#aicommons — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aicommons, aggregated by home.social.
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Interesting data from a new edition of the Foundation Model Transaprency Index - collected six months after the initial index was released.
Overall, there's big improvement, with average score jumping from 37 to 58 point (out of a 100). That's a lot!
The interesting fact is that researchers contacted developers and solicited data - interactions count.
More importantly, there is little improvement, and little overall transparency in a category that researchers describe as "upstream": on data, labour and compute that goes into training. And "data access" gets the lowest score of all the parameters.
More at Tech Policy Press: https://www.techpolicy.press/the-foundation-model-transparency-index-what-changed-in-6-months/
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Interesting data from a new edition of the Foundation Model Transaprency Index - collected six months after the initial index was released.
Overall, there's big improvement, with average score jumping from 37 to 58 point (out of a 100). That's a lot!
The interesting fact is that researchers contacted developers and solicited data - interactions count.
More importantly, there is little improvement, and little overall transparency in a category that researchers describe as "upstream": on data, labour and compute that goes into training. And "data access" gets the lowest score of all the parameters.
More at Tech Policy Press: https://www.techpolicy.press/the-foundation-model-transparency-index-what-changed-in-6-months/
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Interesting data from a new edition of the Foundation Model Transaprency Index - collected six months after the initial index was released.
Overall, there's big improvement, with average score jumping from 37 to 58 point (out of a 100). That's a lot!
The interesting fact is that researchers contacted developers and solicited data - interactions count.
More importantly, there is little improvement, and little overall transparency in a category that researchers describe as "upstream": on data, labour and compute that goes into training. And "data access" gets the lowest score of all the parameters.
More at Tech Policy Press: https://www.techpolicy.press/the-foundation-model-transparency-index-what-changed-in-6-months/
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Interesting data from a new edition of the Foundation Model Transaprency Index - collected six months after the initial index was released.
Overall, there's big improvement, with average score jumping from 37 to 58 point (out of a 100). That's a lot!
The interesting fact is that researchers contacted developers and solicited data - interactions count.
More importantly, there is little improvement, and little overall transparency in a category that researchers describe as "upstream": on data, labour and compute that goes into training. And "data access" gets the lowest score of all the parameters.
More at Tech Policy Press: https://www.techpolicy.press/the-foundation-model-transparency-index-what-changed-in-6-months/
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Interesting data from a new edition of the Foundation Model Transaprency Index - collected six months after the initial index was released.
Overall, there's big improvement, with average score jumping from 37 to 58 point (out of a 100). That's a lot!
The interesting fact is that researchers contacted developers and solicited data - interactions count.
More importantly, there is little improvement, and little overall transparency in a category that researchers describe as "upstream": on data, labour and compute that goes into training. And "data access" gets the lowest score of all the parameters.
More at Tech Policy Press: https://www.techpolicy.press/the-foundation-model-transparency-index-what-changed-in-6-months/
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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.
https://openfuture.eu/publication/commons-based-data-set-governance-for-ai/
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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.
https://openfuture.eu/publication/commons-based-data-set-governance-for-ai/
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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.
#aicommons #datacommons #datagovernance #ai
You can find the report and all the briefs on the UNU webpage: https://unu.edu/cpr/project/breakthrough-people-and-planet
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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).
#aicommons #datacommons #datagovernance #ai
https://www.linkedin.com/events/datagovernanceintheageofgenerat7127306901125521408/comments/