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arXiv · 2305.02780

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations

Susanne Dandl·Giuseppe Casalicchio·Bernd Bischl·Ludwig Bothmann
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Abstract

This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values can be changed without affecting its prediction. They justify a prediction by providing a set of "even if" arguments (semi-factual explanations), and they indicate which features affect a prediction and whether pointwise biases or implausibilities exist. A concrete use case shows that this is valuable for both machine learning modelers and persons subject to a decision. We formalize the search for IRDs as an optimization problem and introduce a unifying framework for computing IRDs that covers desiderata, initialization techniques, and a post-processing method. We show how existing hyperbox methods can be adapted to fit into this unified framework. A benchmark study compares the methods based on several quality measures and identifies two strategies to improve IRDs.

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Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann. 2023-05-04. Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. https://doi.org/10.1007/978-3-031-43418-1_29

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