Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI (2402.05979v1)

Published 7 Feb 2024 in cs.SE and cs.AI

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Allen Institute for AI. AI2 ImpACT Licenses, 2024. URL https://allenai.org/impact-license.
  2. Falcon-40B: an open large language model with state-of-the-art performance. Findings of the Association for Computational Linguistics: ACL, 2023:10755–10773, 2023.
  3. FactSheets: Increasing trust in AI services through supplier’s declarations of conformity. IBM Journal of Research and Development, 63(4/5):6:1–6:13, 2019.
  4. Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2):77–101, 2006. doi: 10.1191/1478088706qp063oa. URL https://www.tandfonline.com/doi/abs/10.1191/1478088706qp063oa.
  5. David Bretthauer. Open Source Software: A History. Published Works, 7, 2001.
  6. Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165, 2020.
  7. Lessons learned on language model safety and misuse, 2022. URL https://openai.com/research/language-model-safety-and-misuse.
  8. From RAIL to Open RAIL: Topologies of RAIL Licenses, 2022a. URL https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses.
  9. Behavioral use licensing for responsible ai. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 778–788, 2022b.
  10. Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. In 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, page 864–876, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450393522. doi: 10.1145/3531146.3533150.
  11. Report of the 1st Workshop on Generative AI and Law. arXiv preprint arXiv:2311.06477, 2023.
  12. Generating Harms: Generative AI’s Impact & Paths Forward. Technical report, Electronic Privacy Information Center, 2023.
  13. Openwebtext corpus, 2019.
  14. CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. arXiv preprint arXiv:2310.16825, 2023.
  15. Adding structure to ai harm: An introduction to cset’s ai harm framework, July 2023. URL https://cset.georgetown.edu/publication/adding-structure-to-ai-harm/.
  16. Edge impulse: An mlops platform for tiny machine learning. Proceedings of Machine Learning and Systems, 5, 2023.
  17. Growth of responsible AI licensing. Analysis of license use for ML models published on. Open Future, feb 7 2023. https://openfuture.pubpub.org/pub/growth-of-responsible-ai-licensing.
  18. Training Generative AI Models on Copyrighted Works Is Fair Use, 2024. URL https://www.arl.org/blog/training-generative-ai-models-on-copyrighted-works-is-fair-use/.
  19. Talkin’ ’Bout AI Generation: Copyright and the Generative-AI Supply Chain. arXiv preprint arXiv:2309.08133, 2023.
  20. Fair Learning. Texas Law Review, 99:743, 2021.
  21. Can licensing mitigate the negative implications of commercial web scraping? In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing, CSCW ’23 Companion, page 553–555, New York, NY, USA, 2023a. Association for Computing Machinery. ISBN 9798400701290. doi: 10.1145/3584931.3611276. URL https://doi.org/10.1145/3584931.3611276.
  22. Starcoder: may the source be with you! arXiv preprint arXiv:2305.06161, 2023b.
  23. rPPG-Toolbox: Deep Remote PPG Toolbox. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023.
  24. Microsoft. Responsible AI investments and safeguards for Facial Recognition, 2022. URL https://azure.microsoft.com/en-us/updates/facelimitedaccess/.
  25. Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* ’19, page 220–229, New York, NY, USA, 2019a. Association for Computing Machinery. ISBN 9781450361255. doi: 10.1145/3287560.3287596. URL https://doi.org/10.1145/3287560.3287596.
  26. Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency, pages 220–229, 2019b.
  27. Scalable Extraction of Training Data from (Production) Language Models. arXiv preprint arXiv:2311.17035, 2023.
  28. OpenAI. Usage policies, 2024. URL https://openai.com/policies/usage-policies.
  29. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
  30. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21:1, 2020.
  31. David B. Resnick. Openness versus Secrecy in Scientific Research Abstract. Episteme, pages 135 – 147, February 2006.
  32. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.
  33. Matthew Sag. Copyright Safety for Generative AI. Houston Law Review, 2023. Forthcoming.
  34. Pamela Samuelson. Generative AI meets copyright. Science, 381(6654):158–161, 2023. doi: 10.1126/science.adi0656. URL https://www.science.org/doi/abs/10.1126/science.adi0656.
  35. Dolma: An Open Corpus of 3 Trillion Tokens for Language Model Pretraining Research. Allen Institute for AI, Tech. Rep, 2023.
  36. Elham Tabassi. Artificial intelligence risk management framework (ai rmf 1.0), 2023-01-26 05:01:00 2023. URL https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936225.
  37. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.
  38. Attention is all you need. In Advances in neural information processing systems, pages 5998–6008, 2017.
  39. Grid: A platform for general robot intelligence development. arXiv preprint arXiv:2310.00887, 2023.
  40. Georg von Krogh and Sebastian Spaeth. The open source software phenomenon: Characteristics that promote research. The Journal of Strategic Information Systems, 16(3):236–253, 2007. ISSN 0963-8687. doi: https://doi.org/10.1016/j.jsis.2007.06.001. URL https://www.sciencedirect.com/science/article/pii/S096386870700025X.
  41. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100, 2022.
Citations (4)

Summary

We haven't generated a summary for this paper yet.

Youtube Logo Streamline Icon: https://streamlinehq.com