In discussing the metrics we have been working on for AI use and consent (#66 and #68 ), we ended up taking so many different perspectives on this that it was deemed worth breaking them down into a taxonomy.
I was assigned to first-pass this taxonomy for the AI use metric and am attaching it below because it potentially broadens the scope too much in advance of FOSSY - allowing us to reframe the current metric in a smaller scope to get it published and iterate.
- Metric - Tool types in use
- Seeking more User stories
- Metric - Specific Tools in use
- As a community architect/company who uses lots of open source, I’d like to be able to quickly determine what tools are in use in the communities I depend on
- As a project maintainer, I want to be able to verify or be aware of the ways that my community members/contributors are (or aren’t) abiding by the policies that the community has consented to so it can inform my response/maintainance of a healthy community
- Seeking more User stories
- Metric - Model Type
- As a maintainer, I’d like to see an overview of the specific AI tools and models that are being used in my (or other) communities so I can compare the quality of different tools and learn about new quality frontier models as my contributors start using them
- As a researcher studying open source and AI use, I’d like to be able to understand which AI models and tools are the most popular across a large random sample of the open source ecosystem
- As a free software advocate, I want to know how well the models a community is using align with my preferred definition of "Open source" or "freely modifiable" AI
- Seeking more User stories
- Metric - AI tool owner
- As a community member, I’d like to know who controls the data behind the AI tools that I interact with in a community so I know where my data is and how I can assert my rights (i.e. GDPR)
- Seeking more User stories
In discussing the metrics we have been working on for AI use and consent (#66 and #68 ), we ended up taking so many different perspectives on this that it was deemed worth breaking them down into a taxonomy.
I was assigned to first-pass this taxonomy for the AI use metric and am attaching it below because it potentially broadens the scope too much in advance of FOSSY - allowing us to reframe the current metric in a smaller scope to get it published and iterate.