ROCOURSENET: ROBUST TRAINING OF A PREDICTION AWARE RECOURSE MODEL

Abstract

Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive) case to individuals who are adversely impacted by predicted outcomes. Existing CF explanation methods generate recourses under the assumption that the underlying target ML model remains stationary over time. However, due to commonly occurring distributional shifts in training data, ML models constantly get updated in practice, which might render previously generated recourses invalid and diminish end-users trust in our algorithmic framework. To address this problem, we propose RoCourseNet, a training framework that jointly optimizes for predictions and recourses that are robust to future data shifts. We have three main contributions: (i) We propose a novel virtual data shift (VDS) algorithm to find worst-case shifted ML models by explicitly considering the worst-case data shift in the training dataset. (ii) We leverage adversarial training to solve a novel tri-level optimization problem inside RoCourseNet, which simultaneously generates predictions and corresponding robust recourses. (iii) Finally, we evaluate RoCourseNet's performance on three real-world datasets and show that RoCourseNet outperforms state-of-the-art baselines by ∼10% in generating robust CF explanations.

1. INTRODUCTION

To explain the prediction made by a Machine Learning (ML) model on data point x, counterfactual (CF) explanation methods find a new counterfactual example x cf , which is similar to x but gets a different/opposite prediction from the ML model. CF explanations (Wachter et al., 2017; Karimi et al., 2020; Verma et al., 2020) are often preferred by end-users as they provide actionable recoursefoot_0 to individuals who are negatively impacted by algorithm-mediated decisions. For example, CF explanation techniques can provide recourse for impoverished loan applicants whose loans have been denied by a bank's ML algorithm. Most CF explanation techniques assume that the underlying ML model is stationary and does not change over time (Barocas et al., 2020) . However, in practice, ML models are often updated regularly when new data is available to improve predictive accuracy on the new shifted data distribution. This shifted ML model might render previously recommended recourses ineffective (Rawal et al., 2020) , and in turn, diminish end users' trust towards our system. For example, when providing a recourse to a loan applicant who was denied a loan by the bank's ML algorithm, it is critical to approve re-applications that fully follow recourse recommendations, even if the bank updates their ML model in the meantime. This necessitates the development of robust algorithms that can generate recourses which remain effective (or valid) for an end-user in the face of ML models being frequently updated. Figure 1 illustrates this challenge of generating robust recourses. Limitations of Prior Work. To our knowledge, only two studies (Upadhyay et al., 2021; Nguyen et al., 2022) propose algorithmic methods to generate robust recourses. Unfortunately, both these studies suffer from two major limitations. First, both methods are based on strong modeling assumptions which degrades their effectiveness at finding robust recourses (as we show in Section 4). For example, Upadhyay et al. (2021) assume that the ML model's decision boundary can be



Note that counterfactual explanation(Wachter et al., 2017) and algorithmic recourse(Ustun et al., 2019) are closely related(Verma et al., 2020; Stepin et al., 2021). Hence, we use these terms interchangeably.1

