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 solution. These objectives capture the desired properties of recourses but do not account for individual user preferences that should be the primary objective. For instance, if a user prefers to change features f 1 and f 2 , then providing them with recourses that change undesirable features make them infeasible even if they are proximal, sparse, and diverse. To address this, some recourse methods define a single cost function that is shared by all the users. A cost function C(f, i, j) denotes the cost of changing a feature f from value i to j. They optimize and evaluate for low-cost solutions under this function (Ustun et al., 2019; Rawal & Lakkaraju, 2020; Karimi et al., 2020c; d; Cui et al., 2015) . We question this assumption and argue for the importance of user-specific cost functions as a shared cost function is likely to poorly represent different users in a diverse population. Hence, these indirect objectives and global cost functions might be necessary but are not sufficient for a feasible recourse. 

In this work, we propose a novel framework, INSPIRE (INdividual uSer Preferences

In RecoursE), that incorporates individual user preference via user-specific cost function to generating algorithmic recourse. INSPIRE provides each user with a recourse set that contains multiple cf options such that there is at least one feasible solution adhering to the user's personal feature preference (if possible). As noted by Rawal & Lakkaraju (2020) , in most cases it is difficult for users to specify their exact feature preferences or cost functions. INSPIRE solves this issue by focusing and improving upon four major components -(1) the procedure to formalize and define individual user preferences via user-specific cost functions, (2) the recourse objective function, (3) the optimization algorithm, and (4) the evaluation procedure. Next, we propose a novel objective function, Expected Minimum Cost (EMC) that approximately optimizes for the cost incurred by the user under their cost function (which is unknown). To do this, (1) we build on Ustun et al. (2019) to propose three distributions over cost functions, D lin , D perc , and D mix , that represent diverse user preferences in a population. These distributions are based on linear and percentile changes in the feature values ( §3.1). ( 2) Next, we compute the expected minimum cost of the generated cfs with respect to multiple sampled cost functions from one of the proposed distributions ( §3.2). In order to efficiently optimize for EMC, we propose a discrete optimization method, Cost-Optimized Local Search (COLS) ( §3.3). COLS guarantees a monotonic reduction in EMC of the recourse set, leading to large empirical reductions in the user-incurred cost. Note that, the EMC objective encourages diversity in the solution set with respect to the diverse feature preferences a user might possess by ensuring that each cf is a good cf under some particular cluster of cost functions from the sampling distribution. Hence, if the user's ground-truth cost function is well represented by any of the clusters, then we will have some counterfactual that is feasible (actionable and low-cost) under their cost function (shown in Figure 1 ). To evaluate the effectiveness of EMC and COLS, we run experiments on two popular real-world datasets: Adult-Income (Dua & Graff, 2017) and COMPAS (Larson et al., 2016) . We compare our method with multiple strong baselines methods like DICE (Mothilal et al., 2020 ), FACE (Poyiadzi et al., 2020) , and Actionable Recourse (AR) (Ustun et al., 2019) . We evaluate these methods on existing metrics from the literature like diversity, proximity, sparsity, and validity ( §4.1) along with our novel cost-based evaluation framework ( §3.4) and a human evaluation. In particular, we define the fraction of satisfied users based on whether their cost of recourse is below a certain satisfiability threshold k. We also report coverage, which is the fraction of users with at least one actionable recourse (Rawal & Lakkaraju, 2020) . Using simulated user cost functions, we show that our method satisfies up to 25.89% more users than strong baseline methods while covering up to 22.35% more users across datasets. Furthermore, our human evaluation shows that the recourses generated by our



Our code is uploaded as supplementary material.



Figure1: Diagram showing the intuition behind the Expected Minimum Cost Objective. This figure represents an abstract cost function space where squares denote cost function samples that are the same color if they are similar and form a cluster. We aim to find a recourse set where each cf (here, {s1, s2, s3}) does well under a particular cluster of cost functions. The shaded big circles each represent a single cf si that caters to the enclosed cost functions. Here the user's hidden groundtruth cost function (grey circle) is served well by s1.

