Trustworthy Hybrid AI
While in recent years AI systems have shown an increased performance at solving complex tasks, these systems still suffer from a lack of reliability, robustness, transparency and other related problems that question their trustworthiness. A trend that aims at improving the trustworthiness of these systems is hybrid AI that will be introduced with a taxonomy and modular design patterns for describing and designing trustworthy AI systems to unify statistical (data-driven) and symbolic (knowledge-driven) methods. We will also dive into graph-representation and multi-agent learning and reasoning in order to give some examples and a context in which the patterns can be used
Dr. André Meyer-Vitali is a computer scientist who got his Ph.D. in software engineering and distributed AI from the University of Zürich. He worked on many applied research projects on multi-agent systems at Philips Research and TNO (The Netherlands) and participated in AgentLink. He also worked at the European Patent Office. Currently, he is a senior researcher at DFKI (Germany) focused on engineering and promoting Trusted AI and is active in the AI networks TAILOR and CLAIRE. His research interests include Software und Knowledge Engineering, Design Patterns, Hybrid Neuro-Symbolic AI, Causality, and Agent-based Social Simulation (ABSS) with the aim to create Trust by Design.