  There is increased interest in assisting non-expert audiences to effectively
interact with machine learning (ML) tools and understand the complex output
such systems produce. Here, we describe user experiments designed to study how
individual skills and personality traits predict interpretability,
explainability, and knowledge discovery from ML generated model output. Our
work relies on Fuzzy Trace Theory, a leading theory of how humans process
numerical stimuli, to examine how different end users will interpret the output
they receive while interacting with the ML system. While our sample was small,
we found that interpretability -- being able to make sense of system output --
and explainability -- understanding how that output was generated -- were
distinct aspects of user experience. Additionally, subjects were more able to
interpret model output if they possessed individual traits that promote
metacognitive monitoring and editing, associated with more detailed, verbatim,
processing of ML output. Finally, subjects who are more familiar with ML
systems felt better supported by them and more able to discover new patterns in
data; however, this did not necessarily translate to meaningful insights. Our
work motivates the design of systems that explicitly take users' mental
representations into account during the design process to more effectively
support end user requirements.
