Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Scrutinizing XAI using linear ground-truth data with suppressor variablesopen access

Authors
Wilming, RickBudding, CelineMueller, Klaus-RobertHaufe, Stefan
Issue Date
5월-2022
Publisher
SPRINGER
Keywords
Explainable AI; Saliency methods; Ground truth; Benchmark; Linear classification; Suppressor variables
Citation
MACHINE LEARNING, v.111, no.5, pp.1903 - 1923
Indexed
SCIE
SCOPUS
Journal Title
MACHINE LEARNING
Volume
111
Number
5
Start Page
1903
End Page
1923
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143076
DOI
10.1007/s10994-022-06167-y
ISSN
0885-6125
Abstract
Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of procedures has been developed to shed light on their inner workings and the ways in which their predictions come about, defining the field of 'explainable AI' (XAI). Saliency methods rank input features according to some measure of 'importance'. Such methods are difficult to validate since a formal definition of feature importance is, thus far, lacking. It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables). To avoid misinterpretations due to such behavior, we propose the actual presence of such an association as a necessary condition and objective preliminary definition for feature importance. We carefully crafted a ground-truth dataset in which all statistical dependencies are well-defined and linear, serving as a benchmark to study the problem of suppressor variables. We evaluate common explanation methods including LRP, DTD, PatternNet, PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with respect to our objective definition. We show that most of these methods are unable to distinguish important features from suppressors in this setting.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE