Colloquium Cognitive Systems

Explicit and implicit knowledge injection for human-in-the-loop machine learning

Prof. Dr. Ute Schmid (University of Bamberg)

Abstract:  For many practical applications of machine learning it is appropriate or even necessary to make use of human expertise to compensate a too small amount or low quality of data. Taking into account knowledge which is available in explicit form reduces the amount of data needed for learning. Furthermore, even if domain experts cannot formulate knowledge explicitly, they typically can recognize and correct erroneous decisions or actions. This type of implicit knowledge can be injected into the learning process to guide model adapation. These insights have led to the so-called third wave of AI with a focus on explainablity (XAI). In the talk, I will introduce research on explanatory and interactive machine learning. I will present inductive programming as a powerful approach to learn interpretable models in relational domains. Arguing for the need of specific exlanations for different stakeholders and goals, I will introduce different types of explanations based on theories and findings from cognitive science. Furthermore, I will show how intelligent tutor systems and XAI can be combined to support constructive learning. Algorithmic realisations of explanation generation will be complemented with results from psychological experiments investigating the effect on joint human-AI task performance and trust. Finally, current research projects are introduced to illustrate applications of the presented work in medical diagnostics, quality control in industrial production, file management, and accountability.

About: Ute Schmid is chair of Cognitive Systems at University of Bamberg. She received university diplomas both in psychology and in computer science from Technical University Berlin (TUB) where she also  received her doctoral degree (Dr.rer.nat., 1994) and her habilitation (2002) in computer science. From 1994 to 2001 she was assistant professor at the Methods of AI/Machine Learning group, Department of Computer Science, TUB. After a one year stay as DFG-funded researcher at Carnegie Mellon University, 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. Main research interest of Ute Schmid is interpretable machine learning, especially learning expressive (recursive) models for complex cognitive problems. Ute Schmid is member of the board of directors of the Bavarian Insistute of Digital Transformation (bidt) and a member of the Bavarian AI Council (Bayerischer KI-Rat). Since 2020 she 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 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. For her outreach activities she has been awarded with the Rainer-Markgraf-Preis 2020.

Ute Schmid at University of Bamberg