INSPIRE: A FRAMEWORK FOR INTEGRATING INDIVIDUAL USER PREFERENCES IN RECOURSE

Abstract

Most recourse generation approaches optimize for indirect distance-based metrics like diversity, proximity, and sparsity, or a shared cost function across all users to generate recourse. The latter is an unrealistic assumption because users can have diverse feature preferences which they might be willing to act upon and any changes to any undesirable feature might lead to an impractical recourse. In this work, we propose a novel framework to incorporate the individuality of users in both recourse generation and evaluation procedure by focusing on the cost incurred by a user when opting for a recourse. To achieve this, we first propose an objective function, Expected Minimum Cost (EMC) that is based on two key ideas: (1) the user should be comfortable adopting at least one solution when presented with multiple options, and (2) we can approximately optimize for users' satisfaction even when their true cost functions (i.e., costs associated with feature changes) are unknown. EMC samples multiple plausible cost functions based on diverse feature preferences in the population and then finds a recourse set with one good solution for each category of user preferences. We optimize EMC with a novel discrete optimization algorithm, Cost-Optimized Local Search (COLS), that is guaranteed to improve the quality of the recourse set over iterations. Our evaluation framework computes the fraction of satisfied users by simulating each user's cost function and then computing the incurred cost for the provided recourse set. Experimental evaluation on popular real-world datasets demonstrates that our method satisfies up to 25.9% more users compared to strong baselines. Moreover, human evaluation shows that our recourses are preferred more than twice as often as the strongest baseline. 1

1. INTRODUCTION

Over the past few years, ML models have been increasingly deployed to make critical decisions related to loan approval (Siddiqi, 2012) , allocation of public resources (Chouldechova et al., 2018) , and hiring decisions (Ajunwa et al., 2016) . These decisions have real-life consequences for the involved users. As a result, there is a growing emphasis on explaining these models' decisions (Poulin et al., 2006; Ribeiro et al., 2018) and providing recourse for unfavorable decisions (Voigt & dem Bussche, 2018) . A recourse is an actionable plan that allows a user to change the decision of a deployed model to a desired alternative (Wachter et al., 2017) . Recourses are often presented to users as a set of counterfactuals (cfs), where each cf details the changes to the user's state vector (i.e., their feature vector). Recourses are desired to be actionable, and feasible. Actionable means that only features which can be changed by the user are requested to be changed. A recourse is feasible if it is easy for the user to adopt, in other words, it is actionable and has a low cost for the user. To achieve these objectives, prior work used feature distance-based objectives like proximity, sparsity, and feature diversity. For instance, Mothilal et al. (2020) and Wachter et al. (2017) encourage proximity by minimizing the distance between the user's state vector and the counterfactuals (cfs) with the assumption that proximal cfs are easier to adopt. Whereas, sparsity quantifies the number of features that require modification to implement a recourse (Mothilal et al., 2020) . In contrast to these, feature diversity (Mothilal et al., 2020; Cheng et al., 2021) provides a user with multiple cfs that change diverse subsets of features assuming that users are more likely to find at least one feasible 1 Our code is uploaded as supplementary material. 1

