The seminar covers the AI Act and its legal and organizational application in AI projects. The European Commission pursues a risk-based approach with the AI Act to classify AI applications differently. This results in new requirements for companies when developing and introducing AI systems. You will take a practical look at exactly these requirements.
It starts with data protection. You clarify the difference between personal and anonymous data. You see the legal bases for data processing and the appropriate contract designs. Then come the pitfalls in AI projects. Topics include the black box versus data protection principles, access to data sources, and anonymized data use.
The second block focuses on the AI Act itself. You get an overview of the current status and the implementation measures. The clustering by risk categories is explained. In a group exercise, you assign concrete cases from practice to the four risk classes.
Then it's about implementation. You learn how high-risk AI projects are set up in compliance with the law. For general-purpose AI models (GPAI), you discuss documentation obligations, transparency obligations, and a strategy for complying with copyright law. Finally, the liability framework for AI products is on the agenda, including the directive on AI liability and the update of the product liability directive.
Addressed are software developers and data science experts involved in the development of AI systems. Also project managers and decision-makers who lead and oversee AI projects. Lawyers, data protection officers, data analysts, regulatory bodies, and people from AI research are also part of the target group.
The schedule is completed in one day. The first seminar part on the legal situation and classification runs from 9:00 AM to 12:30 PM. The second part on technical implementation in AI projects follows from 1:30 PM to 5:00 PM. In total, there are 8 teaching units, after which you receive a certificate of participation.
There are no prerequisites. However, knowledge of current case law or basic knowledge of AI and machine learning is advantageous according to the provider. You can choose between in-person and virtual classroom. At the time of research, there were 16 dates to choose from. For companies, an in-house implementation is possible, and the course is also available in English.
