BETTER SAMPLING IN EXPLANATION METHODS CAN PREVENT DIESELGATE-LIKE DECEPTION

Abstract

Machine learning models are used in many sensitive areas where, besides predictive accuracy, their comprehensibility is also important. Interpretability of prediction models is necessary to determine their biases and causes of errors and is a prerequisite for users' confidence. For complex state-of-the-art black-box models, post-hoc model-independent explanation techniques are an established solution. Popular and effective techniques, such as IME, LIME, and SHAP, use perturbation of instance features to explain individual predictions. Recently, Slack et al. ( 2020) put their robustness into question by showing that their outcomes can be manipulated due to poor perturbation sampling employed. This weakness would allow dieselgate type cheating of owners of sensitive models who could deceive inspection and hide potentially unethical or illegal biases existing in their predictive models. This could undermine public trust in machine learning models and give rise to legal restrictions on their use. We show that better sampling in these explanation methods prevents malicious manipulations. The proposed sampling uses data generators that learn the training set distribution and generate new perturbation instances much more similar to the training set. We show that the improved sampling increases the LIME and SHAP's robustness, while the previously untested method IME is already the most robust of all.

1. INTRODUCTION

Machine learning models are used in many areas where besides predictive performance, their comprehensibility is also important, e.g., in healthcare, legal domain, banking, insurance, consultancy, etc. Users in those areas often do not trust a machine learning model if they do not understand why it made a given decision. Some models, such as decision trees, linear regression, and naïve Bayes, are intrinsically easier to understand due to the simple representation used. However, complex models, mostly used in practice due to better accuracy, are incomprehensible and behave like black boxes, e.g., neural networks, support vector machines, random forests, and boosting. For these models, the area of explainable artificial intelligence (XAI) has developed post-hoc explanation methods that are model-independent and determine the importance of each feature for the predicted outcome. Frequently used methods of this type are IME ( Štrumbelj & Kononenko, 2013) , LIME (Ribeiro et al., 2016) , and SHAP (Lundberg & Lee, 2017). To determine the features' importance, these methods use perturbation sampling. Slack et al. (2020) recently noticed that the data distribution obtained in this way is significantly different from the original distribution of the training data as we illustrate in Figure 1a . They showed that this can be a serious weakness of these methods. The possibility to manipulate the post-hoc explanation methods is a critical problem for the ML community, as the reliability and robustness of explanation methods are essential for their use and public acceptance. These methods are used to interpret otherwise black-box models, help in debugging models, and reveal models' biases, thereby establishing trust in their behavior. Non-robust explanation methods that can be manipulated can lead to catastrophic consequences, as explanations do not detect racist, sexist, or otherwise biased models if the model owner wants to hide these biases. This would enable dieselgate-like cheating where owners of sensitive prediction models could hide the socially, morally, or legally unacceptable biases present in their models. As the schema of the attack on explanation methods on Figure 1b shows,

