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Explainable artificial intelligence techniques are developed at breakneckspeed, but suitable evaluation approaches lag behind. With explainers becomingincreasingly complex and a lack of consensus on how to assess their utility, itis challenging to judge the benefit and effectiveness of differentexplanations. To address this gap, we take a step back from sophisticatedpredictive algorithms and instead look into explainability of simpledecision-making models. In this setting, we aim to assess how people perceivecomprehensibility of their different representations such as mathematicalformulation, graphical representation and textual summarisation (of varyingcomplexity and scope). This allows us to capture how diverse stakeholders --engineers, researchers, consumers, regulators and the like -- judgeintelligibility of fundamental concepts that more elaborate artificialintelligence explanations are built from. This position paper charts ourapproach to establishing appropriate evaluation methodology as well as aconceptual and practical framework to facilitate setting up and executingrelevant user studies.

Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations