  Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks,
have been widely utilized to build predictive models that can extract complex
relationships in a dataset and make predictions for new unseen data records.
However, it is difficult to trust decisions made by such methods since their
inner working and decision logic is hidden from the user. Explainable
Artificial Intelligence (XAI) refers to systems that try to explain how a
black-box AI model produces its outcomes. Post-hoc XAI methods approximate the
behavior of a black-box by extracting relationships between feature values and
the predictions. Perturbation-based and decision set methods are among commonly
used post-hoc XAI systems. The former explanators rely on random perturbations
of data records to build local or global linear models that explain individual
predictions or the whole model. The latter explanators use those feature values
that appear more frequently to construct a set of decision rules that produces
the same outcomes as the target black-box. However, these two classes of XAI
methods have some limitations. Random perturbations do not take into account
the distribution of feature values in different subspaces, leading to
misleading approximations. Decision sets only pay attention to frequent feature
values and miss many important correlations between features and class labels
that appear less frequently but accurately represent decision boundaries of the
model. In this paper, we address the above challenges by proposing an
explanation method named Confident Itemsets Explanation (CIE). We introduce
confident itemsets, a set of feature values that are highly correlated to a
specific class label. CIE utilizes confident itemsets to discretize the whole
decision space of a model to smaller subspaces.
