  A vital component of trust and transparency in intelligent systems built on
machine learning and artificial intelligence is the development of clear,
understandable documentation. However, such systems are notorious for their
complexity and opaqueness making quality documentation a non-trivial task.
Furthermore, little is known about what makes such documentation "good." In
this paper, we propose and evaluate a set of quality dimensions to identify in
what ways this type of documentation falls short. Then, using those dimensions,
we evaluate three different approaches for eliciting intelligent system
documentation. We show how the dimensions identify shortcomings in such
documentation and posit how such dimensions can be use to further enable users
to provide documentation that is suitable to a given persona or use case.
