Given the black box nature of machine learning models, a plethora of
explainability methods have been developed to decipher the factors behind
individual decisions. In this paper, we introduce a novel problem of black box
(probabilistic) explanation certification. We ask the question: Given a black
box model with only query access, an explanation for an example and a quality
metric (viz. fidelity, stability), can we find the largest hypercube (i.e.,
$\ell_{\infty}$ ball) centered at the example such that when the explanation is
applied to all examples within the hypercube, (with high probability) a quality
criterion is met (viz. fidelity greater than some value)? Being able to
efficiently find such a \emph{trust region} has multiple benefits: i) insight
into model behavior in a \emph{region}, with a \emph{guarantee}; ii)
ascertained \emph{stability} of the explanation; iii) \emph{explanation reuse},
which can save time, energy and money by not having to find explanations for
every example; and iv) a possible \emph{meta-metric} to compare explanation
methods. Our contributions include formalizing this problem, proposing
solutions, providing theoretical guarantees for these solutions that are
computable, and experimentally showing their efficacy on synthetic and real
data.

在这篇论文中，我们介绍了一个名为黑盒（概率）解释认证的新问题。我们通过给出一个黑盒模型、一个示例的解释以及一个质量度量（如逼真度和稳定性），问能否找到一个最大的超立方体（即以该示例为中心的 L∞球），使得当解释应用于超立方体内的所有示例时，能够以很高的概率满足质量准则（如逼真度大于某个值）。我们的贡献包括形式化这个问题、提出解决方案、提供可计算的这些解决方案的理论保证，并在合成和真实数据上进行实验以证明它们的有效性。