Deep learning has revolutionized many areas of machine learning, from
computer vision to natural language processing, but these high-performance
models are generally "black box." Explaining such models would improve
transparency and trust in AI-powered decision making and is necessary for
understanding other practical needs such as robustness and fairness. A popular
means of enhancing model transparency is to quantify how individual inputs
contribute to model outputs (called attributions) and the magnitude of
interactions between groups of inputs. A growing number of these methods import
concepts and results from game theory to produce attributions and interactions.
This work presents a unifying framework for game-theory-inspired attribution
and $k^\text{th}$-order interaction methods. We show that, given modest
assumptions, a unique full account of interactions between features, called
synergies, is possible in the continuous input setting. We identify how various
methods are characterized by their policy of distributing synergies. We also
demonstrate that gradient-based methods are characterized by their actions on
monomials, a type of synergy function, and introduce unique gradient-based
methods. We show that the combination of various criteria uniquely defines the
attribution/interaction methods. Thus, the community needs to identify goals
and contexts when developing and employing attribution and interaction methods.

本文介绍了一种基于博弈论的归因和 k 阶相互作用方法的统一框架，重点是解释和解释模型的透明性。作者指出，各种方法都基于其在协同方案中的政策，并介绍了基于梯度的独特方法。最后，作者提出了需要深入了解归因和交互方法的目标和上下文。