#AI#GenerativeAI#AITraining#Copyright#FairUse#IP#DataCommons#Books: "This paper is a snapshot of an idea that is as underexplored as it is rooted in decades of existing work. The concept of mass digitization of books, including to support text and data mining, of which AI is a subset, is not new. But AI training is newly of the zeitgeist, and its transformative use makes questions about how we digitize, preserve, and make accessible knowledge and cultural heritage salient in a distinct way.
As such, efforts to build a books data commons need not start from scratch; there is much to glean from studying and engaging existing and previous efforts. Those learnings might inform substantive decisions about how to build a books data commons for AI training. For instance, looking at the design decisions of HathiTrust may inform how the technical infrastructure and data management practices for AI training might be designed, as well as how to address challenges to building a comprehensive, diverse, and useful corpus. In addition, learnings might inform the process by which we get to a books data commons — for example, illustrating ways to attend to the interests of those likely to be impacted by the dataset’s development." https://openfuture.pubpub.org/pub/towards-a-book-data-commons-for-ai-training/release/1
Open Future's newest white paper, authored by @zwarso and myself, addresses the governance of data sets used for #AI training.
Over the past two years, it has become evident that shared datasets are necessary to create a level playing field and support AI solutions in the public interest. Without these shared datasets, companies with vast proprietary data reserves will always have the winning hand.
However, data sharing in the era of AI poses new challenges. Thus, we need to build upon established methods like #opendata refining them and integrating innovative ideas for data governance.
Our white paper proposes that data sets should be governed as commons, shared and responsibly managed collectively. We outline six principles for commons-based governance, complemented by real-life examples of these principles in action.
We're excited about the new "How to Build a Data Cooperative" handbook draft that @opendatamcr's Data Cooperative Working Group has published! We commend the effort and hope to see more data co-ops emerge as a result.
10 days remain to sign the Cooperative Digital Infrastructure Manifesto! We've received signatures from groups all throughout the international solidarity economy, including Mondragon University's Team Academy. Help us to advocate for digital tools and policies that will support a robust and fair economy - add your signature by 12/31!
I participated yesterday in an expert workshop on Public-Private Partnerships in Global Data Governance, organized by the United Nations University Centre for Policy Research (UNU-CPR) and the International Chamber of Commerce (ICC).
I was also invited to prepare a policy brief that presented how the Public Data Commons model, which we have been advocating for, could be applied at global level for dealing with emergencies, and the broader poly-crisis.
It is exciting to see UNU explore data sharing policies within the context of the policy debate on the UN Global Digital Compact.
Worth noting is also the recent report of the High-Level Advisory Board on Effective Multilateralism, "A Breakthrough for People and Planet". One of the transofrmative shifts, "the just digital transition", includes a recommendation for a global data impact hub.
In my brief, I show how this impact hub could be designed as a Public Data Commons. I also highly recommend other briefs presented at the event, by Alex Novikau, Isabel Rocha de Siqueira, Michael Stampfer and Stefaan Verhulst.
In a month (7-8 December) I will be speaking at a conference on data governance and AI, organized in Washington, DC by the Digital Trade and Data Governance Hub. I am excited about this for two reasons:
first of all, we need to connect the policy debates on data governance and AI governance. The space of AI development offers new opportunities to develop, at scale, commons-based approaches that have been much theorized and advocated for, but not yet implemented.
and secondly, I am a deep believer in dialogue between the US and the EU. US is leading in terms of AI development itself, while EU will most probably be the first country to innovate in terms of AI regulation.
Please consider joining, either in-person or remotely (it's a hybrid event).
#AI#Commons#DataCommons#OpenSource#PublicGoods: "In short, the sharing economy’s world without money was built on top of a world where money was everything, and the bill has come due. Our data has not only been appropriated, but is increasingly used against us. It has become the fuel behind AI models whose power and influence on our lives we are just beginning to fathom, but that we can already see are not all positive, or not all to our advantage – especially to those most vulnerable in our societies.
Big Tech will continue to cling to the idea of data as a non-rival good, claiming that data extractivism is all done for our benefit. They may even promise that their AI models will be open-source public goods, which supposedly means that our stolen data will come back to us as a more useful product, capable of solving the world’s problems.
We must see these moves for what they are: Not the altruistic actions of benevolent corporations, but a way to avoid lawsuits, delay attempts at serious regulation and more importantly, justify the privatisation of the commons."