  We are used to the availability of big data generated in nearly all fields of
science as a consequence of technological progress. However, the analysis of
such data possess vast challenges. One of these relates to the explainability
of artificial intelligence (AI) or machine learning methods. Currently, many of
such methods are non-transparent with respect to their working mechanism and
for this reason are called black box models, most notably deep learning
methods. However, it has been realized that this constitutes severe problems
for a number of fields including the health sciences and criminal justice and
arguments have been brought forward in favor of an explainable AI. In this
paper, we do not assume the usual perspective presenting explainable AI as it
should be, but rather we provide a discussion what explainable AI can be. The
difference is that we do not present wishful thinking but reality grounded
properties in relation to a scientific theory beyond physics.
