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arXiv · 2012.03058

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations

Xingyu Zhao·Wei Huang·Xiaowei Huang·Valentin Robu·David Flynn
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Abstract

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI -- which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

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Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn. 2020-12-05. BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. https://arxiv.org/abs/2012.03058

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