Open Source AI Definition – Weekly update April 15

Having just exited a very busy week here are the two major milestones to know about.

Definition v.0.0.7 is out!

Access the definition here and the discussion of it here
The changelog:
- Incorporating the comments to draft v.0.0.6 and results of the working group analysis
- Removed reference to “the public” in the four freedoms, left the object (users) implied
- Removed reference to ML systems following the text “Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system”
- Separated the ‘checklist’ and made it specific to ML systems, based on the Model Openness Framework
- Described in highly generic terms the conditions to access the model parameters
A concern was raised regarding the checklist making training data optional, potentially undermining the freedom to modify AI systems. This echoes previous debates we have had and likely will continue to have, regarding access to training data.
Discussion on the need to clarify licensing terms to ensure compliance with Open Source principles, suggesting a change to “Available under terms that satisfy the Open Source principles”.
- Proposal to consider the Open Source Definition itself as a checklist and cautious approach suggested before dictating specific requirements for legal documents.
A comment on the definition rather than the forum clarified that there needs to determine whether the freedoms outlined in the Open Source AI Definition should be granted to the deployer or the end user, considering differing access levels and implications for openness

Four different working groups connected with four different AI systems (Llama-2, Pythia, Bloom and OpenCV) have been reviewing legal document and comparing them to the previous 0.0.6 checklist on the Open Source AI Definition
The goal was to see how well the documents align with the components as described in the checklist.
Go here to see the updated checklist
The changes can be described as follows:
- Added legal framework for model parameters (including weights). The framework proposes that, if copyrightable, model parameters can be shared as code

Added the five (5) data transparency components from v.0.0.6 to the checklist under the category “Documentation,” along with legal frameworks