  Explainable artificial intelligence (XAI) methods are currently evaluated
with approaches mostly originated in interpretable machine learning (IML)
research that focus on understanding models such as comparison against existing
attribution approaches, sensitivity analyses, gold set of features, axioms, or
through demonstration of images. There are problems with these methods such as
that they do not indicate where current XAI approaches fail to guide
investigations towards consistent progress of the field. They do not measure
accuracy in support of accountable decisions, and it is practically impossible
to determine whether one XAI method is better than the other or what the
weaknesses of existing models are, leaving researchers without guidance on
which research questions will advance the field. Other fields usually utilize
ground-truth data and create benchmarks. Data representing ground-truth
explanations is not typically used in XAI or IML. One reason is that
explanations are subjective, in the sense that an explanation that satisfies
one user may not satisfy another. To overcome these problems, we propose to
represent explanations with canonical equations that can be used to evaluate
the accuracy of XAI methods. The contributions of this paper include a
methodology to create synthetic data representing ground-truth explanations,
three data sets, an evaluation of LIME using these data sets, and a preliminary
analysis of the challenges and potential benefits in using these data to
evaluate existing XAI approaches. Evaluation methods based on human-centric
studies are outside the scope of this paper.
