  As opaque decision systems are being increasingly adopted in almost any
application field, issues about their lack of transparency and human
readability are a concrete concern for end-users. Amongst existing proposals to
associate human-interpretable knowledge with accurate predictions provided by
opaque models, there are rule extraction techniques, capable of extracting
symbolic knowledge out of an opaque model. However, how to assess the level of
readability of the extracted knowledge quantitatively is still an open issue.
Finding such a metric would be the key, for instance, to enable automatic
comparison between a set of different knowledge representations, paving the way
for the development of parameter autotuning algorithms for knowledge
extractors. In this paper we discuss the need for such a metric as well as the
criticalities of readability assessment and evaluation, taking into account the
most common knowledge representations while highlighting the most puzzling
issues.
