Evaluating the Correctness of Explainable AI Algorithms for
  Classification
  Explainable AI has attracted much research attention in recent years with
feature attribution algorithms, which compute "feature importance" in
predictions, becoming increasingly popular. However, there is little analysis
of the validity of these algorithms as there is no "ground truth" in the
existing datasets to validate their correctness. In this work, we develop a
method to quantitatively evaluate the correctness of XAI algorithms by creating
datasets with known explanation ground truth. To this end, we focus on the
binary classification problems. String datasets are constructed using formal
language derived from a grammar. A string is positive if and only if a certain
property is fulfilled. Symbols serving as explanation ground truth in a
positive string are part of an explanation if and only if they contributes to
fulfilling the property. Two popular feature attribution explainers, Local
Interpretable Model-agnostic Explanations (LIME) and SHapley Additive
exPlanations (SHAP), are used in our experiments.We show that: (1)
classification accuracy is positively correlated with explanation accuracy; (2)
SHAP provides more accurate explanations than LIME; (3) explanation accuracy is
negatively correlated with dataset complexity.
