Published by the Digital Tourism Think Tank
AI Transparency Framework
A suite of open frameworks for tourism organisations, public agencies, enterprises and industry bodies to record, measure and communicate their use of artificial intelligence, across all work types and outputs.
Published versions
Models
Transparencyv1.1
Productivityv1.2
Environmentalv0.6
Integrityv0.2
Instruments
Maturityv0.1
Capabilityv0.4
Wellbeingv0.2
March 2026.
AI Transparency Model · v2.0 · the seven bands
Five graded · two declared
Five bands are graded and reached by calculation. Two sit outside the scale, are declared by a person, and no calculation can reach them.
Artificial intelligence is now embedded in how organisations across tourism and destination management write, research, design, communicate and deliver. In most cases, that involvement is invisible. Audiences have no way to assess how content was produced. Procurement and commissioning relationships rest on assumptions about human effort that AI has materially changed. The environmental costs of AI use go unreported. The ethical dimensions of AI-generated content go unassessed.
The DTTT AI Transparency Framework provides practical, self-service tools for addressing that absence. It comprises four independently versioned models, each addressing a different dimension of AI use. Together they cover what AI contributed, how much time it saved, what it cost the environment, and whether the content produced was ethically sound. Each model can be adopted and applied independently or used as part of a complete disclosure approach.
All models are published under a Creative Commons Attribution 4.0 licence. Any organisation can adopt and apply them. DTTT uses the framework across its own work and invites the wider industry to do the same.
Tier 1 · disclosure models
Model 1AI Transparency Model v1.1Records the balance of human and AI contribution to any piece of work using a five-point A to E scale. Applicable to any organisation and any output type, from a social media post to a major strategy document.View model →
Model 2Productivity and Delivery Extension Model v1.2Measures time savings and delivery capability extension. Supports internal performance reporting, supplier disclosure and demonstrating the tangible value of AI adoption to clients, boards and funders.View model →
Model 3AI Environmental Impact Model v0.6An indicative A to E scale for communicating the relative energy intensity of AI use. A disclosure tool, not a sustainability certification, for organisations reporting honestly on the environmental dimension of their AI activity.View model →
Model 4AI Content Integrity Model v0.2A risk classifier for the ethical dimensions of AI-generated and AI-manipulated visual content. Covers consent, authenticity and audience disclosure, addressing questions that standard image licensing frameworks were not designed to answer.View model →AI Transparency Toolkit
Score your work and generate a disclosure card
The free self-grading toolkit lets you assess any piece of work against any combination of the four models and generate an embeddable disclosure card for reports, articles, websites and publications.
Before you begin: AI Ethics Consideration Tool
Assess the ethical dimensions of any AI task before starting
Five questions that surface the ethical, legal and data considerations relevant to your specific AI task, producing a routing recommendation and a tailored checklist before work begins.
Tier 2 · organisational intelligence
Organisational intelligence instruments
Alongside the four Tier 1 disclosure models, DTTT publishes a suite of Tier 2 instruments that operate at team and organisation level. These are not disclosure tools. They examine the capability, maturity and wellbeing of the people doing the work, and provide the organisational intelligence needed to manage AI adoption effectively over time.
AI Maturity ModelBeta
Where are we on our AI journey?
A five-stage, five-dimension baseline assessment. Suitable for all staff including those not yet using AI. Produces an overall maturity stage and dimensional profile. Use to set organisational targets and track progress quarterly.
AI Capability ModelBeta
What kind of AI user am I?
A scenario-based individual assessment across 16 capabilities and 4 domains. For people who already use AI regularly. Produces an archetype profile and development path. Includes a team benchmark tool for mapping collective capability.
AI Wellbeing InstrumentBeta
What is AI doing to our people?
A periodic reflection across five pillars grounded in the Job Demands-Resources model. Two modes: staff (private, anonymous) and manager (team perspective). Tracks the human impact of AI adoption over time.
Not sure which instrument to start with? The instruments hub explains who each is designed for and includes a short signposting quiz to find the right starting point.
Who the framework is for
The framework is designed for any organisation that uses AI in producing work for or about the tourism sector. This includes destination management and marketing organisations, national and regional tourism bodies, enterprise and investment agencies, local authorities with tourism responsibilities, travel and hospitality businesses, event and conference organisers, tourism-focused research institutions, and agencies and consultancies working in the sector.
It applies equally to public-sector bodies and commercial operators, to large organisations and independent practitioners, and to those at the early stages of AI adoption and those with mature AI workflows.
01
Consumer-facing and public communications
Content published to general audiences, including editorial articles, marketing campaigns, social media posts, imagery, video and any other AI-generated or AI-assisted material where transparency builds public trust. The framework gives audiences the information they need to assess how content was produced, and gives publishers a consistent standard for making that disclosure.
02
Organisational and strategic work
Research reports, destination strategies, funding applications, policy papers, presentations, briefings and internal documents. The Transparency, Productivity and Environmental models all apply to knowledge-based and analytical work, enabling clear disclosure to clients, commissioners and boards while demonstrating the real efficiency gains AI delivers.
03
Procurement and supply chain relationships
Requiring agencies, vendors and suppliers to apply the framework as a condition of engagement establishes a new standard of disclosure across the supply chain. The Content Integrity Model includes a procurement clause for AI-generated visual content. Organisations that make adoption a requirement set a precedent that raises standards across the sector as a whole.
JSON schemas
All four model definitions are published as JSON schema files under CC BY 4.0, including full changelogs, grade definitions and boundary rationale. The versions page holds the complete public release log and schema downloads for all versions.
Governance and community
All models are developed openly, versioned publicly and published under a Creative Commons Attribution 4.0 licence, and DTTT decides as product owner. There is no standing committee: external input is sought and recorded, and the version log is the accountability. Working groups, adopter registration, case study submission and the academic research partnership programme are all described on Research and community.
Disclosure record
This site carries its own declaration
The framework asks every organisation using it to disclose how its work was made. This is that disclosure, for this site, produced with the same model and the same medium definition anyone else would use.
Considerable
31 of 38 points
- Code
- TM-D@2.0
- Medium
- Website, microsite or digital product
- Object
- Output disclosure
| Activity | Answer | Weight | Points |
|---|---|---|---|
| Concept and information architectureThe published declaration: "All strategic decisions, scoring definitions, model architecture and editorial positions are human-authored", with Structure listed among the AI-supported tasks. Human led, AI helped. | AI helped | 3 | 3 |
| Interface designThe published declaration: AI "implemented the full design system". The visual direction was set by the team and the drawing was not. | AI led | 4 | 8 |
| Code implementationThe published declaration, in as many words: "AI produced all HTML, CSS and JavaScript for this page, built every interactive grader". | AI led | 6 | 12 |
| Content and copyThe published declaration: AI "generated all structured content from the framework source materials", and the source materials, the methodology and every editorial position are the team's. Most of what this site says is published words it already published. | AI helped | 4 | 4 |
| Testing and refinementThe gates, the browser measurements and the parity harness were written and run by AI under direction. The team reviewed and decided; the work of checking was not theirs. | AI led | 2 | 4 |
| 31 of 38 points | 31 |
What AI did
AI produced the HTML, CSS and TypeScript for every page, the serverless function layer, the two design system packages and the methodology package, and wrote the build gates that hold them to the frozen contract. It generated the structured content from the framework's own published source materials.
What the team did
The framework methodology, every scoring definition, the model architecture, the medium definitions and all editorial positions were developed by the DTTT team, who directed the build throughout and decided what shipped.
- Productivity
- 5 of 5Transformative
- Delivery Extension
- 5 of 5New capability
- Environmental
- Grade CModerate intensity
This declaration replaces the one this page carried under Transparency Model v1.1, which graded a single-file build that no longer exists. The band letter has not moved. What it is called has: at v1.1 band D was labelled AI-Generated, and at v2.0 the same letter is Considerable, while AI Generated is a declaration that sits above the top graded band and is never reached by calculation. That is the collision the version pin on every code exists for.
Self-reported by the Digital Tourism Think Tank, against its own framework, under the same rules it asks of everyone else.