Research map / Large language models / AI and society

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

ICML 2024 · Large language models › AI and society · approach: Human-centered trust and agency

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Position paper arguing that responsible AI behavioral-use licenses need standardization with customization supported by tooling, based on interviews, license clause clustering, and Hugging Face license adoption analysis.

Problem

Responsible AI licenses with behavioral-use clauses (RAIL) are proliferating with inconsistent clauses, which may confuse users and dilute their impact, while openly released AI assets need controlled downstream use.

Approach

Argues for "standardized customization" of behavioral-use clauses, supported by a license generator and dependency-scanning tools, after mixed-methods evidence on license adoption and clause overlap.

Key results

• RAIL licensed repositories on Hugging Face grew from 1% (N=209) in September 2022 to 24.0% (N=41,700) of 174,163 licensed repositories in January 2024.
• Qualitative analysis of 77 clauses from 7 responsible AI licenses yielded 22 distinct behavioral-use clauses, with discrimination, disinformation, and legal-violation clauses appearing in nearly all licenses.
• Interviews with four license adopters revealed motivation to balance openness with misuse concerns and difficulty of ad hoc license modification.

Surprising findings

• Some of the most popular models do not use RAIL licenses despite RAIL's growing adoption.
• RAIL licenses measured by downloads rank fourth, behind Apache, MIT and CC, despite being second by repository count.

Limitations

• Interview sample is small (four participants).
• Qualitative clause analysis covers only 7 licenses.
• Quantitative analysis is limited to Hugging Face Model Hub.

Experimental setup

Datasets: Hugging Face Model Hub license metadata (174,163 model repositories) 77 legal clauses from 7 responsible AI licenses 4 semi-structured interviews with license adopters

Metrics: license adoption counts and share clause frequency across licenses

Builds on: OpenRAIL Creative Commons license chooser

Closest papers by meaning

• Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance (ICML 2025 · similarity 0.86)
• Position: A Safe Harbor for AI Evaluation and Red Teaming (ICML 2024 · similarity 0.82)
• Bridging the Knowledge Gap: Understanding User Expectations for Trustworthy LLM Standards (AAAI 2025 · similarity 0.82)
• Consent in Crisis: The Rapid Decline of the AI Data Commons (NeurIPS 2024 · similarity 0.82)
• Regulating AI: Applying Insights from Behavioural Economics and Psychology to the Application of Article 5 of the EU AI Act (AAAI 2024 · similarity 0.81)
• Position: Require Frontier AI Labs To Release Small "Analog" Models (NeurIPS 2025 · similarity 0.81)