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

Towards evaluating and eliciting high-quality documentation for intelligent systems