Junior Professorship for AI-Based Decision Support

Welcome to the website of the Junior Professorship for Data-Driven Decision Support

The goal of this junior professorship is to research the design, challenges, and potentials of AI-based decision support systems (AI-DSS) and to teach them. There are two fundamental questions: (i) How are such systems designed (Which algorithms are used?)? On the basis of which data does the intelligent system learn? etc.) and (ii) how do people interact with these systems (Why is there aversion to the use of such systems? How can acceptance be increased?). These two questions complement each other, since design features of such systems have an impact on the attitude of the user. For example, the junior professorship investigates effects of transparency by implementing XAI methods that explain the decision of an AI-DSS and then measuring trust and other characteristics of the user with respect to the system.

In teaching, we first offer the lecture block AI-based Decision Support I and II, which lays the foundations for understanding Machine Learning (ML) and later Deep Learning (DL) and Explainable Artificial Intelligence (XAI), so that students are able to solve complex analytical problems using ML methods and DL methods. The focus is always on the entire analysis process and not only on the actual algorithms: data exploration, preprocessing, evaluation and deployment are central aspects of the lecture series.

In extended practical seminars, students are confronted with data relevant to practice and have to design solutions. Theory seminars and advanced lectures will examine, among other things, the ethical and legal aspects of AI-DSS. Thus, the teaching addresses both the more design-oriented ORBA and finance courses as well as the more behaviorist-oriented business administration courses.