  Over the last few years, the Shapley value, a solution concept from
cooperative game theory, has found numerous applications in machine learning.
In this paper, we first discuss fundamental concepts of cooperative game theory
and axiomatic properties of the Shapley value. Then we give an overview of the
most important applications of the Shapley value in machine learning: feature
selection, explainability, multi-agent reinforcement learning, ensemble
pruning, and data valuation. We examine the most crucial limitations of the
Shapley value and point out directions for future research.
