  With wide application of Artificial Intelligence (AI), it has become
particularly important to make decisions of AI systems explainable and
transparent. In this paper, we proposed a new Explainable Artificial
Intelligence (XAI) method called ShapG (Explanations based on Shapley value for
Graphs) for measuring feature importance. ShapG is a model-agnostic global
explanation method. At the first stage, it defines an undirected graph based on
the dataset, where nodes represent features and edges are added based on
calculation of correlation coefficients between features. At the second stage,
it calculates an approximated Shapley value by sampling the data taking into
account this graph structure. The sampling approach of ShapG allows to
calculate the importance of features efficiently, i.e. to reduce computational
complexity. Comparison of ShapG with other existing XAI methods shows that it
provides more accurate explanations for two examined datasets. We also compared
other XAI methods developed based on cooperative game theory with ShapG in
running time, and the results show that ShapG exhibits obvious advantages in
its running time, which further proves efficiency of ShapG. In addition,
extensive experiments demonstrate a wide range of applicability of the ShapG
method for explaining complex models. We find ShapG an important tool in
improving explainability and transparency of AI systems and believe it can be
widely used in various fields.
