  In the last years, XAI research has mainly been concerned with developing new
technical approaches to explain deep learning models. Just recent research has
started to acknowledge the need to tailor explanations to different contexts
and requirements of stakeholders. Explanations must not only suit developers of
models, but also domain experts as well as end users. Thus, in order to satisfy
different stakeholders, explanation methods need to be combined. While
multi-modal explanations have been used to make model predictions more
transparent, less research has focused on treating explanation as a process,
where users can ask for information according to the level of understanding
gained at a certain point in time. Consequently, an opportunity to explore
explanations on different levels of abstraction should be provided besides
multi-modal explanations. We present a process-based approach that combines
multi-level and multi-modal explanations. The user can ask for textual
explanations or visualizations through conversational interaction in a
drill-down manner. We use Inductive Logic Programming, an interpretable machine
learning approach, to learn a comprehensible model. Further, we present an
algorithm that creates an explanatory tree for each example for which a
classifier decision is to be explained. The explanatory tree can be navigated
by the user to get answers of different levels of detail. We provide a
proof-of-concept implementation for concepts induced from a semantic net about
living beings.
