mlsanityData to 7 Oct 2026

On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI

arXiv 2402.05979 ↗First posted 7 Feb 2024cs.SE cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 7 Feb 2024.

Builds on

18 of 38 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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  11. 2022rPPG-Toolbox: Deep Remote PPG Toolboxcited by 63
  12. 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learningcited by 47
6 more
  1. 2020Behavioral Use Licensing for Responsible AIcited by 46
  2. 2023Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chaincited by 45
  3. 2022Edge Impulse: An MLOps Platform for Tiny Machine Learningcited by 24
  4. 2023CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Imagescited by 15
  5. 2023Report of the 1st Workshop on Generative AI and Lawcited by 12
  6. 2023GRID: A Platform for General Robot Intelligence Developmentcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 1; 2026: 1.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future
  2. 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing
  3. 2026Do User-Authored Permission Policies Improve Protection Against AI Agent Overreach?
  4. 2025From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law ↗
  5. 2025Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment
  6. 2026AI Agents Under EU Law
  7. 2025A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
  8. 2026Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward