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Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI

Research output: Contribution to Journal/MagazineConference articlepeer-review

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  • Daniel McDuff
  • Tim Korjakow
  • Scott Cambo
  • Jesse Josua Benjamin
  • Jenny Lee
  • Yacine Jernite
  • Carlos Muñoz Ferrandis
  • Aaron Gokaslan
  • Alek Tarkowski
  • Joseph Lindley
  • A. Feder Cooper
  • Danish Contractor
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<mark>Journal publication date</mark>21/07/2024
<mark>Journal</mark>Proceedings of Machine Learning Research
Volume235
Number of pages12
Pages (from-to)35255-35266
Publication StatusPublished
<mark>Original language</mark>English
Event41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria
Duration: 21/07/202427/07/2024

Conference

Conference41st International Conference on Machine Learning, ICML 2024
Country/TerritoryAustria
CityVienna
Period21/07/2427/07/24

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.