AI & transparency [click explore]
Internal online training in just a few steps
Get immediate operational readiness - compatible with any learning management system (LMS) or as a stand-alone solution. Interactive, with certificate, and customizable for your industry on request.
1 Select a course (and customize it)
Choose a ready-made course from mybreev and tailor the content to your industry whenever needed.
2 Integrate technically
Seamless integration into your LMS or immediate use as a stand-alone solution. Format: SCORM 1.2, xAPI, HTML5 and LTI.
3 Train interactively
Active learning through integrated quiz questions and practical exercises. Measurable learning progress in real time.
4 Issue certificates
Automatic certificate issuance after successful completion. Documentable for professional development and compliance.
Used across industries
Course content & learning objectives
Chapters
Introduction - AI, transparency and trust
Legal basis for transparency obligations (EU AI Act)
- Which content must be labeled
- What labeling looks like in practice
- What penalties there are for violations
- Why "well-intentioned" is not enough - and how to ensure compliance
The labeling obligations - implementation in practice
- Texts: Where and how to place notices
- Images/videos: Watermarks, "content credentials" symbol and captions
- Chatbots: Clear labeling of AI interactions
- High-risk AI: Documentation requirements and human supervision
Legal exceptions - when labeling is not required
- Standard edits
- Editorial control
- Obviousness
- Art and satire
Find out when you can save time - and when you should still focus on transparency.
Specifications for high-risk AI
- What high-risk AI is and which systems are affected
- The three golden rules for use: operating instructions, human oversight, information quality
- Responsibility of managers (e.g. fundamental rights impact assessment)
Data protection and other reporting obligations
- Information obligations under Article 13 GDPR (e.g. rights of data subjects)
- Explainability of AI decisions (e.g. in the case of automated refusals)
- Documentation obligations (e.g. data protection impact assessment)
- How to protect personal data when using AI and build trust with stakeholders
Liability, practice and recommendations for action
- Technical measures (e.g. logging)
- Organizational measures (e.g. training, internal guidelines)
- Consequences of violations
- How to use transparency as a competitive advantage - for more trust and compliance
You will also go through a checklist of best practices for your day-to-day work.
Final quiz & summary
- Transparency obligations under the EU AI Act
- Labeling of AI content
- Data protection requirements (GDPR)
- Dealing with high-risk AI
At the end you will receive a summary of the most important points.
Learning objectives
- They know the transparency obligations under the EU AI Act and avoid fines of up to 15 million euros by correctly labeling AI content.
- They know how and where AI content needs to be labeled - from watermarks for images to information texts for chatbots.
- You understand when labeling is not necessary and save time in your day-to-day work.
- You will learn about the strict requirements for high-risk AI systems and implement them in a documented and comprehensible manner.
- They know the GDPR requirements for AI use and protect personal data.