Topic: Transparency

How Microsoft and LinkedIn gave users detailed context about media on the professional networking platform

 

What details about media can help audiences understand its origin and history?

  • Microsoft and LinkedIn utilize C2PA metadata to disclose media characteristics to users.
  • Engineers had to consider what information from C2PA technical details was most helpful for audiences and how subtle language changes about the details can impact user interpretation.
  • Media literacy is an important component of Microsoft’s overall strategy to ensure societal resilience to AI harms.

This is Microsoft’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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How Truepic used disclosures to help authenticate cultural heritage imagery in conflict zones

 

How do indirect disclosures support user-facing direct disclosures for cultural heritage content?

  • Truepic utilizes its indirect disclosure tools to help platforms identify where content comes from and then provide direct disclosure to users.
  • Truepic highlights the importance of not only authenticating and disclosing synthetic content, but also non-synthetic content, in an effort to promote transparency across all digital media.
  • Truepic discusses Project Providence, a collaborative effort with Microsoft to leverage its authentication technology to document over 500 attacks in Ukraine and utilize direct and indirect disclosure outputs to support prosecutors in accountability cases.

This is Truepic’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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Policy Alignment on AI Transparency

Analyzing Interoperability of Documentation Requirements across Eight Frameworks

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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 • •
A: Contains high-level commitments to transparency
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.

Table 2A. Comparison of documentation requirements across in-scope frameworks

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 • • • •
• Documentation requirements for specific stage in the AI lifecycle
• Specific documentation artifacts
• General documentation requirements
Table 2B. 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
R&D Pre-system card: Planned testing, evaluation, and risk management procedures for foundation/ frontier models prior to development. Including:

  • Intended training data approach
  • Responsible AI practices
  • Development Team
  • Written “safety case”
 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:

  • Risks and potential impacts
  • Knowledge limits
  • TEVV considerations & tools
  • Measures of trustworthiness
  • Residual risks after mitigations
  • Model details
  • Data curation policies
  • Environmental impacts

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:

  • Evaluation reports
  • Safety & security risks
  • “ensuring appropriate and relevant documentation and transparency across the AI lifecycle”

Document datasets, processes and decisions during development

Regularly update Technical Documentation
Table 2C. Comparison of more general documentation and transparency requirements, at unspecified stages of the AI lifecycle

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:

  • Risk assessments, effectiveness of mitigations, evaluation results
  • Risk thresholds
  • Approach to mitigations
  • Processes to follow if risk thresholds are met/ exceeded
 Countries ratifying the convention must have frameworks (such as national laws) that:

  • Contain documentation requirements that will allow people to seek remedies for human rights violations
  • Require developers to adopt measures to identify, prevent, and mitigate risk. These measures are to include documentation of risks and mitigations
 Principles include:

Transparency and Explainability:

  • “Provide meaningful information” to “foster understanding of AI Systems”
  • “Provide plain and easy-to-understand information on the sources of data/input, factors, processes and/or logic”
  • “Provide information [to] enable those adversely affected by an AI system to challenge its output.”

Accountability:

  • “Ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle”
 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.

Table 3. In-scope frameworks
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.

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The development of standardized documentation artifacts for foundation models should be a priority in AI standardization efforts.

How Adobe designed its Firefly generative AI model with transparency and disclosure

 

Can companies include disclosure in the design of generative AI models?

  • In building Firefly, Adobe’s family of creative generative AI models, Adobe wanted to be sure the product would be commercially safe, provide transparency to consumers, and respect the rights of artists and creators.
  • Adobe had to consider technical, legal, policy, and ethical standards in building Firefly, including how to insulate creator content from model development, if requested, and attach disclosures to content.
  • The Framework provided Adobe with guidance on how to “take steps to provide disclosure mechanisms for those creating and distributing synthetic media.” They did this by developing Firefly with both direct and indirect disclosure built in.

This is Adobe’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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Even the best-intentioned uses of generative AI still need transparency

An analysis by human rights organization WITNESS

 

How much transparency does an artist need to provide when creating synthetic media?

  • To bring awareness to the disappearance of hundreds of children during the Argentine military junta of the late 1970s, a social media account used generative AI to create images of what the kidnapped children may look like today.
  • WITNESS identified this use case as one that had creative intentions, but required greater attention to responsible practices. For example, the synthetic images of the children were not clearly disclosed to users. The creator of the account also did not receive consent to use the photos (from the database/archive) or for the project (from the families of the subjects).
  • The Framework provided WITNESS with a lens for examining this use of synthetic media, as well as to hone best practices that should have been implemented for this content to be created responsibly.

This is WITNESS’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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How TikTok launched new AI labeling policies to prevent misleading content and empower responsible creation

 

How do you balance AI’s creative potential with its potential for harm?

  • TikTok rolled out synthetic media and manipulated content guidance in its Community Guidelines to allow for users’ creative expression with generative AI tools, and simultaneously prevent misuse. As part of this new policy, creators were asked to begin disclosing their own AI-generated content on the platform.
  • TikTok’s new policy included introducing a new toggle for creators to use whenever they posted content that was wholly generated or significantly edited using generative AI. One of the challenges TikTok faced was where to draw the line for requiring users to disclose synthetic content.
  • The Framework provided TikTok with a set of references for how synthetic media could be used harmfully. TikTok also responded to the Framework’s recommendations on how to disclose synthetic media to users responsibly.

This is TikTok’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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How Respeecher enables creative uses of its voice-cloning technology while preventing misuse

Can a voice-cloning startup successfully prevent its product from being misused?

  • Respeecher, in developing its voice cloning technology, sought to prevent misuse by obtaining consent and implementing content moderation.
  • Respeecher’s greatest obstacle was providing disclosure for synthetic voice in a creative context. How could the company provide direct disclosure to users without taking away from the immersive experience of the overall media?
  • While the Framework provides clear guidelines for how to responsibly provide disclosure, the current version does not contain guidance on how to do so while balancing user experience, thus raising the question of what the best practice in a creative context would be.

This is Respeecher’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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How OpenAI is building disclosure into every DALL-E image

 

What’s the best way to inform people that an image is AI-generated?

  • OpenAI explored the use of an image classifier (a synthetic media detector) to provide disclosure for the synthetic content created with their generative AI tools and prevent the potential misuse.
  • OpenAI considered the various tradeoffs in rolling out an image classifier, including accessibility (open vs. closed), accuracy, and public perception of OpenAI as a leader in the synthetic media space. By learning from their decision to take down a text classifier that was not meeting accuracy goals, OpenAI decided to slowly roll out a more accurate image classifier.
  • The Framework provided OpenAI with guidance for Builders on how to responsibly disclose the content created with DALL•E, including providing transparency to users about its limitations, addressed by a phased rollout of the classifier.

This is OpenAI’s case submission as a supporter of PAI’s Synthetic Media Framework. Explore the other case studies

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