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：深度学习已彻底改变了从计算机视觉到自然语言处理等机器学习诸多领域，但这些高性能模型通常是"黑箱"。解释此类模型可提升AI驱动决策的透明度和可信度，且对于理解鲁棒性和公平性等其他实际需求不可或缺。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）以及输入组间交互的强度。越来越多的此类方法引入博弈论概念与成果来生成归因和交互。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$k^\text{th}$阶交互方法。我们证明，在适度假设下，连续输入空间中特征间交互的完整描述（称为协同）具有唯一性。我们揭示了不同方法如何通过其分配协同的策略加以表征，并论证了基于梯度的方法由其单项式（一种协同函数）上的作用所定义，进而引入了独特的基于梯度的方法。研究表明，各类准则的组合唯一确定了归因/交互方法。因此，社区在开发和应用归因与交互方法时需明确目标与场景。