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.

翻译：深度学习已从计算机视觉到自然语言处理等多个机器学习领域带来革命性变革，但这些高性能模型通常属于"黑箱"。解释此类模型有助于提升AI决策的透明度与可信度，并对理解鲁棒性、公平性等实际需求至关重要。增强模型透明度的常用方法是量化单个输入对模型输出的贡献（称为归因）以及输入组之间的交互强度。越来越多的此类方法引入博弈论的概念与成果来生成归因与交互分析。本研究提出了一个统一框架，用于整合受博弈论启发的归因及k阶交互方法。我们证明，在适度假设下，连续输入场景中可实现特征间交互的完整描述（称为协同效应）。我们揭示了不同方法如何通过其协同效应分配策略来表征，并论证基于梯度的方法通过其对单项式（一类协同函数）的作用来定义，从而提出了独特的梯度方法。研究表明，多种准则的组合能唯一确定归因/交互方法。因此，研究界在开发和应用归因与交互方法时，需明确其目标与适用场景。