full-digital --- no travel -- deadline: May, 06, 2020

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 in order to bring together experts from different fields, e.g. computer science, psychology, sociology, philosophy, law, business, … experts who would otherwise possibly not meet together. This cross-domain integration and appraisal of different fields of science and industry shall provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel crazy ideas and a fresh look on the methodologies to put these ideas into business.

Topics include but are not limited to (alphabetically – not prioritized):

Acceptance (“How to ensure acceptance of AI/ML among end users?”)
Accountability and Responsibility (“Who is to blame if something goes wrong?”)
Affective computing for successful human-AI interaction (human-robot interaction)
Argumentation theories of explanations
Artificial Advice Givers
Bayesian rule lists
Bias and Fairness
Causal learning, causal discovery and causal inference
Causality and Causability research
Cognitive issues of explanation and understanding (“understanding understanding”)
Combination of statistical learning approaches with large knowledge repositories (ontologies, terminologies)
Comparison of Human intelligence vs. Artificial Intelligence (HCI — KDD)
Cyber security, Cyber defense and malicious use of adversarial examples
Decision making and decision support systems (“Is a human-like decision good enough?)
Emotional intelligence (“Emotion AI”)
Ethical aspects of AI in general and human-AI interaction in particular
Explanation agents and recommender systems
Explanatory User Interfaces and Human-Computer Interaction (HCI) for explainable AI
Fairness, Accountability and Trust (“How to ensure trust in AI?”)
Frameworks, architectures, algorithms and tools to support post-hoc and ante-hoc explainability
Graphical causal inference and graphical models for explanation and causality
Ground truth
Group recommender systems
Human rights vs. Robot rights
Interactive machine learning with a human-in-the-loop
Interactive machine learning with (many) humans-in-the-loop (crowd intelligence)
Kandinsky Patterns experiments and extensions
Legal aspects of AI/ML (“Who is to blame if an error occurs?”)
Moral principles and Moral dilemmas of current and future AI
Novel intelligent user interfaces (e.g. affective mobile interfaces)
Novel methods, algorithms, tools, procedures for supporting explainability in AI/ML
Philosophical approaches of explainability (“When is it enough explained? Do we have a degree of saturation?”)
Proof-of-concepts and demonstrators of how to integrate explainable AI into real-world workflows and industrial processes
Privacy, surveillance, control and agency
Python for nerds (Python tricks of the trade – relevant for explainable AI)
Self-explanatory agents and decision support systems
Social implications of AI (“What AI impacts”), e.g. labour trends, human-human interaction, machine-machine interaction
Spartanic approaches of explanations (“What is the most simplest explanation?”)
Theoretical approaches of explainability (“What makes a good explanation?”)
Web- and mobile-based cooperative intelligent information systems and tools