LOCALLY INVARIANT EXPLANATIONS: TOWARDS STABLE AND UNIDIRECTIONAL EXPLANATIONS THROUGH LOCAL INVARIANT LEARNING

Abstract

Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity explanations that are also stable and intuitive. In this work, we provide a novel perspective by proposing a model agnostic local explanation method inspired by the invariant risk minimization (IRM) principle -originally proposed for (global) out-of-distribution generalization -to provide such high fidelity explanations that are also stable and unidirectional across nearby examples. Our method is based on a game theoretic formulation where we theoretically show that our approach has a strong tendency to eliminate features where the gradient of the black-box function abruptly changes sign in the locality of the example we want to explain, while in other cases it is more careful and will choose a more conservative (feature) attribution, a behavior which can be highly desirable for recourse. Empirically, we show on tabular, image and text data that the quality of our explanations with neighborhoods formed using random perturbations are much better than LIME and in some cases even comparable to other methods that use realistic neighbors sampled from the data manifold. This is desirable given that learning a manifold to either create realistic neighbors or to project explanations is typically expensive or may even be impossible. Moreover, our algorithm is simple and efficient to train, and can ascertain stable input features for local decisions of a black-box without access to side information such as a (partial) causal graph as has been seen in some recent works.

1. INTRODUCTION

Deployment and usage of neural black-box models has significantly grown in industry over the last few years creating the need for new tools to help users understand and trust models (Gunning, 2017) . Even well-studied application domains such as image recognition require some form of prediction understanding in order for the user to incorporate the model into any important decisions (Simonyan et al., 2013; Lapuschkin et al., 2016) . An example of this could be a doctor who is given a cancer diagnosis based on an image scan. Since the doctor holds responsibility for the final diagnosis, the model must provide sufficient reason for its prediction. Even new text categorization tasks (Feng et al., 2018) are becoming important with the growing need for social media companies to provide better monitoring of public content. Twitter recently began monitoring tweets related to COVID-19 in order to label tweets containing misleading information, disputed claims, or unverified claims (Roth & Pickles, 2020). Laws will likely emerge requiring explanations for why red flags were or were not raised in many examples. In fact, the General Data Protection and Regulation (GDPR) (Yannella & Kagan, 2018) act passed in Europe already requires automated systems that make decisions affecting humans to be able to explain them. Given this acute need, a number of methods have been proposed to explain local decisions (i.e. example specific decisions) of classifiers (Ribeiro et al., 2016; Lundberg & Lee, 2017; Simonyan et al., 2013; Lapuschkin et al., 2016; Dhurandhar et al., 2018a) . Locally interpretable model-agnostic explanations (LIME) is arguably the most well-known local explanation method that requires only query (or black-box) access to the model. Although LIME is a popular method, it is known to be sensitive to certain design choices such as 1

