AI Systems Engineering addresses the systematic development and operation of AI-based solutions as part of systems that perform complex tasks.
The design and engineering of complex systems that contain AI and ML components differ from classical engineering, which only uses clearly described components whose behavior can be predicted relatively accurately in advance. Systems with machine learning and decision-making capabilities, on the other hand, may only reveal their final behavior or functionality at runtime, depending on the data. Nevertheless, such systems with intelligent components must be designed in such a way that reliable predictions can be made about their behavior during runtime and guarantees can be given.
AI Systems Engineering, as a discipline complementing basic research into AI methods, makes the use of artificial intelligence systematically accessible and available to engineering. Particularly with regard to the possible certification of AI systems (especially with regard to functional safety, IT security, and privacy), a reliable description of AI and ML components and systems is necessary in order to be able to plan AI and ML components accurately during system design, i.e., at the design stage.
Through the AI Systems Engineering methodology (PAISE® - Process Model for AI Systems Engineering) developed under the leadership of our institute and the use of appropriate tools, we address the systematic development and operation of AI-based solutions as an integral part of complex systems for performing demanding tasks.