Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial intelligence (XAI) aims to develop tools to examine the inner workings of these closed boxes. An often-used model-agnostic approach to XAI involves using simple models as local approximations to produce so-called local explanations; examples of this approach include LIME, SHAP, and SLISEMAP. This paper shows how a large set of local explanations can be reduced to a small "proxy set" of simple models, which can act as a generative global explanation. This reduction procedure, ExplainReduce, can be formulated as an optimisation problem and approximated efficiently using greedy heuristics. We show that, for many problems, as few as five explanations can faithfully emulate the closed-box model and that our reduction procedure is competitive with other model aggregation methods.

翻译：最常用的非线性机器学习方法多为黑箱模型，人类难以理解其机理。可解释人工智能（XAI）领域旨在开发工具以探查这些黑箱的内部运作机制。一种常用的模型不可知XAI方法是通过使用简单模型作为局部近似来生成所谓的局部解释，例如LIME、SHAP和SLISEMAP。本文展示了如何将大量局部解释简化为一个由简单模型组成的小型"代理集"，该集合可作为生成式全局解释。这种简化过程（称为ExplainReduce）可表述为优化问题，并通过贪婪启发式算法进行高效近似求解。我们证明，对于许多问题而言，仅需五个解释便可忠实模拟黑箱模型，且我们的简化过程在模型聚合方法中具有竞争力。