We call for contributions that focus on, but are not limited to the following topics with cross-domain applications:
– Explanations beyond the DNN classifiers: Random forests, unsupervised learning, reinforcement learning
– Explanations beyond heat maps: structured explanations, Q/A and dialog systems, human-in-the-loop
– Explanation beyond explanation : improve ML models and algorithms, verify ML, gain insights
including, but not limited to (alphabetically, not prioritized):
- Adversarial attacks explainability
- Believability and manipulability of explantions (especially in contexts where they need to meet a legal evidence standard)
- Explainability, Causality, Causability (Causa-bi-lity is not a typo, see definitions below *)
- Causability (the measureable extent to which an explanation to a human achieves a specified level of causal understanding, see Holzinger)
- Counterfactual explanations
- Dialogue Systems for xAI
- Graph Neural Networks
- Human-AI interfaces
- Human-centered AI and responsibility
- Human interpretability
- Interactive Machine Learning with the human-in-the-loop
- Interpretable Models (vs. post-hoc explanations)
- Intelligent User Interfaces
- Knowledge Graphs
- Multi-Classifier Systems
- Ontologies and xAI
- Question/Answering Dialog Systems
- Explainability and Recommender systems
*) Please discriminate:
- Explainability := technically highlights decision relevant parts of machine representations and machine models i.e., parts which contributed to model accuracy in training, or to a specific prediction. It does NOT refer to a human model !
- Causality : = relationship between cause and effect in the sense of Pearl
- Causability := the measureable extent to which an explanation to a human achieves a specified level of causal understanding, see Holzinger) It does refer to a human model !
