AIG-016 AI Interaction and Output Disclosure
Description
Users interacting with an AI system are informed that they are doing so, unless it is unambiguous from context. For systems that generate synthetic audio, image, video, or text content, outputs are marked as AI-generated using a machine-readable mechanism (e.g. C2PA metadata, watermark). For deep fake or synthetic media outputs, deployers disclose AI generation to affected parties. Disclosure mechanisms are tested to ensure they are robust and not trivially removable.
Rationale
Undisclosed AI interaction is deceptive; disclosure is a base-level trust requirement for AI-mediated services and a regulatory obligation in most jurisdictions.
Framework Mappings (5)
| EU-AI-Art.50.1 | Transparency Obligations — AI Interaction Disclosure | full |
| EU-AI-Art.50.2 | Transparency Obligations — Synthetic Content Marking | full |
| EU-AI-Art.50.4 | Transparency Obligations — Deep Fake Disclosure | full |
| A.8.2 | System documentation and information for users | partial |
| MEASURE 2.8 | AI Transparency and Accountability Risks | informative |
Evidence (2)
UI or API configuration demonstrating that AI interaction disclosure is presented to users before or at the point of first interaction with an AI system.
Example: Chatbot system prompt configuration (exported from AWS Bedrock / Azure OpenAI deployment config) showing mandatory opening disclosure message 'This response is generated by an AI assistant'; UI component config showing AI badge rendered on all AI-generated responses
Test: Review the AI interaction disclosure implementation. Verify: (1) disclosure is presented before or at first AI interaction (inspect live system or configuration), (2) disclosure is not dismissible before being read, (3) for synthetic media outputs, a machine-readable marking mechanism (C2PA metadata, watermark) is configured and tested, (4) disclosure cannot be trivially removed by end-user action.
Disclosure mechanism test report confirming that AI interaction disclosure and synthetic content marking have been tested for robustness and correct rendering across supported interfaces.
Example: AI Disclosure QA Test Report v1 (TestRail, exported 2026-01-20), covering 6 user interface entry points, disclosure rendering on mobile and desktop, watermark persistence after image download, and synthetic media label display in API responses
Test: Request the disclosure mechanism test report. Verify: (1) test cases cover all customer-facing interfaces, (2) synthetic content marking is tested for persistence (e.g. watermark survives format conversion), (3) test results show pass against expected disclosure behaviour, (4) report is dated within the last 12 months or after last significant UI change.
Questions (2)
Are users of your AI systems informed that they are interacting with an AI before or at the point of first interaction?
Undisclosed AI interaction is deceptive and a regulatory obligation in most jurisdictions. Disclosure must be presented before interaction begins and should not be easily dismissed or hidden.
For AI systems that generate synthetic content (text, images, audio, video), which of the following disclosure mechanisms are in place?
All four mechanisms apply to generative AI systems producing synthetic media. Organisations using AI only for classification or decision-support (not synthetic media generation) should note which apply and which do not.