies is becoming more and more a topic of debate as we face a huge transformation of society driven by new technologies. This is deeply intertwined with research ethics and social value of research itself. Should we be actors of change or trying to apply a value-free approach to knowledge generation? There is no end to this debate, but there are few points on which we agreed.

From an open data perspective, we are both drawn to a critical perspective in our research. For instance, we recognize that open government data enables citizens to obtain information about the use of public resources by governments, and, thus, they can use this data to hold governments accountable. This is widely recognized by scholars as one of the objectives of open data. But, as many researchers of critical data studies and science and technology studies have shown, the distributional benefits of open data are not equal. Citizens with a higher degree of technical skill (often white male) are the more dominant stakeholders in finding open data, creating and maintain applications from it like OpenStreetMaps, and conducting open data hackathons. Similarly, indigenous communities draw attention to the ways in which existing open datasets either don’t represent these communities, or represent them inaccurately.

There is also a politics involved in what data is made ‘open’. For instance, as Caterina notes in another blog post, in Belgium, the availability of spots in childcare bodies is open data does not reflect their accessibility by public transit. Scholars of science and technology studies have undertaken ethnographic research into how meteorological data and public transportation data are made open – the kinds of organizational decisions about how to source this data within government departments, merge them, format them, and release them as an open dataset.  From this ethnographic research, it’s useful to identify what motivates a public administration to release a certain dataset, and what datasets are not released as open data. This, in turn, allows us to critically examine the impact of open data on society.

Beyond just the traditionally discussed benefits of open data as boosting transparency and accountability of public administration, as well as encouraging innovation, a critical approach to open data allows us to probe into more specific questions – why a certain dataset is made open, what datasets are not open, who benefits, and who is missed out. This allows us to integrate concepts of social equity, vulnerability and justice into the open data agenda – which can allow the open data movement to move beyond techno-solutionism.

As researchers also interested in policymaking, this workshop on translating critical data and algorithm studies has been enriching for us in how to expand our interest in these disciplines, while finding more effective ways of communicating complex concepts from these disciplines to policymakers.

Some takeaways worth mentioning are:

  • Participants in studies can benefit from our insights.What if we started including an “Impact Appendix” in our consent forms? In this appendix, we could detail how we believe our research might benefit both participants and society as a whole.
  • Focus on communicating the social value of our researchrather than just listing publications. This can be reflected in our resumes or through informal/social media communications (e.g., LinkedIn).
  • Be mindful of the fact that much of our research is behind paywalls.As critical scholars, it’s our responsibility to connect with society and find ways to communicate science that consider varying resources and levels of research literacy. This could entail publishing in different avenues (beyond just journals) and through different mediums (e.g., audio and video), depending on time and resources available to us as researchers.

Authors

Ramya Chandrasekhar | Legal Researcher, CNRS (ODECO)
Caterina Santoro | PhD Researcher, KU Leuven (ODECO)

(Cross-posted from https://odeco-research.eu/?p=3990)