# Open Source AI needs to require data to be viable

**URL:** <https://discuss.opensource.org/t/open-source-ai-needs-to-require-data-to-be-viable/351>\
**Category:** Open Source AI\
**Created:** [May 19, 2024, 6:03pm UTC](https://discuss.opensource.org/t/open-source-ai-needs-to-require-data-to-be-viable/351 "2024-05-19T18:03:48Z")\
**Posts on this page:** 1\
**Showing post:** 13

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**Author:** ![Mark](https://yyz1.discourse-cdn.com/flex011/user_avatar/discuss.opensource.org/mark/32/177_2.png) [@Mark](https://discuss.opensource.org/u/Mark)\
**Post date:** [May 31, 2024, 7:05pm UTC](https://discuss.opensource.org/t/open-source-ai-needs-to-require-data-to-be-viable/351/13 "2024-05-31T19:05:15Z")

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We’ve been following the discussion with great interest. Stefano mentions “a few relevant efforts that support a degree of openness”. Ours is among them (see [opening-up-chatgpt.github.io](https://opening-up-chatgpt.github.io)) and we’ve supported the argument in multiple peer-reviewed papers as well as a contribution last October to one of the OSI deep dives.

I want to share a new FAccT’24 paper of ours in which we go into these matters in some detail. I don’t want to hijack this thread, but since Stefano pointed me to it and invited me to share this here I take the liberty of providing a quick pointwise summary:

1. We diagnose open-washing as a key threat of Big AI that hurts the open source ecology
2. We point out a possible loophole in the EU AI Act that provides “open source” models with exemptions (and thereby gives Big AI a strong incentive to open-wash)
3. We point out the risk of relying exclusively on a (binary) license for open source decisions
4. We argue for a _gradient_ and _composite_ notion of openness to disincentivize open-washing and other forms of audit-washing.
5. We draw attention to BloomZ’s RAIL license and propose this should count towards openness too, or at least should not necessarily detract from it (in line with what @Danish_Contractor says above).
6. We argue that datasets represent the area that is most lagging behind in openness. A definition that is vague on this risks being toothless and vulnerable to open-washing. (I agree with @juliaferraioli OP concerning this.)

We also question the wisdom of focusing on a binary definition. As we point out:

> A licence and its definition forms a single pressure point that will be targeted by corporate lobbies and big companies. The way to subvert this risk is to use a composite and gradient approach to openness.

Hopefully our evidence-based approach, survey of \>45 ‘open’ LLMs, and documentation of actual ways in which companies are already open-washing provides some useful empirical background to the discussions here.

Liesenfeld, Andreas, and Mark Dingemanse. 2024. ‘Rethinking Open Source Generative AI: Open-Washing and the EU AI Act’. In _The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24)_. Rio de Janeiro, Brazil: ACM. ([public PDF in our institutional repository](https://pure.mpg.de/pubman/item/item_3588217_2/component/file_3588218/liesenfeld_dingemanse_2024_FAccT_generative_AI_open-washing_EU_AI_Act.pdf))

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