Title
Fooling LIME and SHAP - Adversarial Attacks on Post hoc Explanation Methods.
Abstract
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.
Year
DOI
Venue
2020
10.1145/3375627.3375830
AIES
Field
DocType
Citations 
Lime,Computer science,Post hoc,Artificial intelligence,Machine learning,Adversarial system
Conference
4
PageRank 
References 
Authors
0.42
0
5
Name
Order
Citations
PageRank
Slack Dylan140.42
Hilgard Sophie240.42
Jia Emily340.42
Sameer Singh4106071.63
Himabindu Lakkaraju523218.12