BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations

12/05/2020 ∙ by Xingyu Zhao, et al. ∙ 0

A key impediment to the use of AI is the lacking of transparency, especially in safety/security critical applications. The black-box nature of AI systems prevents humans from direct explanations on how the AI makes predictions, which stimulated Explainable AI (XAI) – a research field that aims at improving the trust and transparency of AI systems. In this paper, we introduce a novel XAI technique, BayLIME, which is a Bayesian modification of the widely used XAI approach LIME. BayLIME exploits prior knowledge to improve the consistency in repeated explanations of a single prediction and also the robustness to kernel settings. Both theoretical analysis and extensive experiments are conducted to support our conclusions.



There are no comments yet.


page 2

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.