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🗞️"Transparency around the content used to train AI and information about how it was processed can support the legitimate interests of preventing discrimination and respecting cultural diversity." Learn more in this article by Maximilian Gahntz & Zuzanna Warso, published in Tech Policy Press.

techpolicy.press/how-the-eu-ai

Tech Policy Press · How the EU AI Act Can Increase Transparency Around AI Training Data | TechPolicy.PressTrade secrets can’t serve as a blanket excuse for intransparency, write Zuzanna Warso & Maximilian Gahntz.

"The Open Source Definition is an important step in defining the standard of openness in AI development. Still, it should be seen as just one position in a broader debate that needs to bridge positions of AI developers with those of other stakeholders."

– read our new analysis of the Open Source Initiative (OSI)'s definition of open source AI, by @tarkowski and @paulk

openfuture.eu/blog/the-open-so

Open FutureThe Open Source AI Definition is a step forward in defining openness in AI – Open FutureThis week, the Open Source Initiative released its definition of open source AI. This analysis considers its significance as a standard, its limitations, and the need for a broader community norm.

"Archival Images of AI", great new project from Sound & Vision.
It starts with a simple premise: you can usage heritage collections to make images of AI that are better than the ever-present illustrations of humanoid robots.
And turns out to be an opportunity to ask serious questions about heritage, the commons, and AI.
aixdesign.co/posts/archival-im
#aicommons #ai #DigitalHeritage

aixdesign.coArchival Images of AIRemixing and reusing digital heritage collections to craft better images of AI.

Dan Cohen and Dave Hansen wrote recently a really good piece on books, libraries and AI training (the piece refers to the paper on Books Data Commons that I co-authored).

They start with a well-known argument about levelling the field: without offering public access to training resources, AI monopolies will benefit from information asymmetries. Google already has access to 40 million scanned books.

They add to this a key point about libraries' public interest stance - and suggest that libraries could actively govern / gatekeep access to books.

This reminds me of the recent paper by Melanie Dulong de Rosnay and Yaniv Benhamou, which for me is groundbreaking - it proposes that license-based approaches to sharing are combined with trusted institutions that offer more fine-grained access governance.

So it's good to see that this line of thinking is getting traction.

authorsalliance.org/2024/05/13

Authors Alliance · Books are Big AI’s Achilles HeelBy Dave Hansen and Dan Cohen Image of the Rijksmuseum by Michael D Beckwith. Image dedicated to the Public Domain. Rapidly advancing artificial intelligence is remaking how we work and live, a revo…

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: techpolicy.press/the-foundatio

Tech Policy Press · The Foundation Model Transparency Index: What Changed in 6 Months? | TechPolicy.PressFourteen model developers provided transparency reports on each of 100 indicators devised by Stanford, Princeton, and Harvard researchers.

The Think7 Italy Summit is happening this week, with the theme “The G7 and the World: Rebuilding Bridges”.

We have been invited to write a brief on “Democratic governance of AI systems and datasets”, which will be presented tomorrow by @tarkowski .

The brief has been a joint effort of three organizations: Open Future Foundation, Centro Politiche Europee and MicroSave Consulting (MSC), with contributions from Renata Avila, Lea Gimpel, and @savi.

think7.org/event/t7-italy-summ

think7.orgT7 Italy Summit – The G7 and the World: Rebuilding Bridges | Think 7

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.

openfuture.eu/publication/comm

Open FutureCommons-based Data Set Governance – Open FutureIn this white paper, we propose an approach to sharing data sets for AI training as a public good governed as a commons.

In the UK, NESTA is launching a Civic #AI Observatory, with the goal of “to talk calmly and collaboratively about the potential civic applications of powerful technologies like AI” - in contrast to a “breathless and polarised AI discourse”.

I really like the focus on *calmness*, it’s something much needed in tech debates, and not often seen. ( @jamestplunkett , who’s leading this, has in recent months done some great writing about technology in a broader social context).

One question remains: will this be only UK focused, or broader? There are good reasons to keep such focus - largely to keep complexity at bay. But the AI debate is also fragmented between regions. There is a strong network of actors and a public debate in the UK that often feels just a bit insular. I hope this observatory will bridge this gap.

medium.com/@jamestplunkett/ann

Medium · Announcing the Civic AI Observatory - James Plunkett - MediumBy James Plunkett

In our latest opinion, Paul Keller shows how the European text and data mining exception, introduced in the Copyright Directive, creates a framework for regulating machine learning.

Paul argues that on the basis of this regulation a new form of collective action can be built. And a new balance between the rights of the creators and the users can be achieved.

openfuture.eu/blog/protecting-

Open FutureProtecting Creatives or Impeding Progress? – Open FutureAs generative machine learning (ML) becomes more widespread, the issue of copyright and ML input is back in focus. This post explores the Eu legal framework governing the use of copyrighted works for training ML systems and the potential for collective action by artists and creators.