Fooling LIME and SHAP

TLDR

Machine‑learning black boxes are increasingly used in high‑stakes domains such as healthcare and criminal justice, prompting a need for interpretable explanations to diagnose systematic errors and biases. This study demonstrates that post‑hoc explanation methods that rely on input perturbations, like LIME and SHAP, are unreliable. The authors introduce a scaffolding technique that lets an adversary craft arbitrary explanations, masking a classifier’s bias while preserving its biased predictions. Evaluation on multiple real‑world datasets, including COMPAS, shows that highly biased classifiers can easily fool LIME and SHAP into producing innocuous explanations that conceal their underlying racism.

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.