The Mirage of Artificial Intelligence Terms of Use Restrictions
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Abstract (arXiv)
Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforceable tools for preventing misuse, particularly in policy dialogs. But are these terms truly meaningful? There are myriad examples where these broad terms are regularly and repeatedly violated. Yet except for some account suspensions on platforms, no model creator has actually tried to enforce these terms with monetary penalties or injunctive relief. This is likely for good reason: we think that the legal enforceability of these licenses is questionable. This Article systematically assesses of the enforceability of AI model terms of use and offers three contributions. First, we pinpoint a key problem: the artifacts that they protect, namely model weights and model outputs, are largely not copyrightable, making it unclear whether there is even anything to be licensed. Second, we examine the problems this creates for other enforcement. Recent doctrinal trends in copyright preemption may further undermine state-law claims, while other legal frameworks like the DMCA and CFAA offer limited recourse. Anti-competitive provisions likely fare even worse than responsible use provisions. Third, we provide recommendations to policymakers. There are compelling reasons for many provisions to be unenforceable: they chill good faith research, constrain competition, and create quasi-copyright ownership where none should exist. There are, of course, downsides: model creators have fewer tools to prevent harmful misuse. But we think the better approach is for statutory provisions, not private fiat, to distinguish between good and bad uses of AI, restricting the latter.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Dec 2024.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AIcited by 7
- 2026Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integritycited by 3
- 2025New Tools are Needed for Tracking Adherence to AI Model Behavioral Use Clausescited by 0
- 2026The Limits of AI Data Transparency Policy: Three Disclosure Fallaciescited by 0
- 2026Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Modelscited by 0
- 2026Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models5 Jun 2026
- 2026Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity9 Feb 2026
- 2026The Limits of AI Data Transparency Policy: Three Disclosure Fallacies26 Jan 2026
- 2025New Tools are Needed for Tracking Adherence to AI Model Behavioral Use Clauses28 May 2025
- 2025In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI21 Mar 2025