Explaining Black Boxes With a SMILE: Statistical Model-Agnostic Interpretability With Local Explanations

被引：0
|
作者：
Aslansefat, Koorosh [1 ]
Hashemian, Mojgan [2 ]
Walker, Martin [1 ]
Akram, Mohammed Naveed [3 ]
Sorokos, Ioannis [3 ]
Papadopoulos, Yiannis [4 ]
机构：
[1] Univ Hull, Comp Sci, Kingston Upon Hull HU6 7RX, England
[2] Direct Line Grp Ltd, Leeds LS1 4AZ, England
[3] Fraunhofer Inst Expt Software Engn, D-67663 Kaiserslautern, Germany
[4] Univ Hull, Dependable Intelligent Syst Res Grp, Kingston Upon Hull HU6 7RX, England
来源：
IEEE SOFTWARE | 2024年 / 41卷 / 01期
关键词：
Closed Box; Perturbation Methods; Predictive Models; Gaussian Distribution; Data Models; Machine Learning; Training; Object Object; Use Of Measures; Statistical Measures; Wide Range Of Domains; Growth In Recent Years; Statistical Distance; Variety Of Supports; Linear Model Object Object; Alternative Models Object Object; Model Coefficients; Maximum Distance; Intersection Over Union Object Object Object Object Object Object; Kernel Function; Input Samples; Light Signal Object Object; Part Of The Image; Adversarial Attacks; Random Perturbations; Perturbation Vector; Human Intuition; Game Theory Object Object; Understanding Of Models;
D O I：
10.1109/MS.2023.3321282
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Explainability is a key aspect of improving trustworthiness. We therefore propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains.
引用
收藏
页码：87 / 97
页数：11
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