In line with the general theme of the CD-MAKE conference of augmenting human intelligence with artificial intelligence, and Science is to test crazy ideas – Engineering is to bring these ideas into Business – we foster cross-disciplinary and interdisciplinary work including but not limited to:

Novel methods, algorithms, tools, procedures for supporting explainability in AI/ML
Proof-of-concepts and demonstrators of how to integrate explainable AI into workflows and industrial processes
Frameworks, architectures, algorithms and tools to support post-hoc and ante-hoc explainability
Work on causality machine learning
Theoretical approaches of explainability (“What makes a good explanation?”)
Philsophical approaches of explainability (“When is it enough, do we have a degree of saturation?”)
Towards argumentation theories of explanation and issues of cognition
Comparison Human intelligence vs. Artificial Intelligence (HCI — KDD)
Interactive machine learning with human(s)-in-the-loop (crowd intelligence)
Explanatory User Interfaces and Human-Computer Interaction (HCI) for explainable AI
Novel Intelligent User Interfaces and affective computing approaches
Fairness, accountability and trust
Ethical aspects and law, legal issues and social responsibility
Business aspects of explainable AI
Self-explanatory agents and decision support systems
Explanation agents and recommender systems
Combination of statistical learning approaches with large knowledge repositories (ontologies)