  Explainable components in XAI algorithms often come from a familiar set of
models, such as linear models or decision trees. We formulate an approach where
the type of explanation produced is guided by a specification. Specifications
are elicited from the user, possibly using interaction with the user and
contributions from other areas. Areas where a specification could be obtained
include forensic, medical, and scientific applications. Providing a menu of
possible types of specifications in an area is an exploratory knowledge
representation and reasoning task for the algorithm designer, aiming at
understanding the possibilities and limitations of efficiently computable modes
of explanations. Two examples are discussed: explanations for Bayesian networks
using the theory of argumentation, and explanations for graph neural networks.
The latter case illustrates the possibility of having a representation
formalism available to the user for specifying the type of explanation
requested, for example, a chemical query language for classifying molecules.
The approach is motivated by a theory of explanation in the philosophy of
science, and it is related to current questions in the philosophy of science on
the role of machine learning.
