Relational Local Explanations

Vadim Borisov; Gjergji Kasneci

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arXiv · 2212.12374

Relational Local Explanations

Vadim Borisov·Gjergji Kasneci

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Abstract

The majority of existing post-hoc explanation approaches for machine learning models produce independent, per-variable feature attribution scores, ignoring a critical inherent characteristics of homogeneously structured data, such as visual or text data: there exist latent inter-variable relationships between features. In response, we develop a novel model-agnostic and permutation-based feature attribution approach based on the relational analysis between input variables. As a result, we are able to gain a broader insight into the predictions and decisions of machine learning models. Experimental evaluations of our framework in comparison with state-of-the-art attribution techniques on various setups involving both image and text data modalities demonstrate the effectiveness and validity of our method.

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Vadim Borisov, Gjergji Kasneci. 2023-02-11. Relational Local Explanations. https://arxiv.org/abs/2212.12374

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