ExplainReduce: Summarising local explanations via proxies

• URL: http://arxiv.org/abs/2502.10311v1
• Date: Fri, 14 Feb 2025 17:14:02 GMT
• Title: ExplainReduce: Summarising local explanations via proxies
• Authors: Lauri Seppäläinen, Mudong Guo, Kai Puolamäki,
• Abstract summary: An often-used model-agnostic approach to XAI involves using simple models as local approximations to produce so-called local explanations. 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.
• Score: 2.3185929089334594
• License:
• Abstract: 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.

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