Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by revealing the most contributing features to decisions that have been made. A widely accepted way of deriving feature attributions is to analyze the gradients of the target function with respect to input features. Analysis of gradients requires full access to the target system, meaning that solutions of this kind treat the target system as a white-box. However, the white-box assumption may be untenable due to security and safety concerns, thus limiting their practical applications. As an answer to the limited flexibility, this paper presents GEEX (gradient-estimation-based explanation), an explanation method that delivers gradient-like explanations under a black-box setting. Furthermore, we integrate the proposed method with a path method. The resulting approach iGEEX (integrated GEEX) satisfies the four fundamental axioms of attribution methods: sensitivity, insensitivity, implementation invariance, and linearity. With a focus on image data, the exhaustive experiments empirically show that the proposed methods outperform state-of-the-art black-box methods and achieve competitive performance compared to the ones with full access.

翻译：归因方法通过揭示对决策贡献最大的特征来阐明数据驱动方法（如深度学习模型）的可解释性。一种广泛接受的获取特征归因的方法是通过分析目标函数相对于输入特征的梯度。梯度分析要求对目标系统具有完全的访问权限，这意味着此类方法将目标系统视为白盒。然而，出于安全和保密考虑，白盒假设可能无法成立，从而限制了其实际应用。为解决灵活性有限的问题，本文提出了GEEX（基于梯度估计的解释），这是一种在黑盒设置下提供类似梯度解释的方法。此外，我们将所提出的方法与路径方法相结合，得到的方法iGEEX（集成GEEX）满足归因方法的四个基本公理：敏感性、不敏感性、实现不变性和线性。以图像数据为重点，大量实验经验性地证明，所提出的方法优于最先进的黑盒方法，并与具有完全访问权限的方法相比取得了具有竞争力的性能。