REVIEW 2 cited by
Acceptable Use Policies for Foundation Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
As foundation models have accumulated hundreds of millions of users, developers have begun to take steps to prevent harmful types of uses. One salient intervention that foundation model developers adopt is acceptable use policies: legally binding policies that prohibit users from using a model for specific purposes. This paper identifies acceptable use policies from 30 foundation model developers, analyzes the use restrictions they contain, and argues that acceptable use policies are an important lens for understanding the regulation of foundation models. Taken together, developers' acceptable use policies include 127 distinct use restrictions; the wide variety in the number and type of use restrictions may create fragmentation across the AI supply chain. Developers also employ acceptable use policies to prevent competitors or specific industries from making use of their models. Developers alone decide what constitutes acceptable use, and rarely provide transparency about how they enforce their policies. In practice, acceptable use policies are difficult to enforce, and scrupulous enforcement can act as a barrier to researcher access and limit beneficial uses of foundation models. Nevertheless, acceptable use policies for foundation models are an early example of self-regulation that have a significant impact on the market for foundation models and the overall AI ecosystem.
Forward citations
Cited by 2 Pith papers
-
Bridging the Data Provenance Gap Across Text, Speech and Video
A manual audit of nearly 4,000 text, speech, and video datasets finds AI training data increasingly comes from web and social media sources, carries hidden non-commercial restrictions, and remains Western-centric with...
-
Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
Current large language models show stigma and give clinically inappropriate responses to common mental health symptoms, so they should not be deployed as replacement therapists.
Discussion (0). Continue with ORCID to comment.