On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
read the original abstract
Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses. Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics). This paper explores why and how these licenses have been adopted, and why and how they have been adapted to fit particular use cases. We use a mixed-methods methodology of qualitative interviews, clustering of license clauses, and quantitative analysis of license adoption. Based on this evidence we take the position that responsible AI licenses need standardization to avoid confusing users or diluting their impact. At the same time, customization of behavioral restrictions is also appropriate in some contexts (e.g., medical domains). We advocate for ``standardized customization'' that can meet users' needs and can be supported via tooling.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
Prompts for Public-Sector LLMs Should Be Governed as Commons
Prompts for public-sector LLMs encode value-laden decisions and should be governed through community-maintained Prompt Commons repositories with provenance, licensing, and moderation.
-
A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
-
Towards Imputation of Pre-Trained Language Model Metadata using Semantic Fingerprinting
SemFin combines model configuration files with repository tags to impute missing metadata across 317k PTLMs, outperforming propagation baselines by up to 31.4% and expanding reuse and license lineage chains on 167k models.
-
Generative AI Technologies, Techniques & Tensions: A Primer
Generative AI systems arise from statistical data processing that produces human-like outputs, creating a mismatch with traditional computer expectations and positioning educational researchers to lead in studying and...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.