We need to talk about AI and transparency!

That’s what we thought when we agreed to join the latest episode of the AI lab. podcast, fittingly titled “AI & the quest for transparency”.

Over the course of 20 minutes, Teresa walks the listeners through some of the key questions when it comes to the training of generative AI models and how it affects the text and data mining (TDM) exception laid down in Articles 3 and 4 of the EU Copyright Directive (see also our Policy Paper #15).

Disclaimer: Playback of the embedded video establishes a connection to YouTube and may lead to data being collected by and shared with third parties. Proceed only if you agree.

While the Directive clearly establishes the right to mine online content and use it to train machine learning algorithms, this right hinges on the possibility for rightsholders to opt out their works if the activity takes place in a commercial context. A key issue we are currently seeing is that there is a lack of transparency around the training of generative AI models, which makes it impossible to tell whether such opt-outs are being respected or not.

In the discussion, Teresa highlighted the need for more transparency regarding opt-outs but also across the copyright ecosystem as a whole. COMMUNIA has long advocated for the creation of a database of copyrighted works, which would contribute to managing the system of opt-outs.

The discussion ended with a call upon the European Commission to provide guidance and lead technical discussions towards establishing a clear, reliable and transparent framework for opt-outs for TDM (see also Open Future’s recent policy brief on this issue). Only through dialogue between the concerned stakeholders, led by an independent third party, will we be able to establish best practices that uphold Articles 3 and 4 of the Copyright Directive while providing a fair and balanced framework for the training of machine learning models.

Etching of a printer's press by Abraham Bosse.
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