Zhao, X ORCID: 0000-0002-3474-349X, Huang, W, Huang, X ORCID: 0000-0001-6267-0366, Robu, V and Flynn, D
(2021)
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations.
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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.
Item Type: | Conference or Workshop Item (Unspecified) |
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Uncontrolled Keywords: | cs.AI, cs.AI |
Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
Depositing User: | Symplectic Admin |
Date Deposited: | 14 May 2021 07:51 |
Last Modified: | 19 Jul 2023 10:39 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3122584 |
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