用少量简单模型生成全局解释，让黑箱模型更透明。
ExplainReduce: Generating global explanations from many local explanations
- 从大量局部解释中提炼出少数代表性模型作为全局代理
- 仅需5个解释即可准确模拟原黑箱模型行为
- 适合需要可解释性但不想牺牲性能的开发者
多数常用非线性机器学习模型为黑箱，难以被人理解。可解释人工智能（XAI）旨在开发工具以揭示这些黑箱的内部机制。一种常见的模型无关方法是使用简单模型作为局部近似，生成局部解释，如LIME、SHAP和SLISEMAP。本文提出，可通过优化问题将大量局部解释压缩为少数“代理模型”，形成生成式全局解释。该方法名为ExplainReduce，采用贪心启发式高效求解。实验表明，对多个任务而言，仅需五个解释即可忠实再现黑箱模型行为，且其性能优于其他模型聚合方法。
原文摘要 · Abstract (English)
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.
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