Reconciling knowledge-based and data-driven AI for human-in-the-loop machine learning

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. In the talk, I will introduce inductive logic programming (ILP) as a powerful interpretable machine learning approach which allows to combine logic and learning. In ILP domain theories, background knowledge, training examples, and the learned model are represented in the same format, namely Horn theories. I will present first ideas how to combine CNNS and ILP into a neuro-symbolic framework. Afterwards, I will address the topic of explanatory AI. I will argue that, although ILP-learned models are symbolic (white-box), it might nevertheless be necessary to explain system decisions. Depending on who needs an explanation for what goal in which situation, different forms of explanations are necessary. I will show how ILP can be combined with different methods for explanation generation and propose a framework for human-in-the-loop learning. There, explanations are designed to be mutual -- not only from the AI system for the human but also the other way around. The presented approach will be illustrated with different application domains from medical diagnostics, file management, and quality control in manufacturing.

Short CV - Ute Schmid is a professor of Applied Computer Science/Cognitive Systems at University of Bamberg since 2004. She received university diplomas both in psychology and in computer science from Technical University Berlin (TUB). She received her doctoral degree (Dr.rer.nat.) in computer science in 1994 and her habilitation in computer science in 2002 from TUB. 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. 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.

https://tu-dortmund.zoom.us/j/93031373687?pwd=MmdxT3Y5NWVJWTVTa0lTcnIzczBpUT09