  This paper aims to develop a new attribution method to explain the conflict
between individual variables' attributions and their coalition's attribution
from a fully new perspective. First, we find that the Shapley value can be
reformulated as the allocation of Harsanyi interactions encoded by the AI
model. Second, based the re-alloction of interactions, we extend the Shapley
value to the attribution of coalitions. Third we ective. We derive the
fundamental mechanism behind the conflict. This conflict come from the
interaction containing partial variables in their coalition.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition