Built for transparency workflows
The tool focuses on chatbot notices, AI-generated content labels, deepfake disclosures, and practical compliance-file notes.
Article 50 transparency
EU AI Act Disclosure Generator
Generate practical AI transparency notices for chatbots, AI-generated content, synthetic media, deepfake workflows, and public AI-assisted text. The output includes a short label, long notice, HTML banner, and compliance-file note that your team can review before launch.
Runs in your browser
The current version does not upload your inputs or generated notices. Review and adapt the output before production use.
Pair it with risk triage
If the AI system affects employment, education, healthcare, credit, housing, safety, or rights, run the risk classifier too.
When a disclosure may be useful
- A user is interacting with a chatbot, assistant, or automated advisor.
- Text, image, audio, or video was generated or materially modified by AI.
- Media could be mistaken for a real person, event, voice, or endorsement.
- AI-generated text is published for public-interest or decision-relevant contexts.
Source links
Frequently asked questions
Is the generated notice enough?
It is a first draft. You still need to check the exact product behavior, jurisdiction, user flow, and legal obligations before launch.
Should every AI output be labeled?
The answer depends on the output type, context, and risk of deception. Public and realistic media usually need stronger disclosure.
Can I use the HTML banner directly?
Yes as a starter implementation, but your team should adapt styling, placement, accessibility, and legal wording for the final product.
Disclaimer: AI Compliance Kit provides initial self-assessment tools and educational content.
It does not provide legal advice, certification, or a guarantee of compliance.
practical AI compliance self-assessment
Practical notes for EU AI Act Disclosure Generator
EU AI Act Article 50 Disclosure Generator | AI Compliance Kit is maintained for founders, product managers, compliance owners, agencies, and small teams building AI workflows who need AI governance workflow. The goal is to help visitors complete a real task and leave with an AI inventory, risk note, disclosure draft, vendor question set, policy outline, or review workflow report, not only read a generic summary.
Define the system
Start by naming the AI feature, users, decision impact, data categories, vendors, and the team member responsible for maintaining the review record.
Separate triage from advice
Use the generated output as first-pass operational triage. Legal, medical, hiring, credit, education, biometric, and public-sector uses still need specialist review.
Keep evidence
Save the output, assumptions, date, source links, and reviewer notes so the team can explain why a risk level, disclosure, or vendor question set was chosen.
Before relying on this page
- Review high-impact use cases manually.
- Keep policies and disclosures aligned with the real product behavior.
- Re-run the workflow when vendors, data, or user impact changes.
Review record
How to use EU AI Act Disclosure Generator in an AI compliance file
A compliance tool is useful when it leaves a traceable record. The output should name the AI system, explain the assumptions, and show what the team still needs to verify with product, legal, security, or vendor owners.
Describe the real system
Record the feature name, user group, decision impact, data sources, vendor dependencies, and the human owner. A generic "chatbot" label is rarely enough. A hiring assistant, support summarizer, medical triage bot, and product recommender can have very different risk profiles even if they all use language models.
Separate signal from conclusion
Treat this page as first-pass triage. It can flag high-risk areas, transparency duties, missing evidence, and questions to ask a vendor. It should not be treated as legal approval, clinical advice, hiring approval, credit approval, or permission to launch without human review.
Save evidence and changes
Save the generated result with the date, reviewer, source links, and unresolved questions. Update the record when the model, data, users, product flow, vendor, or region changes. This keeps the site useful for actual operators rather than only being a static explanation page.
Evidence checklist
- Document what the AI system does and what it does not do.
- Record whether the system influences employment, education, credit, public benefits, healthcare, biometric identification, safety, or other high-impact outcomes.
- Keep vendor documentation, model notes, data descriptions, user notices, human oversight notes, and monitoring plans together.
- Re-run the review when the product behavior changes, not only when the law changes.
- Use specialist review for high-impact or regulated workflows before relying on any generated text.