Search

The explainability of artificial intelligence (AI) is one of the central challenges for the wider use of the new technology in many industries and applications. The more powerful and efficient the algorithms of AI work, the less it is usually comprehensible to users. While there is widespread agreement on the basic requirement of explainability for AI applications, the design of an adequate AI explanation is rarely defined. This contribution presents basic concepts of explainability as well as current approaches to explanations for AI. It describes which methods are fundamentally suitable for considering an explanation to be complete and how it must be designed in order to be assessed as interpretable for AI.

What is special about the German-speaking education system is that there are two types of higher education institutions. While classical universities do academic teaching, so-called universities of applied sciences (UAS) focus more on practical training for the labour market. This chapter presents the experiences of two professors with practice-integrated teaching at two UAS that ensure employability to a large extent. The common setting of the four courses presented is that students work on real business cases, practical challenges, and real fields of innovation. Methodologically, design think and SCRUM are used. It is described for these cases how the design of the course contributes to students developing innovative and digital approaches for real companies. The chapter concludes with a compilation of good practices that emerged from experiences. These can serve as input for other teachers to design their own practice-integrated courses.