As governments and organizations worldwide race to develop policy frameworks for foundation models, we face a juncture that demands both swift action and careful coordination. However, without coordination, we risk creating an inconsistent patchwork of frameworks and divergent understandings of best practices.
Ensuring these frameworks work together is critical.
Partnership on AI’s Policy Alignment on AI Transparency conducts a comparative analysis of eight leading policy frameworks for foundation models, with a particular focus on documentation requirements, which are a critical lever for achieving transparency and safety.
In this report, we analyze current and potential near-term interoperability challenges between the documentation requirements in leading policy frameworks, and offer recommendations that aim to promote interoperability as well as establish a common baseline for best practices for accountability and transparency. However, we recognize that this is only the beginning of a much larger conversation. Achieving global interoperability will require ongoing efforts and substantial input, particularly from the Global Majority, to reflect diverse perspectives and priorities.
The full report provides a comprehensive exploration of interoperability challenges, including the nuances of our methodology, key findings, and detailed recommendations. This summary serves to highlight our most salient conclusions, aiming to inform and guide ongoing policy discussions and decision-making in this rapidly evolving field.
As we navigate the complex landscape of AI governance, the need for coordinated, interoperable policy frameworks becomes increasingly clear. By working together across borders and sectors, we can create a more coherent, effective approach to managing the risks and harnessing the potential of foundation models, ensuring accountability, transparency and fostering innovation in the global AI ecosystem.
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Without coordination, we risk creating an inconsistent patchwork of frameworks and divergent understandings of best practices.
Frameworks Reviewed
Documentation and transparency play a key role in managing risk for foundation models, and are a common feature of policy frameworks.
To explore how documentation guidance is being incorporated in current policy frameworks, we compared key frameworks from the US, EU, UK, and select multilateral initiatives (see Table 1). The table outlines each framework’s provisions for foundation model documentation, ranging from high-level transparency guidelines to specific artifact requirements. It also indicates ongoing development processes for each framework.
Table 1. Frameworks reviewed in this report
| A | B | C | D | E | |
|---|---|---|---|---|---|
| Multilateral | |||||
| OECD AI Principles | • | ||||
| Seoul Frontier AI Safety Commitments | • | • | • | ||
| Hiroshima AI Process Code of Conduct | • | • | • | • | |
| Council of Europe AI Convention | • | ||||
| Regional | |||||
| EU AI Act | • | • | • | ||
| National | |||||
| US AI Executive Order 14110 | • | • | • | ||
| NIST AI RMF (with Gen-AI Profile) | • | • | • | ||
| UK AI White Paper and followup | • | • | |||
B: Requires/recommends documentation practices
C: Requires documentation artifacts
D: Further/more detailed provisions proposed or in development
E: Specifically addresses foundation models
Mapping Documentation Requirements Across the Frameworks
Documentation guidance and requirements under the reviewed frameworks are summarized in Table 2A below. Key findings include:
- Documentation is a common feature of the frameworks, though this is couched in various terms. Several of the high-level frameworks recommend providing certain kinds of information to various actors; some require recording and/or reporting of information, and some require the preparation of specific documentation artifacts.
- The most commonly referenced artifacts are technical documentation, instructions for use, information about datasets, and incident reports. However, there is little detail in most of the frameworks about what should be included in each of these documents and the form each document should take.
- This analysis suggests that there is an opportunity to develop standardized requirements for some of the key documentation artifacts required across frameworks – provided that agreement can be reached about what the content of these artifacts should be.
Tables 2A, 2B and 2C contain a comparison of documentation requirements across in-scope frameworks. Specific documentation artifacts are shown in red. The principal documentation guidelines from PAI’s Model Deployment Guidance are included as a comparator.
| Stage in AI Lifecycle | Framework | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| PAI Model Deployment Guidance | EU AI Act | AI Executive Order | NIST RMF and Generative AI Companion | Hiroshima Code of Conduct | Seoul Frontier AI Commitments | COE Convention | OECD AI Principles | UK AI White Paper, AI Principles, Response | |
| R&D | • | • | • | ||||||
| Pre-deployment/ on deployment | • • | • • | • | • | • • | ||||
| Post-deployment | • | • • | • | • • | |||||
| Across lifecycle | • | • | • • | ||||||
| Unspecified | • | • | • | • | |||||
• Specific documentation artifacts
• General documentation requirements
Specific documentation artifacts are shown in red. The principal documentation guidelines from PAI’s Model Deployment Guidance are included as a comparator.
| Stage in AI Lifecycle | Framework | ||||
|---|---|---|---|---|---|
| PAI Model Deployment Guidance | EU AI Act | AI Executive Order | NIST RMF and Generative AI Companion | Hiroshima Code of Conduct | |
| R&D | Pre-system card: Planned testing, evaluation, and risk management procedures for foundation/ frontier models prior to development. Including:
|
Notify EU Commission of models with systemic risk | Report dual-use models to Department of Commerce; report cybersecurity protections | N/A | N/A |
| Pre-deployment/ on deployment |
Publicly report model impacts “Key ingredient list”: including details of evaluations, limitations, risks, compute, parameters, architecture, training data approach, model documentation Disclose performance benchmarks, intended use, risks and mitigations, testing and evaluation methodologies, environmental and labor impacts Downstream use documentation: including appropriate uses, limitations, mitigations, safe development practices Share red-teaming findings |
Technical documentation: including information about training, testing, and evaluations Documentation for downstream developers: including information about capabilities, limitations, and to aid downstream compliance Public summary of training data |
Report red-teaming results to Department of Commerce | Multiple guidelines for documentation, including of:
|
Technical documentation Transparency reports: with “meaningful information” Instructions for Use Technical Documentation Documentation to include details of evaluations, capabilities/ limitations re: domains of use; risks to safety and society; red-teaming results |
| Post-deployment |
Incident reporting Transparency reporting (frontier model usage and policy violations) |
Serious incident reports | N/A |
Incident and performance reporting Transparency reports with steps taken to update generative AI systems |
Maintain “appropriate documentation” of reported incidents |
| Across lifecycle |
Iteration of model development Provide documentation to government as required Environmental Impacts Severe labor market risks Human rights impact assessments |
N/A | N/A | Multiple guidelines to document processes and management systems | “Work towards” information sharing and incident reporting, including on:
Document datasets, processes and decisions during development Regularly update Technical Documentation |
| Framework | |||
|---|---|---|---|
| Seoul Frontier AI Commitments | COE Convention | OECD AI Principles | UK AI White Paper, AI Principles, Response |
|
Publicly report model or system capabilities, limitations, and domains of appropriate and inappropriate use Provide public transparency on implementation of commitments, including on:
|
Countries ratifying the convention must have frameworks (such as national laws) that:
|
Principles include:
Transparency and Explainability:
Accountability:
|
Provide transparency and accountability, including “documentation on key decisions throughout the AI system life cycle” |
Other Features of the Frameworks Relevant to Interoperability
In reviewing the in-scope frameworks, a number of additional factors emerged including their binding nature, enforcement mechanisms, scope of applicable models, overseeing institutions, development processes, and emphasis on collaboration, as detailed in Table 3.
Normative status, coverage/thresholds, reference to international standardization processes and collaboration/interoperability
| Framework | PAI Model Deployment Guidance | EU AI Act | AI Executive Order | NIST RMF and Generative AI Companion | Hiroshima Code of Conduct | UK AI White Paper, Consultation Response | OECD | Seoul frontier AI Commitments |
|---|---|---|---|---|---|---|---|---|
| Binding or Voluntary? | Voluntary | Binding | Partly binding | Voluntary | Voluntary | Voluntary (guidance for sectoral regulators) | Voluntary | Voluntary |
| Coverage | Foundation models (with guidance tailored according to three capability bands and four release strategies). The most stringent guidance applies to “paradigm-shifting or frontier” models |
General- purpose AI models (baseline requirements) “General- purpose AI models with systemic risk” |
“Dual-use foundation models” | AI systems (NIST AI RMF)
Generative foundation models (Gen-AI Profile) |
“The most advanced AI systems, including the most advanced foundation models and generative AI systems” |
AI systems; generally a sectoral approach Initial focus of UK AISI on advanced systems Planned laws for “the most powerful AI systems” |
AI Systems | “Frontier AI” – “highly capable general- purpose AI models or systems that can perform a wide variety of tasks and match or exceed the capabilities present in the most advanced models” |
| Initial Threshold | N/A |
None (baseline requirements) 10^25 FLOPs (models “with systemic risk”) |
10^26 FLOPs (10^23 FLOPs for models trained on biological sequence data) | N/A | N/A | N/A | N/A | N/A |
| Institutions/ Oversight | N/A | AI Office | Dept of Commerce (for reporting requirements) | N/A | OECD (monitoring mechanism under development) | AISI | N/A | N/A |
| Next Steps | N/A |
Codes of Practice for GPAI due August 2025 Templates for training data (AI Office) Harmonized standards Delegated acts – thresholds for GPAI with systemic risk; documentation requirements |
Various, including: OMB materials for federal procurement Copyright guidance Dept of Commerce can change threshold for dual-use model reporting |
NIST/the NIST AISI have a broad work plan including developing tools, evaluations, metrics |
COC to be iterated by G7 HAIP OECD developing monitoring mechanism |
Intention to legislate announced re advanced models, and to place AISI on statutory footing | OECD developing Due Diligence Guidance (DDG) for AI under OECD Responsible Business Conduct (RBC) guidelines |
AI Action Summit February 2025 (France) AI Safety Science Report to be published at AI Action Summit |
| Commitment to cooperation/ collaboration? | Collaborate with cross-sector Al stakeholders re risk identification, methodologies, best practices, standardization | Mandates creation of AI Board, Advisory Forum; multistakeholder participation in development of Codes of Practice and harmonized standards | Under EO, NIST released plan for global engagement on AI standards; Secretary of State is developing Global Development Playbook; EO contained several consultation requirements |
Several references to collaboration e.g. with external researchers, industry experts, and community representatives about best risk measurement and management practices NIST is committed to collaboration/ cooperation, e.g. through AISI Consortium and pending Network of AISIs |
Across sectors, including on research to assess/ adopt risk mitigations, document incidents, and share information with the public to promote safety | Focus on collaboration across government, stakeholder groups, and internationally | OECD convenes the Network of Experts | Information sharing, collaboration on safety research (Seoul AI Principles) |
| Commitment to standards | Development and adoption of standards | EU harmonized Standards—though EU committed to adopting international standards where possible |
NIST is required to develop standards Under EO, NIST has released plan for global engagement on promoting and developing AI standards |
Contains references to considering relevance of standards (including NIST frameworks) NIST will continue to align AI RMF with international standards |
Advance development and adoption of standards | Support for work on assurance techniques and technical standards | Governments should promote standards development | Contribute to/ take account of international standards |
Summary of Findings
- Interoperability and collaboration are explicitly included as policy goals in a number of the international frameworks, though there is no current agreement on how those goals will be achieved.
- The frameworks emphasize the importance of documentation for foundation models, but remain light on detail about the form and content it should take.
- There are a number of steps that could be taken to advance interoperability now and in the future, leveraging existing and proposed forums, mechanisms and processes.
- An early focus should be on agreeing upon thresholds for regulation, to provide international consistency about which foundation models are captured by regulatory and policy frameworks.
- While there are some challenges to relying on international standardization processes to align AI policy frameworks, standards remain an important plank in that effort.
- Harmonizing key documentation requirements across national, regional, and international foundation model policy frameworks – and in particular, harmonizing the form and content of documentation artifacts – should be made a priority.
- The lack of consensus on the best approaches to manage AI risks is a significant challenge to developing interoperable frameworks, including for documentation.
- The existing and newly announced AI Safety Institutes can establish a foundation for AI safety consensus through research, evaluations, scientific advancement, and collaborative development of safety standards and documentation practices.
- Participation by civil society and the global community is needed in all major foundation model policy initiatives if we are to ensure that they lead to alignment around best practices, and that the agenda for global interoperability is not set by a comparatively small group of nations from the Global North.
Participation by civil society and the global community is needed in all major foundation model policy initiatives.
Summary of Recommendations
- National governments and the EU should prioritize cooperation in setting thresholds for identifying which foundation models require additional governance measures, including through supporting the OECD’s work on this issue. The AI Summit Series could also be used to take this forward. Agreeing on a common definition, and thresholds, for the models covered by policy frameworks, should flow through to greater alignment between the frameworks, including in relation to documentation requirements.
- The G7 Presidency should continue developing the Hiroshima Code of Conduct into a more detailed framework to provide more detail about thresholds, relevant risks, and the form and content of documentation artifacts. This work should be a focus of Canada’s G7 Presidency in 2025, including aligning closely with the EU Code of Practice development timeline. In doing this, it should seek input from foundation model providers, civil society, academia and other stakeholder groups equally.
- When creating and approving initial Codes of Practice for the EU AI Act, all involved parties should prioritize compatibility with other major AI governance frameworks where possible. The involvement of non-EU model providers, experts and civil society organizations will help advance this objective.
- To support the development of standardized documentation artifacts, Standards Development Organizations should ensure that their processes are informed by socio-technical expertise and diverse perspectives as well as required resources. To that end, SDOs, industry, governments, and other bodies should invest in capacity building for civil society and academic stakeholders to engage in standards-making processes, including to ensure participation from the Global South.
- The development of standardized documentation artifacts for foundation models should be a priority in AI standardization efforts. This would promote internationally comparable documentation requirements for foundation models – promoting interoperability and establishing a baseline for best practice internationally.
- International collaboration and research initiatives should prioritize research that will support the development of standards for foundation model documentation artifacts. Documentation is a key feature of foundation model policy requirements, and common requirements for artifacts will directly improve interoperability. It will also make comparisons between models from different countries easier, promoting accountability and innovation.
- National governments should continue to prioritize both international dialogue and collaboration on the science of AI Safety, however with more specificity and tracking of progress on commitments that will foster good practice. This work will inform a common understanding of what should be included in documentation artifacts to promote accountability and address foundation model risks.
- National governments should support the creation/development of AI Safety Institutes (or equivalent bodies), and ensure they have the resources, functions, and powers necessary to fulfill their core tasks. Efforts should be made to align the functions of these bodies with those common among existing AISIs. This will promote efforts to develop trusted mechanisms to evaluate advanced foundation models, and may, at a later stage, lead to the potential to work towards “institutional interoperability.”
- The Network of AISIs (and bodies with equivalent or overlapping functions such as the EU AI Office) should be supported and efforts should be made to expand its membership. Consideration should be given to how the Network could support broader AI Safety research initiatives.
Background and Methodology
The work plan leading to this report was developed with guidance from PAI’s Policy Steering Committee. This report has been informed through desk research and consultations with experts from industry, civil society, academia, and non-profit organizations, drawn from PAI’s partner and wider stakeholder networks. We tested our initial thinking in a virtual multistakeholder workshop in August 2024. The views and recommendations in this report remain those of PAI.
For a comprehensive exploration of interoperability challenges, including our methodology, key findings, detailed recommendations, and more, please download the full report. To stay in touch with our latest policy work, sign up here.
The development of standardized documentation artifacts for foundation models should be a priority in AI standardization efforts.