  Explainable artificial intelligence techniques are developed at breakneck
speed, but suitable evaluation approaches lag behind. With explainers becoming
increasingly complex and a lack of consensus on how to assess their utility, it
is challenging to judge the benefit and effectiveness of different
explanations. To address this gap, we take a step back from sophisticated
predictive algorithms and instead look into explainability of simple
decision-making models. In this setting, we aim to assess how people perceive
comprehensibility of their different representations such as mathematical
formulation, graphical representation and textual summarisation (of varying
complexity and scope). This allows us to capture how diverse stakeholders --
engineers, researchers, consumers, regulators and the like -- judge
intelligibility of fundamental concepts that more elaborate artificial
intelligence explanations are built from. This position paper charts our
approach to establishing appropriate evaluation methodology as well as a
conceptual and practical framework to facilitate setting up and executing
relevant user studies.
