The Gradient of Generative AI Release: Methods and Considerations
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As increasingly powerful generative AI systems are developed, the release method greatly varies. We propose a framework to assess six levels of access to generative AI systems: fully closed; gradual or staged access; hosted access; cloud-based or API access; downloadable access; and fully open. Each level, from fully closed to fully open, can be viewed as an option along a gradient. We outline key considerations across this gradient: release methods come with tradeoffs, especially around the tension between concentrating power and mitigating risks. Diverse and multidisciplinary perspectives are needed to examine and mitigate risk in generative AI systems from conception to deployment. We show trends in generative system release over time, noting closedness among large companies for powerful systems and openness among organizations founded on principles of openness. We also enumerate safety controls and guardrails for generative systems and necessary investments to improve future releases.
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The sheer amount of training data, the architectural complexity, and the many moving parts make full openness a tall order for generative AI.
“The sheer amount of training data, the architectural complexity, and the many moving parts make full openness a tall order for generative AI [53].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [53]
Section 5.4 (page unavailable) defines full openness as access to all system components, and §5.3.3 says model size can create an infrastructure barrier to access. Section 4.4 also says large training datasets are difficult to analyze at scale. The paper does not identify architectural complexity or the sheer amount of training data as reasons that fully opening a system is difficult. (LLM, conf=0.82)
Experimental — accuracy not yet measuredWe are already seeing a strong trend towards selective and self-serving forms of openness, both in release strategies and in empirical surveys of model openness.
“For one, we are already seeing a strong trend towards selective and self-serving forms of openness, both in release strategies and in empirical surveys of model openness ([32, 53, 60], and see below).”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [53]
Section 6.2 reports a trend toward closed or partly closed releases among large companies, and Section 5.1 notes that public-facing releases can be cherry-picked. These passages support selective release strategies, but they do not establish a trend toward self-serving forms of openness in empirical surveys of model openness. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe necessarily gradient nature of openness is seen in release methods, where prior work has pointed out tradeoffs between full openness and risk mitigation.
“The necessarily gradient nature of openness is seen in release methods, where prior work has pointed out tradeoffs between full openness and risk mitigation [16, 53].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [53]
For present-day generative AI systems, openness is not an all-or-nothing feature; rather, it is best conceived as a composite and gradient notion.
“For present-day generative AI systems, it is not an all-or-nothing feature; rather, it is best conceived as a composite and gradient notion [53].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [53]
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- Authors
- Solaiman, I.
- Affiliations
- Hugging Face (US)
- Corresponding author
- Solaiman, Irene
- ORCID
- Solaiman, Irene
- Venue
- 2023 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 111-122
- OpenAlex
- W4380360954
- Cited by
- 90
- References
- 47
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 35 · 2023: 19
- FWCI
- 22.53
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- access levels, open-source AI, API access, concentration of power, AI risk mitigation, safety control, guardrails, large corporations
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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