  While a lot of research in explainable AI focuses on producing effective
explanations, less work is devoted to the question of how people understand and
interpret the explanation. In this work, we focus on this question through a
study of saliency-based explanations over textual data. Feature-attribution
explanations of text models aim to communicate which parts of the input text
were more influential than others towards the model decision. Many current
explanation methods, such as gradient-based or Shapley value-based methods,
provide measures of importance which are well-understood mathematically. But
how does a person receiving the explanation (the explainee) comprehend it? And
does their understanding match what the explanation attempted to communicate?
We empirically investigate the effect of various factors of the input, the
feature-attribution explanation, and visualization procedure, on laypeople's
interpretation of the explanation. We query crowdworkers for their
interpretation on tasks in English and German, and fit a GAMM model to their
responses considering the factors of interest. We find that people often
mis-interpret the explanations: superficial and unrelated factors, such as word
length, influence the explainees' importance assignment despite the explanation
communicating importance directly. We then show that some of this distortion
can be attenuated: we propose a method to adjust saliencies based on model
estimates of over- and under-perception, and explore bar charts as an
alternative to heatmap saliency visualization. We find that both approaches can
attenuate the distorting effect of specific factors, leading to
better-calibrated understanding of the explanation.
