We are happy to announce our second keynote speaker – Ute Schmid!

Ute Schmid is head of the chair for cognitive systems at University of Bamberg. She holds a diploma in psychology and a diploma in computer science, both from Technical University Berlin (TUB), Germany. She received her doctoral degree in computer science from TUB in 1994 and her habilitation in computer science in 2002. From 1994 to 2001 she was assistant professor at the Methods of AI/Machine Learning group, Department of Computer Science, TUB. Afterwards she worked as lecturer for Intelligent Systems at the Department of Mathematics and Computer Science at University Osnabrück and was member of the Cognitive Science Institute. In 2004 she became professor of Applied Computer Science/Cognitive Systems at the University of Bamberg. In 2022 Ute Schmid was elected as EurAI fellow and in 2023 as GI fellow. She is a fortiss research fellow. Since 2020 she is member of the board of directors of the Bavarian Research Institute of Digital Transformation (bidt). Ute Schmid is also a member of the Bavarian AI Council (Bayerischer KI-Rat). Furthermore, since 2020 Ute Schmid is head of the Fraunhofer IIS project group Comprehensible AI (CAI). Ute Schmid dedicates a significant amount of her time to measures supporting women in computer science and to promote computer science as a topic in elementary, primary, and secondary education. She won the Minerva Gender Equality Award of Informatics Europe 2018 for her university. Since many years, Ute Schmid is engaged in educating the public about artificial intelligence in general and machine learning and she gives workshops for teachers as well as high-school students about AI and machine learning (see Talks). For her outreach activities she has been awarded with the Rainer-Markgraf-Preis 2020.

Research interests of Ute Schmid are mainly in the domain of comprehensible machine learning, explainable AI, and high-level learning on relational data, especially inductive programming. Research topics are generation of visual, verbal and example-based explanations, intelligent tutor systems, interactive (human-in-the-loop) learning, combining deep learning and symbolic learning (neuro-symbolic AI), knowledge level learning from planning, learning structural prototypes, analogical problem solving and learning. Main application domains are image based diagnostics in medicine and industrial quality control as well as education. A further area of research is cognitive science with a focus on empirical and experimental work on high-level cognitive processes. Ute Schmid is a pioneer of Computer Science for Primary School (FELI) and is engaged in the domain of AI education.

Address title: AI Literacy – Why Basic Understanding of AI Methods is Relevant for Save, Efficient, and Reflected Use of AI-Tools

With the growing number of AI-Tools in many domains, AI literacy is essential for their save, efficient and reflected use. Often, AI literacy has a focus on the usage of tools. In my talk, I will argue why a theoretical understanding of basic principles of AI methods is an important prerequisite for tool use as well as for critical reflections of effects of AI technologies on society and environment. The major challenge in communicating AI concepts to laypeople is to convey technically correct information that is at the same time didactically tailored to the target group. In the talk, I will introduce basic AI concepts together with illustrations how these concepts can be taught. Furthermore, I will point out typical pitfalls for misjudgments that are based on an inadmissible anthropomorphization of AI.