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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)


High value datasets – What can the EU learn from India?

In the European Union, the concept of “high value datasets” has been introduced by way of the Open Data Directive.

  • Recital 66 of the Open Data Directive states that certain open government datasets are associated with “important socio-economic benefits”.
  • The definition of “high-value datasets in Article 2(10) further elaborates by stating that these datasets are “associated with important benefits for society, the environment and the economy, in particular because of their suitability for the creation of value-added services, applications and new, high-quality and decent jobs, and of the number of potential beneficiaries of the value-added services and applications based on those datasets.”

Based on these attributes, the Open Data Directive requires public bodies to make these datasets available for re-use free of charge (in most cases), available as bulk downloads and accessible through APIs, and machine-readable.

In December 2022, the European Commission passed a regulation specifying different types of high-value datasets from 6 sectors – geospatial, earth observation and environment, meteorological, statistics, companies and company ownership, mobility.

Tim Davies has written about this “strong economic frame” adopted in the definition of high-value datasets. He writes that the benefits of open data cannot always be quantified by adding up the revenue of firms who use open data. Instead, value is realized in other ways too. For instance, he identifies a couple of other ways in which value is realized from open data – fostering risk reduction, increasing internal efficiency and innovation, enabling exercise of rights, realizing value through network effects, redistributing surplus value. As a result, he notes that we need new calculative logics to capture these types of value realisations.

The view from India

In India, the equivalent of the Open Data Directive is an executive policy known as the National Data Sharing and Accessibility Policy, 2012. This policy does not mention high-value datasets.

However, the vocabulary of high-value datasets became introduced into Indian law and policymaking since 2020. For instance, a parliamentary expert committee was set up in 2019 to recommend regulatory frameworks for non-government data. This committee released a report in December, 2020, which introduced the term high-value datasets. BUT, the committee defines high-value datasets as datasets that are “beneficial to the community at large and shared as a public good.” The report provides some illustrations, which ofcourse includes datasets that have the potential to create more jobs or enable more innovations. But the report also identifies datasets that are relevant for citizen engagement, poverty alleviation, financial inclusion, skill development and divert and inclusion as high-value datasets.  A later report released by NASSCOM – the National Association of Software and Service Companies in India – echoes a similar broad understanding of high-value datasets.

This illustrates a more balanced approach to high-value datasets in India – one that combines the economic value of open datasets with their social value.

Screenshot of India’s data portal showing high-value datasets as of September 11, 2024, 12:09 PM

At present, India’s open data portal hosts more than 15000 high-value datasets. Datasets relating to tuberculosis treatment outcomes, expenditure and progress of road construction projects in rural areas, public spending on welfare schemes, and tax revenue of the federal government – to name a few. This illustrates a different more socially-conscious approach to implementing high-value datasets. And in doing so, it offers a knowledge transfer opportunity for EU policymakers.

(Cross-posted from https://odeco-research.eu/?p=4126)


Open data licenses and use restrictions

One of the defining characteristics of open data is that is it free to use and re-use. Legal claims of copyright or the sui generis right over datasets make re-use difficult. Open data licenses allow dataset creators to provide pre-facto authorisations for re-use of their datasets. This helps contribute more open data to the ecosystem.

There are many types of open data licenses today. Some are created by government bodies, and applied to public sector information. An example is France’s License Ouverte – created by Etalab (the French department that manages France’s national open data portal). Some open data licenses are created by non-profit or advocacy organisations. The Community Data License Agreement managed by the Linux Foundation is one example. The Open Data Commons licenses managed by the Open Knowledge Foundation is another example. And then there are licenses originally devised for creative content, but apply to datasets as well. The Creative Commons licenses (with the exception of CC-NC) are an example.

The history of open data licenses is intimately connected to the open source and open science movements. Advocates of Free and Open Source Software (FOSS) like Richard Stallman firmly believed in four freedoms that were sought to be protected through open source licenses – the freedom to run a software program from any purpose, the free to study the program (by being provided access to the source code), the freedom to redistribute copies, and the freedom to distribute copies of modifications. These ‘freedoms’ translated to open data licenses as well.

From this perspective, one point of tension in open data licenses, is the concept of use-restrictions. For example, a CC BY-NC license does not allow re-use of the licensed material for commercial purposes.

With increasing re-use of copyrighted material as training data for Large Language Models, a new licensing framework known as ‘Responsible AI Licensing’ (RAIL) has emerged. In RAIL, licenses impose certain ethical use restrictions on datasets, software and models in the AI context. These include harmful use of AI models for generating personal data, harming minors, engaging in fully automated decision-making that has adverse effects on an individual’s legal rights, or exploiting the vulnerability of a particular group of people.

Most RAIL licenses were developed for software and AI models. AI2Impact licenses developed by the Allen Institute for AI extend their ethical licensing framework to training datasets as well. In fact, the Allen Institute released a training dataset known as Dolma, containing 3 million token of web data, under an AI2Impact license in 2023 (but changed the license to CC-BY in 2024).

Strict adherents of the open data movement would argue that use restrictions detract from the very essence of openness, as they limit a particular type of re-use. But on the other hand, certain uses of open data can have harmful effects on individuals and communities.

As artists around the world argue in legal claims against GenAI, the use of their creative content licensed under an open license to create a GenAI model which produces a very similar output to the creative works of such artists raises economic challenges for the artists as well as ethical challenges. This has led to some proponents of open data and open source to rally around Responsible AI Licenses, which contain some ethical use restrictions. So where do we draw the line? Should open data licenses should be revamped to include some kinds of use restrictions? Or is this against the fundamental idea of openness?

One set of reflections on these questions can emerge from the history of the open data movement. There is growing scholarship on how transparency was understood by the open data movement in its early days as part of open government initiatives, and how this has changed over time. This scholarship also engages with liberal and neoliberal conceptions of transparency. Engaging with this historical literature can help us situate open data licenses within the specific context in which they were created, and then evaluate whether this context has changed and whether therefore the licenses need to be revamped. For instance, perhaps open data licenses should be grounded in an understanding of transparency as in/visibility, or of transparency as observability, or as I explore in my forthcoming research, of openness as processes of selective revealing.

(Cross-posted from https://odeco-research.eu/?p=4064)


Open Data Commons in the age of AI and Big Data

Recap from CPDP.ai, 2024

Picture of 3 people seated on a black table, one of whom is talking. On the corner is a digital screenshot showing two more faces, of participants who joined online.

On May 22, 2024, the Centre for Internet and Society, CNRS convened a panel at CPDP.ai.  The panel brought together researchers and experts of digital commons to try and answer the question at the heart of the conference – to govern AI or to be governed by AI?

The panel was moderated by Alexandra Giannopoulou (Digital Freedom Fund). Invited panelists were Melanie Dulong de Rosnay (Centre Internet et Société, CNRS), Renata Avila (Open Knowledge Foundation), Yaniv Benhamou (University of Geneva) and Ramya Chandrasekhar (Centre Internet et Société, CNRS).

The common(s) thread running across all our interventions was that AI is bring forth new types of capture, appropriation and enclosure of data that limits the realisation of its collective societal value. AI development entails new forms of data generation as well as large-scale re-use of publicly available data for training, fine-tuning and evaluating AI models. In her introduction, Alexanda referred to the MegaFace dataset – dataset created by a consortium of research institutions and commercial companies containing 3 million CC-licensed photographs sourced from Flickr. This dataset was subsequently used to train facial-recognition AI systems. She referred to how this type of re-use illustrates the new challenges for the open movement – how to encourage open sharing of data and content, while protecting privacy, artists’ rights and while preventing data extractivism.

There are also new actors in the AI supply chain, as well as new configurations between state and market actors. Non-profit actors like OpenAI are leading the charge in consuming large amounts of planetary resources as well as entrenching more data extractivism in the pursuit of different types of GenAI applications. In this context, Ramya spoke about the role of the state in the agenda for more commons-based governance of data. She noted that the state is no longer just a sanctioning authority, but also a curator of data (such as open government data which is used for training AI systems), as well as a consumer of these systems themselves. EU regulation needs to engage more with this multi-faceted role of the state.

Originally, the commons had promise of preventing capture and enclosure of shared resources by the state and by the market. The theory of the commons was applied to free software, public sector information, and creative works to encourage shared management of these resoruces.

But now, we also need to rethink how to make the commons relevant to data governance in the age of Big Data and AI. Data is most definitely a shared resource, but the ways in which value is being extracted out of data and the actors who share this value is determined by new constellations of power between the state and market actors.

Against this background, Yaniv and Melanie spoke about the role that licenses can continue to play in instilling certain values to data sharing and re-use, as well as serving as legal mechanisms for protecting privacy and intellectual property of individuals and communities in data. They presented their Open Data Commons license template. This license expands original open data licenses, to include contractual provisions relating to copyright and privacy. The license contemplates four mandatory elements (that serve as value signals):

  • Share-alike pledge (to ensure circularity of data in the commons)
  • Privacy pledge (to respect legal obligations for privacy at each downstream use),
  • Right to erasure  (to enable individuals to exercise this right at every downstream use).
  • Sustainability pledge (to ensure that downstream re-uses undertake assessments of the ecological impact of their proposed data-reuse).

The license then contemplates new modular elements that each licensor can choose from – including the right to make derivatives, the right to limit use to an identified set of re-users, and the right to charge a fee for re-use where the fee is used to maintain the licensor’s data sharing infrastructure. They also discussed the need for trusted intermediaries like data trusts (drawing inspiration from Copyright Management Organisations) to steward data of multiple individuals/communities, and manage the Open Data Commons licenses.

Finally, Renata offered some useful suggestions from the perspective of civil society organisations. She spoke about the Open Data Commons license as a tool for empowering individuals and communities to share more data, but be able to exercise more control over how this data is used and for whose benefit. This license can enable the individuals and communities who are the data generators for developing AI systems to have more say in receiving the benefits of these AI systems. She also spoke about the need to think about technical interoperability and community-driven data standards. This is necessary to ensure that big players who have more economic and computational resources do not exercise disproportionate control over accessing and re-using data for development of AI, and that other smaller as well as community-based actors can also develop and deploy their own AI systems.

All panelists spoke about the urgent need to not just conceive of, but also implement viable solutions for community-based data governance that balances privacy and artists’ rights with innovation for collective benefit. The Open Data Commons license presents one such solution, which the Open Knowledge Foundation proposes to develop and disseminate further, to encourage its uptake. There is significant promise in initiatives like the Open Data Commons license to ensure inclusive data governance and sustainability. It’s now the time for action – to implement such initiatives, and work together as a community in realising the promises of data commons.

Author

Ramya Chandrasekhar | Legal Researcher
Ramya Chandrasekhar | Legal Researcher (CIS-ODECO)

Participants

Alexandra Giannopoulou | Digital Freedom Fund
Yaniv Benhamou | University of Geneva
Ramya Chandrasekhar | Legal Researcher
Ramya Chandrasekhar | Centre Internet et Société (ODECO)
Mélanie Dulong de Rosnay | Centre Internet et Société
Renata Avila | Open Knowledge Foundation

Funding Acknowledgement

Takeaways from the DCPC/CIS Policy Lab, May 2024

Fostering Public Support for Digital Commons

The Digital Commons Policy Council (DCPC) conducts scientific research to increase recognition for the digital commons and the voluntary work that creates these common goods. The DCPC is an informal think tank which was founded in 2021 at the University of Canberra, and builds on the earlier work of the Journal of Peer Production. The DCPC produces public reports based on empirical data, submissions to lawmakers, educational resources for schools, and scientific articles.

In 2022 the Société des Communs held two Policy Labs bringing together digital commons project representatives and members of government entities. Building on this work, the DCPC-CIS 2024 Policy Lab was held in Paris on May 30 and 31, in partnership with the Centre Internet et Société (CIS) of the French National Center for Scientific Research (CNRS). The Policy Lab was organised by Mathieu O’Neil, University of Canberra, with support from the Digital Infrastructure Insight Fund’s Katharina Meyer and from CIS Director Mélanie Dulong de Rosnay. The event was facilitated by O’Neil, and held thanks to the financial support of the Ford Foundation’s Technology and Society program and the Digital Infrastructure Insights Fund.

The event brought together thirty digital commons experts from Australia, France, Germany, Italy, the Netherlands, Norway, Sweden and the UK. They included representatives from digital commons communities, e.g., Civic Data Coop Liverpool, Framasoft, La Coop des Communs, Open Food Facts, Wikimedia France; from public organisations, e.g., Agence Nationale de la Cohésion des Territoires (ANCT), Direction Interministérielle du Numérique (DINUM), Sovereign Tech Fund; from civil society organisations, e.g., Collectif pour une Société des Communs, Inno3, Open Forum Europe, Open Future, Open Knowledge Foundation; and from academia, e.g., Institut Polytechnique de Paris, Fondazione Bruno Kessler – Digital Commons Lab, University of Dundee, Université Paris 8, Université de Technologie de Compiègne. Ramya Chandrasekhar represented ODECO in this workshop. It was a chance for participants to share experiences, present their work, and discuss long-term challenges and opportunities. New connections were made, and new plans were hatched.

The main purpose of the DCPC-CIS 2024 Policy Lab was to identify best practices and opportunities in the public institutions-digital commons communities space, and to develop tools for increasing their cooperation. Participants identified problems and solutions on Day 1, and sorted themselves into groups on Day 2 to collaboratively develop three resources.

  1. Public Support Best Practices Guide: The first group is identifying and documenting successful public support mechanisms in different countries that could be replicated elsewhere. Categories include government-led commons initiatives during emergencies (e.g., Italy’s open-source Covid contact-tracing app), academic-government collaboration projects (e.g., UK’s Data Trusts project), and public funding opportunities (e.g., France’s Call for commons or Appels à communs). This group is led by Angela Daly, University of Dundee, and Riccardo Nanni, Fondazione Bruno Kessler – Digital Commons Lab. Ramya Chandrasekhar and Melanie Dulong de Rosnay are contributors.
  2. Policy Proposals Mapping: The second group is mapping existing policy proposals to support the digital commons, starting with recommendations from participating entities and other key proposals. The goal is to create a list on Wikipedia for policymakers and advocates to access easily. Participants working on conversion into Wikidata reported that this was doable, if time-consuming. List variables include “addressee” (e.g., Local, National, Transnational), “policy instrument” (e.g., Nodality, Authority, Treasure, Organisation) and “approach to digital commons” (e.g., holistic, or specific: open source; open science; open hardware; open knowledge; open infrastructure; data commons). This group is led by Tom Fredrik Blenning of Electronic Frontier Norway, Valérian Guillier of the CIS-CNRS, and Jan Krewer from Open Future.
  3. Public Procurement Guide: The third group is creating a guide on public procurement issues for digital commons. This guide will compile policy proposals, legal procedures, and practical solutions to better integrate digital commons into public procurement processes. The group leaders are Célya Gruson Daniel from Inno3, Bastien Guerry from the French Interministerial Digital Directorate (DINUM), and Sébastien Schulz from the Université de Technologie de Compiègne. They plan to add more information to the guide through a hackathon organised by DINUM in Paris in the second half of 2024.

The DCPC will incorporate key findings from the Policy Lab in a forthcoming activity report. In addition, two easy-to-read DCPC Handbooks on Public Support Best Practice and on Procurement will be co-developed with group leads and released in several languages.

During a final debrief session Policy Lab participants reviewed how the event was organised, with debates informing future editions. Indeed, all present agreed that the Policy Lab was a useful event which should happen again. DCPC therefore plans to facilitate a follow-up event in September / October 2025, possibly in the UK, to continue bringing together researchers and practitioners working on the digital commons, and to develop new opportunities for collaboration. This will also provide an opportunity to incorporate more perspectives from the Global South and from indigenous communities (eg., Canada, NZ).

CIS will continue to be involved in future editions of the Policy Lab, to foster more research and public engagement on the digital and data commons.

[Cross-posted from https://dcpc.info/lab24/]

Participants from CIS:

Valérian Guillier
Valérian Guillier | Postdoctoral researcher (NGI Commons)
Ramya Chandrasekhar | Legal Researcher
Ramya Chandrasekhar | Legal Researcher (ODECO)
Mélanie Dulong de Rosnay | Director of research