Behavioral Use Licensing for Responsible AI
Open accessAbstract
With the growing reliance on artificial intelligence (AI) for many different applications, the sharing of code, data, and models is important to ensure the replicability and democratization of scientific knowledge. Many high-profile academic publishing venues expect code and models to be submitted and released with papers. Furthermore, developers often want to release these assets to encourage development of technology that leverages their frameworks and services. A number of organizations have expressed concerns about the inappropriate or irresponsible use of AI and have proposed ethical guidelines around the application of such systems. While such guidelines can help set norms and shape policy, they are not easily enforceable. In this paper, we advocate the use of licensing to enable legally enforceable behavioral use conditions on software and code and provide several case studies that demonstrate the feasibility of behavioral use licensing. We envision how licensing may be implemented in accordance with existing responsible AI guidelines.
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BloomZ's source code is Apache 2.0, an OSI-approved open source licence, while the model weights are released under the Responsible AI Licence (RAIL); Llama 2 is released under Meta's own Community Licence.
“BloomZ has two relevant licences. Its source code is Apache 2.0, an OSI-approved open source licence, while the model weights are released under the Responsible AI Licence (RAIL) [13]. Llama 2 is released under Meta's own Community Licence.”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [13]
The paper’s Limitations and Open Questions section describes Apache 2.0 as an open-source license, but does not identify BloomZ’s source-code or model-weight licenses. Searches for “BloomZ” and “Responsible AI License” found no passage establishing the BloomZ licensing claim attached to this citation. The separate Llama 2 sentence is not attributed to this source. (LLM, conf=0.76)
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ModelGo: A Practical Tool for Machine Learning License Analysis
Duan, M., Li, Q., & He, B. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645520 · Cited by 6 · see citing works
Rethinking use-restricted open-source licenses for regulating abuse of generative models
Cui, J., & Araujo, D. A. (2024) · Big Data & Society · doi:10.1177/20539517241229699 · Cited by 6 · see citing works
Ethics and Regulations in Generative AI
ZadZiabari, A., & Tabatabaei, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-82963-5_8 · Cited by 5 · see citing works
Generative artificial intelligence part 1: exploring ethical considerations
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Data Hazards: An open-source vocabulary of ethical hazards for data-intensive projects
Zelenka, N., Cara, N. H. D., Bennet, E. D., Clatworthy, P. L., Day, H., Garcia, I. K., García, S. R., Hanschke, V. A., Kuwertz, E. S., & Garcia, I. K. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100110 · Cited by 6 · see citing works
Can Licensing Mitigate the Negative Implications of Commercial Web Scraping?
Li, H., Vincent, N., Jernite, Y., Merrill, N., Benjamin, J. J., & Tarkowski, A. (2023) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3584931.3611276 · Cited by 4 · see citing works
Self-citationEquity, autonomy, and the ethical risks and opportunities of generalist medical AI
Sass, R. (2023) · AI and Ethics · doi:10.1007/s43681-023-00380-8 · Cited by 4 · see citing works
Piasecki, S., & Helberger, N. (2025) · The Information Society · doi:10.1080/01972243.2025.2473398 · Cited by 4 · see citing works
The Brief and Wondrous Life of Open Models
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ML-Asset Management: Curation, Discovery, and Utilization
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- Authors
- Contractor, D., McDuff, D., Haines, J. K., Lee, J., Hines, C., Hecht, B., Vincent, N., & Li, H.
- Affiliations
- IBM (United States) (US)
- Microsoft (United States) (US)
- RAIL, USA
- K&L Gates (United States) (US)
- Northwestern University (US)
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 778-788
- OpenAlex
- W3101447603
- Cited by
- 54
- References
- 59
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 15 · 2023: 11 · 2022: 3 · 2021: 1
- FWCI
- 11.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- responsible AI, AI ethics guidelines, software licensing, code sharing, AI governance, democratization of AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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