Reconciling knowledge-based and data-driven AI for human-in-the-loop machine learning
November 17th, 2021 1:15 PM - 2:00 PM EST
Ute Schmid
Professor of Cognitive Systems
Bamberg University
Website: https://www.uni-bamberg.de/en/cogsys/schmid/
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.
Bio: Ute Schmid is professor of Cognitive Systems at the University of Bamberg. She has university diplomas in computer science as well as psychology, and a doctor degree and a habilitation in computer science from TU Berlin. Her research interests are in the domain of human-level machine learning, explainable AI, and learning on relational data, especially inductive programming. Research topics are explanation generation, cognitive tutor systems, and cooperative and interactive learning. Ute Schmid is head of the Fraunhofer IIS project group Comprehensible AI (CAI) and member of the Bavarian AI council. She has been president of the German Cognitive Science Socienty (GK) and speaker of the SIG Cognition of the Section for AI of the German Computer Science Society (GI). Ute dedicates a significant amount of her time to measures supporting women in computer science and in 2018 won the Minerva Gender Equality Award of Informatics Europe for her university. Since many years she offers and organizes computer science workshops for children, including workshops on AI. For her outreach activities, in 2020 she has received the Rainer-Markgraf Award.