HARE: Human-in-the-Loop Algorithmic Recourse
Authors: Sai Srinivas Kancheti
Rahul Vigneswaran
Bamdev Mishra
Vineeth N. Balasubramanian
Venue: Accepted by TMLR ()

Abstract: Machine learning models are seeing increasing use as decision making systems in domains such as education, finance and healthcare. It is desirable that these models are trustworthy to the end-user, by ensuring fairness, transparency and reliability of decisions. In this work, we consider a key aspect of responsible and transparent AI models -- actionable explanations, viz. the ability of such models to provide recourse to end users adversely affected by their decisions. While algorithmic recourse has seen a variety of efforts in recent years, there have been very few efforts on exploring personalized recourse for a given user. Two users with the same feature profile may prefer vastly different recourses. The limited work in this direction hitherto rely on one-time feature preferences provided by a user. Instead, we present a human-in-the-loop formulation of algorithmic recourse that can incorporate both relative and absolute human feedback for a given test instance. We show that our formulation can extend any existing recourse generating method, enabling the generation of recourses that are satisfactory to the user. We perform experiments on 3 benchmark datasets on top of 6 popular baseline recourse methods where we observe that our framework performs significantly better on simulated user preferences.

Published in Transactions on Machine Learning Research (03/2025)
HARE: Human-in-the-Logg, Algorithmic Recourse
Sai Srinivas Kancheti
rs2 matt:at0010@@hh....oo
Indian Institute of Technology Hyderabad, India
Rahul Vigneswaran'
cs cs23mtcchh220020iiu oc. oc.in
Indian Institute of Technology Hyderabad, India
Bamdev Mishra
bemdeom@@mierosoof com
Microsoft, India
Vineeth N Balasubramanian
vineethnb @iith
Indian Instätute of Technology Hyderobad, India
Reviewed on OpenReview: https: // openreview. net/ formm? id= 56EBgl CFum
Abstract
Machine learning models are seeing increasing use as decision making systems in domains such as education, finance and healthcare. It is desirable that these models are trustworthy to the end-user, by ensuring fairness, transparency and reliability of decisions. In this work, consider a key aspect of responsible and transparent AT models actionable explanations, vdz. the ability of such models to provide recourse to end users adversely affected by their decisions. While algorithmie recourse has seen variety of efforts in recent years, there have been very few efforts on exploring personalized recourse for a given user. Two users with the same feature profile may prefer vastly different recourses. The limited work in this direction hitherto rely on one-time feature preferences provided by a user. Instead, we present a comaan-n--the-ooee formulation of algorithmic recourse that can incorporate both relative and absolute human feedback for a given test instance. We show that our formulation can extend any existing recourse generating method, cnabling the generation of recourses That are satisfactory to the user. We perform experiments on 3 benchmark datasets on top of 6 popular baseline recourse methods where we observe that our framework performs significantly better on simulated user preferences.
1 Introduction
Machine Learning (ML) models are being increasingly utilized as decision-making tools in domains of signifi- cant human impact including recruitment, law, and education. Regulations and practical requirements have made model explanations a key requirement of modern ML systems for such application domains. While post-hoc model explanations provide onc-way view into the modcl's dccision-making for a prodiction, many applications can benefit. from the availability of actionable explanations that allow user to take specific actions to improve their outcome. Algorithmic recourse (Karimi et al. 2022 Verma al.) 2020) refers to the domain of methods that offer actionable recommendations to individuals who have been adversely affected by blx decision making systems. The recommendations are usually offered as counterfactual explana- tions, which describe how a given user's feature profile should have appeared to be classified positively. Since the recommendations are meant to be implemented by the end user, we require the generated counterfactual explanations to be actionable and, idcally, to have low exccution cost. This is accomplished by answering the counterfactual question "Given that the decision was y for inpul Fy, what would the decision be if the imput had been Tef instead?*. Here, Tef represents the counterfactual point, showing how the features would need to change from 2 to yield the desired outcome. However, not all counterfactuals are useful our aim is to generate realistic and feasible counterfactuals that individuals can effectively act upon.
Existing algorithms that generate recourses do so by solving a minimum cost optimization problem for each individual Karimi et al. 2022). However, the notion of cost in many application domains can be subjective,
Equal Contribution

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Preferences over features
Preferences over Counterfactuals
Income
income
user preference: income > credit_scomm score
X feedback
Income=100
credit score
credit score=20
credit
Income=100 vredit_score=22
150
_ score=20
hccom=115
credit score=21
Income=110
credit credit/score=25 Scorem
Income=130
cradit_ score=26
+ region
fevv region
Figure 1: Personalized algorithmic recourse provides a pathway to consider individual user preferences algorithmic recourse. The limited efforts so far [Toni et al. (2024); Yetukuri et al.) (2023) rely on feature- level user preferences. The above figure illustrates the benefits of instead considering user preferences over counterfactual samples. Consider the decision boundary (in blue) of a classifier over two features income and credūt score. The right subfigure shows three candidate recourses over which user feedback is taken (green indicates preferred recourse). Comparing plausible counterfactuals allows the user to make a holistic judgement. Left subfigure shows the consequence of providing feature preferences a priori (the user here prefers changing income over credit score). For this decision boundary, such a preference can admit a profile of income 150, credit score = 20, which clearly sub-optinal. Although the user may begin with an implicit preference for changing income, exposure counterfactusl choices reveals a more optimal preference.
with different individuals having differing notions of cost (for c.g., User A may incur a certain cost in increasing their credit score towards loan approval, while User B may have a different set of challenges cost for similar credit score increase). The difficulty of taking actions to modify their underlying features varies from person to person in a complex, possibly non-deterministic manner. Existing approaches tackle this problem by generating a set of diverse recourses giving greater choice to the user (Mothilal et al, 2019) or by asking individuals for preferences over specific features (Yetukuri et al. 2023 Singh et al. (003a)). We propose instead to view the process of generating a recourse as a two-way interaction between the individual and the model. An interactive formulation allows for finer preference elicitation, with simpler options for the user at each step, which elicits lower cognitive burden. Better knowledge of individual preferences allows to find cheaper recourses for the specific individual, and discourages strategic behavior including gaming.
Many recourse generating algorithms use a fixed cost function for all individuals (Laugel et al. 2017 Poyiadzi et al. 2019 Pawelczyk et al.) 2020 Antoran et al., 2021). This is not practical, since different individuals may have different notions of difficulty and actionability, which is not captured by a static cost function. To account for this, we propose to learn a metric for every individual based on their preferences. We present an iterative approach to interactively generate recourses. In each iteration, the individual is presented with small set of candidate recourses and is queried about how suitable/ suitable/umsuitabble /unsuitable they arc. This feedback is then used to learn personalized distance metric for the individual. Based this, new set of recourses are generated which have a low cost according to the user's personalized metric. This iterative process is repeated until the user finds a satisfactory recourse among the generated candidates. In each iteration, we utilize preference information to learn a better metric for the individual.
Our approach differs from existing methods in a few ways: (i) compared to many existing methods Karimi et al. (2022) which do not consider user's preference, we provide a two-way interactive approach for personalized algorithmic recourse; (ii) when compared to few recent methods Toni et al. 2024h: ) Yetukuri et al. 2023); Singh al. (202aa) that consider such personalization of recourses, we elicit preferences over counterfactuals rather than features. While there has been recent effort (Toni et al., 2024) that also allows interaction, they collect preferences over sequential interventions to update their estimate of the cost of interactions between features; we instead go beyond feature-wise preferences to directly query individuals on their preferred counterfactuals. As shown in Figure [ this approach allows individuals to make judgments in the context of complete alternative outcomes, rather than isolated features changes. Instead of proposing directives to user, we present feasible counterfactual (or set of them) and allow the user decide
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how they'd like to achieve the counterfactual based on their choice. We empirically study our proposed approach on 3 real-world datasets using 6 base recourse generation methods and 2 different machine learning models, and observe that it generates more personalized recourses than existing efforts. Our approach HARE (Human-in-the-loop Algorithmic REcourse) is a simple, modular, and casily generalizable framework for generating personalized recourses. A key challenge in personalized recourse is that the form and nature of a user's cost function are often difficult to model explicitly. Unlike existing methods such as PEAR (Toni et al.] 2024), which assume a structured cost function parametrized by a directed acyclic graph (DAG), HARE makes no assumptions about the underlying cost and instead learns user preferences directly from counterfactual choices. This approach provides greater control to the end user, allowing them to select their preferred recourse rather than relying on inferred intervention sequences. Furthermore, unlike PEAR, which is designed around a specific recourse generalor (FARE (Toni et al. 2023)), HARE is fully modular and can be applied on top of any existing recourse generation method, making it applicable across different applications. A detailed comparison with PEAR is provided in Appendix $ A Considering the limited efforts so far, we also propose an evaluation scheme to measure user satisfaction by simulating ground-truth recourse. Our experimental evaluation shows that HARE generates recourses that significantly outperform base recourse generation methods in terms of acceptance by end-users. Our code is publicly available
2 Related Work
Algorithmic Recoursc. Methods to generate actionable counterfactual explanations, a problem called algorithmic recourse, have largely emerged in the last, 4-5 years. A survey of existing methods has been presented in (Kaimi et al. 2022. Verma et al. 2020). The most common family of methods find a recourse by minimizing a fixed distance function, also known as cost function, between the factual point and the recourse. These methods vary in the choice of cost functions employed as well as in the choice of constraints employed. [Ustun et. al.] (2018) provided an Integer Programming formulation to find recourses for linear models. Wachter et al. (2017) proposed to use gradient descent to find recourses that minimize the Euclidean distance to the falt point. Other methods such as Laugel et al. (2017); Mothilal et al. (2019); Poyiadzi et al. (2019): ; Paawllzyk et al. (2200): Antoran et al. (2021) added other constraints such as diversity, closeness to the data manifold and classifier uncertainty. None of these methods however considered the preferences of the end user seeking recourse. Lack of aligument with a user's actionability or preference may lead to adverse outcomes such as users trying to game the system Hardt et al. (2015).
Personalized Algorithmic Recourse. While most initial recourse methods assumed fixed cost function that is optimized for every individual, such methods cannot account for personal preferences and may lead to recourses which are not actionable or of low cost of the user. To address this, Mothilal et al.) (2019) initially proposed to generate a set of diverse recourses to increase the probability that a desirable recourse is found for the user. However there is no guarantee that a preferred recourse belongs to the generated set. In more recent work, Yetukuri et al. (2023); Singh el. al.] (2023a) proposed to incorporate user preferences over features, either as a vector of numerical preference scores or as a ranking among features. While this offers a pathway to personalized recourse, eliciting feature-level preferences can place a high cognitive burden on a user, especially when numerical score is required. Furthermore, these algorithms are susceptible to preference misspecification, and do not allow for post-hoc correction. HIP-CORE AAbate et al. (2024) is a recent human-in-the-loop approach that models user preferences using probability densities over counterfactuals. HIP-CORE solves a multi-object problem which aims to generate a set of recourses that satisfy various metrics such as sparsity, diversity and user preference. We differ from HIP-CORE in many important ways -i) HARE can build on top of any recourse generator bascline to generate personalized recourses; ii) HIP- CORE elicits a numerical piee-prefeeence for every counterfactual leading to a huge cognitive burden for the user (they ask over 300 numerical preferences per user). In contrast, we ask small number of comparison queries (controlled via budget B= 30), making our approach practical; iii) HIP-CORE models preferences via a separate density function for each feature, whose parameters are learned using preferences. In contrast, we work directly with the user's preferred counterfactual; iv) Actionability constraints cannot be encoded into HIP- CORE'S optimization; and finally v) HIP-CORE does not compare against many baselines such as Wachter et al. (017)) Laugel et al. (2017): Poyiadzi et al. (2019h. PEAR Toni al. (2024) recently presented
https://gittubb httt:://ggihubb..om//aa//t//// htgp:/tht...mm///uuu/iieeeaaa//a
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interactive approach to generate recourses, where the user selects from set of feature-level interventions that maximize information gain. Different from these efforts, we instead propose: a methodology to allow the user to select and work with counterfactuals directly, instead of dealing with features separately. In contrast with Toni et al. (2024). our approach does not require any modeling of the interactions between features, which may or may not align with the user's preferences. While we share the objectives of PEAR, our goal in proposing HARE is to have a simple, modular and casily generalizable approach for generating personalized recourses. It is often non-trivial to associate form and nature to a user's cost function. HARE thus makes assumptions on the user's cost, and relies only on the counterfactual choices made by the interacting user. HARE can work on top of any existing recourse generator, in contrast to PEAR which builds on top of FARE (Toni et al. 2023), and its empirical results provide strong evidence to show that such simple systems can work in practice. A detailed discussion of the differences wet the two approaches in made in $ A of the Appendix. We further discuss the benefits of presenting choices over direct counterfactuals in contrast to featuro-level preferences in $3.4
Learning from Human Feedback. Preference elicitation methods aim to learn human preferences from data, and have had an independent history dating back to (Bradley & Terry 1952 Traditional approaches leveraging foedback involve learning from logged data et al. (2023): ; Ma et al. [2023] or assume a human choice oracle Mozannar et al. (2022). Algorithmic recourse is essentially intended to be an individualistic paradigm (Venkatasubramanian & Alfano, 2020), even more in high-stakes scenarios. Thus, for a new user, it is usually not possible to find data pertinent to the individual. Our proposed human-in-the-llop formulation allows for learning preferences over feasible recourses for each individual akin to having a dialog. It is well-known that asking users for quantitative preferences places cognitive burden that can lead to noisy or incorrect choicos Luce (1979; ; Keeney ct al. (1977): Feffer et al. (2023). Our proposed algorithm
thus utilizes pairwise queries Tesauro 1988) Yue et al. (2012) Joachims et al. (0007), where the user selects
the best of two alternatives.
We now present our methodology.
3 HARE: Methodology
In this section, present HARE (Human-in-the-loop Algorithmic REcourse), framework to generate personalized recourses with human-in-the-loop. Our framework is way to generate personalized recourses for every user by interactively soliciting their preferences. Our human-in-the-loop formulation solicits human feedback over a set of exploratory recourses, which we term candidate recourses. Asking for preferences over a pair of recoursos more feasible, and allows the user to even consider interactions between features. This feedback is incorporated by the algorithm to generate refined candidates in the next iteration. The process ends when the user is satisfied with some recourse(s) from the candidate set. We describe each component of this proposed framework in the remainder of this section; a detailed algorithm of HARE is provided in $ 3.44 HARE can work with any existing recourse- crourse-generating algorithm to integrate this human feedback component and generate personalized recourses. We begin by discussing the preliminaries.
3.1 Notations and Preliminaries
Consider an end user u E U with a feature vector s(u) E X, and a fixed binary classifier h X + {+]}. We consider classifiers of the form h(x)= = (g)(f(x)) where sgn(-) is the sign function and f : X' + R is a scoring function. WLOG, we consider +1 the desired class, and I = {a E X h(x) = +1} the region in feature space that is positively classified. We assume z(u) $ I (since recourse is sought only when the desired class is not obtained model prediction). Let A(u)= S X denote the set of feature values that the user can attain by making actionable changes to their feature vector. A recourse generator M: X + (X)) where (X') is the set of all finite subsets of X, maps a uscr's fcature vector to a finite set of recourses. The generated recourses are considered as valid counterfactuals and as being actionable by the user, i.e Vr E M(x(w)), h(r) = +1 and r E A(u). Our framework HARE can be integrated into any existing recourse generation method M to generate personalized recourses. We describe a few salient recourse generation methods in Appendix $ C HARE uses M to find a initial bascline recourso(s), which is used to sample exploratory candidate recourses, described below.
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3.2 Generation of Candidate Recourses
To elicit preferences of user u, we require set of recourses over which leedback can be provided. To this end, we sample candidate recourses around baseline recourse ro(u), which is generated using any existing recourse generation method. We present an algorithm ActionableSampling to sample k actionable candidate recourses around a baseline recourse To ro(u) using gradient descent. The set of personalized candidate recourses C(u) should be diverse enough to capture possible variations in user's preference, but not too large as to be cognitively burdensome to query the user. The output of ActionableSampling is hence a set of diverse candidate recourses which are valid w.r.t user and base classifier g, i.e. they are actionable for and are classified positively by g.
We now herein describe Algorithm 1 We start with h random actionable ex- ploratory direction vectors {PL..... Pah: Pr E X, which are added to ro to gen- erate the candidate recourses. Here, k is decided by the query budget of the user, discussed in 3.4 We require the exploratory directions to be as di- verse as possible, while remaining action- able to the user. To make the directions as diverse as possible, we follow Mettes et al. (2019) and minimize the mean max- imum similarity between the directions. Let P = IPI,..., Pk), P E | be the matrix with directions as columns. The similarity matrix is PT P P 2 II, where we subtract the identity to remove self- similarity. Denoting similarity loss by (P)), we have:
Algorithm 1: ActionableSampling
Input : Baseline recourse ro(u), Classifier g, Number of candidates k; Actionability set A(u), Gradient descent itcrations n, Hyp. X
Output Initialize:
Candidate recourses C(u)
A random actionable directions P1= (Pi,...,, Pk. such that Ps E A(u)Vi
for i = 1,2, ........P. do
[P,),) <- mean(row max P. P. - -- I))
3
[2(P:) = SoaaryCCoossEttroppe (roo())ggg()g)))) + P.),
P/ f- P. - (AL((P)) I.2(P.)
P++1 + {argmin |pp pjll pj E P/}
pEA(u)
P+1 + { p
ITll p P.+1}
7 (*( + P++1
C(u) t (ro(u) tn-plpe
9 return C(u)
(P))
1 K
Σ (k)
max PT. P-2-11
]E(K]
Minimizing Eq generates directions that are away from each other, and hence diverse. The candidate recourses are generated from the exploratory directions as rn(u) + P, where y is magnitude hyperpa- rameter. To ensure that sampled candidate recourses are valid w.rt to the base classifier g, we add a binary cross entropy loss (P):
(P( = BiinarCrossEntroopltuue
1),
(2)
Minimizing this loss ensures that the base classifier assigns label of +1 to the candidate recourses. Subse- quently, as shown in Algorithm [ we perform n steps of Projected Gradient descent over X [P) + Iz((P) to yield valid, diverse directions p* After each gradient descent step, actionability ensured by pro- jecting the directions onto the set A(u The output set of candidate counterfactuals is given by C(u) (ro(u)++)))) 7p PepeP (*)))
In the candidate generation process, features identified immutable for user (when such information is available) remain unchanged, ensuring all candidates are actionable. While our method does not rely on immutabiliyy inputs from users, it can seamlessly incorporate them when provided.
3.2.1 Calibrating Candidate Recourses
While set of candidate recourses are now available, it important that these candidate recourses are also those of minimal cost to the end user. To achieve this, we calibrate each candidate recourse by identifying sample that is in the direction of c, but closest to the decision boundary from s(u).
We abuse notation slightly and use A(u) also indicate the set of actionable feature changes.
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We treat this as root-finding problem fx) 0 for the classifier scoring function f which captures the point on the line joining r(u) and that is closest to the decision boundary.
Algorithm 2: Boundary Pinttearc
Input
User feature sru), Candidate recourse c; Classifier 9.
Tolerance e
Output Recourse on decision boundary
Since f(a(u)) < O and f(c) > (by construction of re- course methods), there is at least one root in [a(u), cl. For classifiers with non-linear decision boundaries, terested in the root closest to We use modified version of the binary search algorithm for this purpose, which we call BoundaryP PonuudaryPoinSSewre... ointSeerch. This variant is summarized in Algorithm Algorithm describbs the procedure to gen- erate the final candidate recourses (u( by using Binary Search on every candidate. The user u can now indicate their preferences over C' (u)); which we describe next.
Initialize: slart < =(u), end while ||start endll2 do mid start + end
are in-
2
3 if g(mid) 0.5 then end mid
start mid 5
else
c' < end
8 return
3.3 Eliciting Feedback over Candidate Recourses
Existing works that consider user feedback in algorithmic recourse require preferences over individual features, where the user either provides a numorical score indicating their preference for changing a feature, or provide a ordering over features. Eliciting such feature-wise preferences may be non-trivial or even infeasible for a user due multiple reasons: (i) Provid- ing feature-wise preferences place high cognitive burden on the user, especially for algorithms that require numeri- cal value of features importance or applications where there are a large number of features; (ii) may be difficult. for a user to fully order features, since ordering implicitly in- volves (d2) pairwise comparisons for d-dimensional data; (iii) It is also possible that there may be many feasible re- courses that satisfy such feature ranking preferences, and the user may want to choose between them; (iv) Care has to be taken to ensure that the algorithm is robust to preference misspecification, which again scales with dimensionality. We thus propose an alternate mechanism, where users simply declare their preferences over candidate recourses themselves. We consider two types of feedback provided by a user u over candidates from C'(w):
Yetukuri et al.
2023 Toni et al.
2024
Algorithm 3: FinalCandidrtRRcoursss
Input
User feature s(u), Classifier g, Initial candidate recourses
C(u), Tolerance c
Output : Final candidate recourses C'(u) Initialize: C'(u) + 0
1 for CEC(u) do
2
d'
3 C'(u) t- u()
return C'(w)
Relative Feedback: The user compares pair of recourses and chooses their preferred recourse. Relative feedback is modeled with Comparative Evaluation (CE) function over pair of recourses r, and ry- Absolute Feedback: Given a counterfactual, the user either accepts it or rejects it. Absolute feedback is modeled with an Indicator Evaluation (IE) given recourse r.
Ti >a rj +1 0 Relative feedback is casier to provide due to its lower cognitive load and reduced chances of the user making erroneous judgement TTssarro (1988); Yue et al. (2012). We hence primarily rely on CE queries to solicit feedback over the set of candidate recourses. We use absolute feedback as a termination criterion, when the user has been exposed to multiple comparisons, and is ready to make final decision.
EE(T) = +1 satisfactory
CE(raTy)
Tt Ku r3
otherwise
3.4 The Overall HARE Framework
Given a calibrated candidate set C'(u), we allow for asking relative and absolute foodback queries the user in our overall framework return personalized recourses. For instance, we begin by asking CE (or IE) random pair of recourses from C' (w().. The winner is then compared to un-visited candidate recourse,

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and the process repeats till the best candidate recourse r" is found. The above process is repeated over iterations, where the r* at the end of an iteration / t-1 is used as the base recourse to compute the candidate recourses in iteration t. Additionally, our approach allows us to provide the user with their preferred recourses within a fixed query budget. Assuming each query (CE or IE) consumes one unit of query budget and an overall user budget B, we allow & candidate recourses in each of iterations of the overall process to stay within the specified budget.
Algorithm f summarizes point of our algorithm by a classifier (i.c. an undosirable prediction, which the user seeks to reverse).
framework's methodology. Without loss of generality, we consider the starting with data sample x(u) that has been unfavorably (negatively) classified
Algorithm 4: HARE
input
User feature æ(u), actionability set A(u), classifier 9, Base Recourse Generator M, user relative and absolute feedback functions C.E,, & EE,, user query budget B, total iterations T, gradient descent iterations n, regularization hyperparam X, tolerance output Preferred recourse r* (")
initialize: ro(u) t M(z(u))
for 1.2..... do ((u)) ActionableSamplingg hasooline__ hawpapppiiiinccciiieeeeeessssss recourse (u), classifier 9, number of_________ candidatts
B Actionabiliiity set = A(w); gd itteratiitn = n,, regularization param = A) feature z(u), classifier = 9, candidate recourses
3 C,(u) FinalCandidateReeousssse
C,(u), tolerance <)
while [((2) do
5
pick re from C((u)
6
(i())= + C((()) (C...r)r)) + (1- (1-CE,((rr)))
7
(u( C.)
8
if (r.(((()) =1 then terminate loop
9
10 r" ("( + (u) 11 return ("(
For each iteration E (I...., T}, the following steps are performed:
1. HARE first generates a baseline recourse ( using any off-the-shelf recourse generator M (or) as the best candidate returned by the previous iteration
2. In Line of Algorithm 4 T diverse initial candidate recourses (u) are generated around (=)) via the ActionableSampling algorithm described in 3.2
3. In Line 3, these initial candidates are calibrated to obtain the final candidate recourses () that are relatively closer the decision boundary of g, via the FhalCanndidateeRecousss algorithm 3
4. In Lines 4-6, described in 3.3 the best candidate (u) is chosen over C((u) by making B comparative evaluations.
5. In Line 7, the best candidate is chosen the baseline recourse for the next iteration
The overall algorithm has a query budget of B. The algorithm terminates when the query budget is exhausted when the user is happy with the generated recourse (Line of Algorithm 4 In practice, there must be two-way dialog between the user and the recourse generator. If the user is unable to accept any of the candidates after many rounds of feedback, the recourse generator must be able to accept suggestions from the user and check if they are valid. For instance, consider the example from Figure [ of the main paper where the user provided the final 3 options (income, coddit__ccrre of (110,25), (130,26), (145,21). Even if the user is unable accept any of these, her exposure to the choices allows her to suggest a possible recourse that works. For instance, she may state that (100,25) is doable, and in this example is valid recourse. Note that the may have apriori implicit preference of changing income over credit score (perhaps

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they want to be future proof), but the exposure to counterfactual choices (which are constrained by the underlying predictive model) can reveal different preferences. Furthermore, HARE is flexible, allowing end users to dictate the kinds of choices available to them. For example, the ActionableSampling algorithm [] ensures that candidates are diverse. It also allows users to specify new actionability constraints on the fly, and to restrict the range of sampling by tweaking 7. For consequential decision making systems (loans, jobs, crime ctc), it in the end user's best interest to achieve a recourse that they can act on, and our method presents a simple and modular framework to the end user, where the user preference can be integrated with any existing recourse generation method.
4 Experiments, Results and Analysis
We comprehensively evaluate HARE, both with number of iterations T 1 and T 1, on 3 real-world benchmark datasets commonly used in carlier similar work. For convenience of reading, we refer to the use of our method when T 1 as HARE and when T > 1 as Multi-HARE. As described earlier, our framework generates personalized recourses by incorporating a human-in-the-loop. and can be applied on top of any base recourse generator. Therefore, we study the effectiveness of our framework across diverse base recourse generation methods. Since recourse can be applied on top of any machine learning model, we perform experiments with two kinds of classífiers: a 3-layer Artificial Neural Network (ANN) and Logistic Regression (LR). Our results demonstrate that HARE generates more personalized recourses compared to any standalone base recourse generator.
4.1 Datasets, Simulating Ground-Trun Recourses & Evaluation Metrics
Datasets. We evaluate on commonly used binary datasets spanning different application domains in- cluding credit worthiness, criminal recidivism, and income prediction, which are popularly used in recourse literature. Adult Income Becker & Kohavi (1996) is binary classification dataset with 13 features (6 con- timuous, 7 categorical) that are used to prodict if anmual income exceeds $50K. We binarize the categorical features, and consider 'age', 'sex', 'Julugl', 'nalive-counrry', 'race', 'velationship' to be immutable features in this work. GiveMeSomeCredit KKggle (2021) is used to predict credit worthiness and consists of 10 contin- uous features, out of which we consider 'age', NumberOOfDeeeddee COMPAS Larson et al. (2016), which is used to predict recidivism risk. It consists of features (4 continous and 3 categorical) out of which 'age', 'race', 'sex' are considered immutable. For all datasets, continuous features are scaled to lic within the range [0, 1] and categorical features are binarized.
to be immutable, Finally we consider
Base Recourse Generators. HARE can be integrated with any existing recourse generation algorithm to generate personalized recourses. We evaluate its performance on 6 popular baseline recourse methods:
Wachter Wachter et al. (2017), Growing Spheres (GS) Laugel et al. (2017), FACE Poyiadzi et al. (2019)
CCHVAE Pawelezyk et al. (2020). CLUE Antoran et al. (2021) and CRUDS [Dwns el al. (2020). These
baselines represent diverse collection of recourse generators which differ widely in their scope and approach. A short description of these bascline methods is given in Appendix [C for completenoss.
Simulating Ground-Truth Recourses. Since real human feedback is unavailable, we simulate ground- truth recourse to approximate a user's preferences. This ground-truth recourse serves two purposes: (i) It provides a reference for simulating user feedback for instance, relative feedback on two candidate recourses is simulated by comparing each of them against the goound-truth, and (ii) It allows us to quantitatively evaluate user satisfaction of generated recourse. Given a user u with feature profile æ(u), the g.t. recourse gt (u) captures the recourse most preferred by the user. Importantly, this preference is not known to the algorithm. We simulate (1)( for u by randomly sampling actionable recourses on the classifter decision boundary. We sample rt at three scales near, where the g.t recourse is close in euclidean distance to the factual point; intermediate and far. This categorization allows us to model users with different preferences -ome may prefer small adjustments across multiple features (near), while others may prefer changing a single feature by a large amount. Additionally, users at the far scale may also represent those willing to incur higher costs for a more robust recourse that remains valid under classifier shifts Upadhyay et al. (2021). We evaluate the performance of personalized recourse methods by measuring how closely the generated recourses align with simulated ground-truths. Note that the only information HARE gets about ret(2) (u) is obtained
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indirectly, through simulated user feedback. Standalone base recourse generators do not incorporate user feedback and have no way of obtaining information about the g.t recourse.
Evaluation Metrics: We propose two new metrics to measure end user satisfaction with personalized recourses- (i) Ground-Trun Prozimity (GTP) measures the L2 (Euclidean) distance between the generated recourse and the simulated g.t recourse. A low GTP value indicates that. the generated recourse is close to the user's ideal solution, and ii) Ground-Truie the vectors from the factual point to the generated recourse and to the g.t recourse. A low value of GTD implies that the generated recourse aligns in direction with the user's preferred changes. These new metrics specifically assess how well the generated recourse matches user's preference.
Dissimilarity (GTD) measures the cosine distance between
We also evaluate on classical recourse metrics such as Success Rale, which measures the fraction of test individuals for which a recourse could be generated; Constraint Violations, which measures the average number of immutable features changed; Redundancy, which measures the average number of unncoessary feature changes; Prozimily, the L2 distance between the factual point and generated recourse; and Sparsily, which measures the number of features modified to generate the recourse. For all metrics except Success rate, lower is better.
4.2 Implementation Details
Classifier. We train and fix a 3-layered Artifical Neural Network (ANN) and a Logistic Regression (LR) model for cach
are generated for 150 fixed individual samples taken from the test-sct. Recourse Generator Baselines. We use recourse generator implementations provided by CARLA Pawel- czyk et al. (002)) for all 6 baselines: Wachter, GS, FACE, CCHVAE, CLUE and CRUDS. Hyperparameters for each baseline are set to values that maximize success rate of recourse generation, shown in Appendix $ SD
HARE. We have total budget of B 30 user queries (across iterations) unless specified otherwise (i.e. if T 5, we allow for 6 queries per iteration in this setting). Since the end user is often motivated achieve positive classification, we believe that this query budget is reasonable. However, for completeness of un- derstanding, we study the efficacy of personalized recourse generation under different user budgets in $ 14 The itcrative variant of our approach, termed Multi-HARE, divides the total query budget into multiple it- erations, allowing for iteralive refinement of the recourse based on user feedback. Unless specified, we choose T= 5 iterations. For ActionableSampling we perform full-batch gradient descent using the Adam [Kingma & Ba (2014) optimizer for n 100 itcrations with a learning-rate 0.1. We set the magnitude hyperparameter y to 1, and the regularization hyperparamcter À to 10. In BoundaryPointSearr the tolerance value e is set to 1e-06. All experimental results are averaged over 5 seeds to ensure robustness. Our code will be made publicly available upon acceptance.
4.3 Results
We now present the results of our experiments, where we evaluate HARE across a variety of settings. Our goal is to assess how effectively HARE generates personalized recourses compared to standalone basc recourse generators. We study five keys aspects - i) Measuring User Satisfaction, where we measure GTP & GTD defined in $ 4.1 to evaluate the degree to which generated recourses align with the simulated ground-truth recourses; ii) Budget Ablation, where we examine how varying the number of user queries affects the quality of generated recourses; iii) HARE Ablation, where we study the impact of iterative refinement on recourse generation; iv) RoundaryPoinlSeene
Ablation, where we evaluate the effect of not performing a BoundaryPointteaach for every candidate recourse; and v) Ground- Truth Recourse Scale Ablation, where we investigate the performance of HARE across three different scales of ground-truth recourses. Finally we report classical recourse metrics as discussed in 4.1 These metrics do not measure the degree of personalization of the generated recourses, but we report them for completeness. These experiments help us understand the various components of HARE and study how it performs under varying conditions.
Measuring User Satisfaction. In Table D we show the results for all six baseline recourse generators across three datasots, evaluated on both ANN and LR classificrs. For cach bascline, we find that HARE and its iterative variant, Multi-HARE, consistently obtain recourses that align more closely with the simulated ground-truths measured by both Ground-Trus Proximity (GTP) and Ground-Trun Truth Dissimilarity (GTD).
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Base
ADULT
COMPAS
CREDIT
Ours
Recourse
GTP
GTD
GTP
GTD
GTP
GTD
Base:
0.93.10.38
0.87
0.39.10.3
0.75.10.30
1.07
0.97
Wachter
+ Random
1.01 #0.35
1.17 10.14
0.39 +0.24
0.87 #0.27
1.21 10.15
1.09 10.1
+ HARE
0.69-+0331
0.09=0.04
0.27=0.26
0.27
0.89
0.37=00.99
+ Multi-HARE
0.58+t0.22
0.08 0.08-00.3
0.27
0.26+0.15
0.90=#0:22
0.38=0.12
Base
0.92::0.39
0.82 0.
0.40=0031
0.80
1.06:
0.06=0.09
GS
+ Random
0.95 =0.35
0.97 +0.35
0.17
+0.29
0.99
+0.24
1.23 =0.14
1.2 10.18
HARE
0.71:10.34
0.10:10.05
0.26 0..222222
0.21
0.89:10.18
0.06
+ Multi-HARE
0.56 L0.27
0.08 10.03
0.25 0.23
0.21 10.10
0.83 0..2110.21
0.30 0.30
Base
1.06:10.36
1.06:10.09
0.42 0.33
0.82:10.28
1.11:10.19
1.09::0.1
ANN
FACE
+ Random
HARE
1.04 10.39
0.77:t0.35
0.24
1.43 10.14
0.44 1.033
0.26
0.88 10.15
0.
0.90=
1.21 10.13
0.42=00.00
1.09 1:0.5
+ Mult--HARE
0.56::0.26
0.08:t0.00
0.25 0.21
0.21
0.83+00.22
0.32 0.00
Base
1.32
0.99
0.42
0.76:
1.13:10.20
1.0120.11
CCHVAB
+ Random
1.10 =0.31
1.29 #0.06
0.15 +0.30
0.91 #0.17
1.20 =0.14
1.03 00.05
HARE
0.83-10.35
0.48=00008
0.29 0.330
0.26
0.91-:0118
0.44=0.07
+ Multi-HARE
0.60 1.029
0.07 10.03
0.29
0.331
0.25 1..14
0.83 L0.21
0.32 10.07
Base
0.95 19
0.71 19
1.22: 18
1.04 10
CLUE
+ Random
1.0.99
10.23
10.14
1.06 10.1
+ HARE
0.32
0.33
0.117
0.49
+ Multi-HARE
0.26=00
0
0.84+00.21
0.33+0000
Base
2.16#0..4
1.12:0.112
1.22
0.70: 00
1.20:40.22
1.01::0.16
Random
1.26 10.23
1,20 10011
0.52 10.36
0.78
1.0.22
1.16 10.13
0.84 1.012
CRUDS
HARE
1.09:
1.00=0.14
0.2050005
0.38 .9
0..7:
0.34
+ Multi-HARE
0.50
0.09 10.02
0.15 0.03
0.15 0.002
0.64
0.004
Base
0.94
0.80
0.25
0.58
0.90
0.73 0.05
+ Random
1.04 =0.31
1.10 #0.11
0.34 +0.14
0.88 +0.38
0.95 =0.14
0,86 10.10
Wachter
+ HARE
0.63:10.27
0.10
0.16 0.16 0.005
0.14
0.72.10.17
0.30
+ Multi-HARE
0.39::0.17
0.07 :0002
0.15=0.mm
0.13+
0.65
0.21 0..02
Base
0.91::0.38
0.72
0.28::0.0
0.89:::030
0.93
0.87 06
GS
+ Random
0.91 =0.32
0.72
10.26
0.32 1:015
0.91
1:0.2
0.98 20.27
0.87 10.26
+ HARE
0.64:10.29
0.12
0.17:
0.15 00007
0.73
0.29 0.001
+ Multi-HARE
0.39::0.21
0.05 00.02
0,16 0.006
0.17
10.18
0.19 0.19::0.06
Base
1.19100.1
1.0310.12
0.007
0.72.10..5
0.96.10.21
0.91 1:010
+ Random
1.13 =0.33
1.06 10.11
0.31 +0.14
0.62 10.16
=0.26 0.95
0.85 20.29
Face
LR
HARE
0.77=20.32
0.30
0.15=0.00
0.14=0.07
0.71
0.31=0.00
+ Multi-HARE
0.43 10.20
0.07 00002
0.15 0.05
0.16:10.12
0.63 1.
0.19 0.00
Base
1.42::0.200
0.96 05
0.31#0009
0.75
0.98: 0.24
0.86 16
CCHVAE
+ Random
1.22 =0.30
0.99 +0.02
0.27 +0.08
0.63 10.17
0.94 #0.28
0.81 00.31
HARE
0.87.10.30
0.50100.3
0.12
0.12 L0.07
0.74-10.21
0.30 10.06
+ Multi-HARE
0.50:10.23
0.09:10.04
0.13=0.00
0.13 0.04
0.63
0.19 0.00
Base
0.89 0.803
0.67 1..13
1.07 10..3
0.87 0.113
CLUE
+ Random
10.10
+0.30
0.94 -0.27
0.80 20.29
+ HARE
0.20=
0 0.09
0.76=+0.20
0.33=0.00
+ Multi-HARE
0.16=0.00
0.17#:0.15 +0.13
0.64
0.20 0.00
Base
2.92:10.45 45
1.22: 12
1.27 0.00
0.70 11
1.04
0.85 14
CRUDS
+ Random
=0.46
#0.13
10.13
+0
=0.21
0.81 10.16
HARE
1.09 1.09.0.36
10114
0.23 0.005
0.38 10.09
0.77-10.19
0.34 10.08
+ Multi-HARE
0.50.10.19
0.09
0.15=0.00
1.002
L..4
0.23
Table 1: Results on Measuring User Satisfaction for the far g.t recourse scale. We show results on three datasets (in columns), and six baseline recourse generators (in rows) for two classifiers, ANN & LR. We do not report numbers for CLUE on Adult as the success rate is too. low For each baseline, we show the impact of incorporating user feedback using HARE and MuREi-HARE. Results bold indicate best numbers, and underline becond-best. We observe that our framework can effectively incorporate user feedback.
We achieve lower GTP and GTD scores, indicating that the generated recourses are both spatially closer to the user's ideal solution and also align better in terms of direction of change. For both methods, we use a total user query budget of B = 30 queries, while Multi-HARE performs recourse refinement by distributing the queries across T = iterations. Multi-HARE generates the most personalized recourses, suggesting that refining recourses across multiple iterations can better capture user preferences. Morcover it presents smaller candidate set at each iteration, making it easier for users to express preferences at each stage, leading to lower cognitive load overall. In the random baseline, pick candidate at random from the final candidate set at every iteration, instead of using the feedback provided by the user. We add this simple bascline to show that actually using user feedback is crucial to obtain good quality recourscs.
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Published in Transactions on Machine Learning Research (03/2025)
4.4 Analysis and Ablation Studies
Study of Budget Choice. In real-world scenarios, increased interaction with end-users typically leads to better personalized recourses. To explore the sensitivity of HARE to the query budget, we conduct an ablation study with different. budget allocations. The results, shown in Table demonstrate that there is a consistent improvement in both GTP and GTD metrics as the budget increases. Notably, the performance gains are significant up to a budget of 30 queries, beyond which improvements platcau. While larger budgets yield more personalized recourses, a budget of around 30-70 queries is optimal and strikes a balance between user satisfaction and cognitive load. In $ B.3 in the Appendix, we study the performance of HARE under even lower budgets.
Base
Budget
ADULT
COMPAS
CREDIT
Recourse
(B)
GTP
GTD 4
GTP
GTD
GTP 4
GTD
Base
0.93+0038
0.87:-0.03
0.39+0.34
0.75=0.30
1004:0.18
0.97
10
0.69-0031
0.10==0.04
0.33++031
0.40=0.21
0.95+=0.18
0.42+00. 14
Wachter
30
0.60-+0.31
0.09
0.27--0.26
0.27=0.17
0.80:10.17
0.37 09
0..3
0.10=00.03
0.24+:0.23
0.21=0.16
0.86+0018
0.32
100
0.67=0033
0,12
0.24:10.25
0.20:10.14
0.85 0..19
0.33:10.04
Base
00.39
0.82 10.09
0.40.10.31
0.80
1.06 1.019
1.0610.00
ANN
10
0.75
0.23::0.07
0.34
0,38 09
0.91-:0.15
0.45 16
GS
30
0..34
0.101
0.26110.22
0.21 0.110
0.89 1.018
0.40
70
0.68-
0.11:10.02
0.25:10.25
0.19 10
0.85:L0.17 17
0.31 4:0.05
100
0.65=0.32
0.10#0.02
0.23+:0.23
0.16 09
0.83 0.11
0.30
Base
1.06::0.96
1.06::0.09
0.42:+0.32
0.82=0.18
1.11::0.19
1.09-:0.17
10
0.81--0.35
0.33
0.34+0.38
0.35=0.10
15.11
0.46+
Face
30
0.77=:0.35
0.24=:0.05
0.26:#0.2
0,21=0.10
0.90::0.18
0.42:
70
0.74
0.20:10.03
0.25:10.25
0.18
0.86 1..17
0.34
100
0.72.10.34
0.000
0.23.10.23
0.17
0.84 00.17
0.31 L.0.22
Table 2: Study of change in budget choice for the for g.t recourse scale. We show results on three datasets (in columns) and three baselines for the ANN classifier. We study the effect of incorporating user feedback using HARE with one ileration across range of query budgets B = 10, 30, 70, 100. The results indicate that budget of around 70 strikes balance between performance and cognitive load. The * indicates the default query budget used in Table 1
Study of Number of Iterations: Multi-HARE. Tn Table we present the results of Multi-HARE across varying number of itcrations (T 1,3,5, 10), showing the impact of iterative recourse refinement when keeping the budget fixed at B = 30. The results indicate that in general, increasing the number of iterations leads to generation of more personalized recourses. However performance tends to plateau or even degrade with more iterations. At higher itcration counts, the candidate set presented to the user at cach round becomes smaller, which can restrict the diversity of recourses available for comparison. This can limit the extent to which user preferences can be incorporated. Across different baselines and datasets, T ===3 & B 30 strikes a balance between performance and user cognitive load, and practical starting point for personalized recourse generation.
Study of Choice of BoundaryPointtSeurch PointSearch. In each candidate to move closer to the classifie'ss 's decision boundary, ensuring that the user's feature changes are smaller and thus casier to carry out. In this section we study the impact of calibrating only the best candidate instead of adjusting the entire candidate set. We solicit user feedback over the un-calibrated candidate set itself, to choose the best candidate. The best candidate. The results presented in Table indicate that calibrating all candidates leads to better user satisfaction. Given that the binary search procedure incurs minimal computational overhead, calibrating all candidate recourses is an effective way of improving user satisfaction.
3.4 we calibrate the initial candidate set by adjusting
algorithm is applied to calibrate only the
Study of Ground-Trun Recourse Scale. As described in $ 4.1 we simulate ground-truth recourses at three scales- - near, intermediate and far, to model users with different preferences. In Table D we present the performance of HARE and Multi-HARE, for the ANN classifier. Both methods perform well across all scales, particularly in the far scenario, which the most challenging in practice due the larger magnitude of feature changes. Notably we observe significant gains in the GTD metric, indicating that our framework
11

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Published in Transactions on Machine Learning Research (03/2025)
Base
ADULT
COMPAS
CREDIT
Iters (T)
Recourse
GTP
GTD
GTP +
GTD 4
GTP
GTD 4
00.31
0.09.10.04
0.27.10.26
0.2710011
0.8911..17
0.37.10.09
Wachter
3
0..23
0.07
0.22:1:018
0.22-00144
0.89--0.19
0.36-L0.09
0.58=0.28
0.08++0.00
0.27+00.26
0.26=0015
0.90+0022
0.38+0.11 12
0.39-0.22 10
0.10::0.03
0.22
0.20
0.87:0.22
0.32
ANN
0.71=0034
0.10
0.20++0222
0.21=:0.10
0.89+
0.40 06
GS
3
0.55 0.65-+0.25
0.10: 02
0.27
0.21
0.86
0.31 11
0.56::0. 0.27
0.08 1:00100.00
0.25
0.21:10.10
0.83
0.30 0.007
10
0.58:.0.2
0.07 1.002
0.28:10.28
0.23
0..17
0.33-10.05
0.77:2033
0.24:10.05
0.26::0.22
0.21 :0.10
0.90
0.42::0.09
Faon
3
0.58 1..55.00.20
0.11:10.03
0.22
0.21 10.08
0.86 0..21
0.34
5"
0.56=0.222 26
0.08++0.00
0.25:t0.24
0.21:
0.83=0.2
0.32
10
0..1-0.28
0.08=0.00 0.003
0.27
0.22=0009
0.86
0.31+00.03
Table 3: Study of change in iterations, Multi-HARE for the for g.l recourse scale. We show results three datasels and three baselines for the ANN classifier. We study the effect of varying the number of iterations in Multi-HARE as T 1,3, 5, 10 with fixed budget of B = 30. The results indicate that iterative refinement generates better personalized recourses. The * indicates the default number of iterations used in Table !
Dataset
Base
Ours
GTP 4
GTD L
Suec. Rate f Con. Vio. 4
Red. 4
Pro. 4
Spa. 4
Base
0.93
0.87.10.03
1.00
0.00.10.00
3.30
0.02:10.00
4.00:10.00
Wachter
HARE
0.69:10.31
0.09+:0.04
1.00+0.00
0.00=10.00
3.41=0.33
0.10+00.04
4.00=:0.00
+ Multi-HARE
0.58:t0.28
0.08
1.00=00.00
0.00=:0.00
3.46=0.11
0.25+:0.12 12
1.00=:0.00
ADULT
Base
0.92
0.82 10.09
1.00
0.00
2.78
0.010:0000
4.00
GS
+ HARE
0.71+0.34
0.10==0.08
1.00# 00
0.00 00
3.43=0.26
0.09 02
4.00+ 00
MAR--HARE
0.56:10.27
0,08
10.03
1.00:00000
0.00
3.43=0.27
0.29
4.00
Base
1003
1.06.10.09
1.0010.00
2.01110.02
2.80
0.20
4.22
Face
HARE
0.77+:0.35
0.24:10.05
1.00=0.00
2.00#:0.01
2.23:10.21
0.09:00.03
6.04
Mult--HREE
0.56::0.26
0.08+0..0
1.00
1.96:0000
2.43=0.35
0.29 15
5.97:0004
Base
0.39
0.75-10030
0.83+ 22
0.00 00
2.02=0.19
0.03-00.02
2.95-10.01
Wachter
HARE
0.27
0.27=:0.17
0.82 22
0.00+00000 0.00
1.67=
0.09 10
3.00 01
+ Multi-HARE
0.27:10.26
0.26 0..15
0.82:50.22 22
0.00
0.09
2.99
COMPAS
Base
0.40=#0.31
0.80==0.10 10
1.00 00
0.00 0.00
1.29
0.04
3.00 0.00
GS
HARE
0.26:10.2
0.21 10.10
1.00::0.00
0.00::0.00
1.81=0.52
0.13::0.12
3.00=:0.00
Multi-AARE
0.25.10.23
0.21 10.10
1.00.10.00
0.00.10.00
1.53 _.54_
0.15
3.00
Base
0.42:1032
0.82:10.16
1000
0.81
1.08:10.0
0.06:00.01
2.55+20.02
Face
+ HARE
0.21 0...2
1.00.10.00
0.81:10.06
0.13
3.80
Multi-HARE
0.25:10.24
0.010
1.00:0.000
0.75 10
1.12=0.53
0.14
3.70-10.09
Base
1.07=0.18
0.97=:0.26
0.86=00188
0.00 00
1.99=0.21
0.03+0.02
6.90 07
Wachter
HARE
0.89::0.17
0.37:
0.86:10.18
0.00=t0.00
1.64=0.29
0.15:10.03
7.99 0.01
Mul--HHREE
0.90.1..2
0.38.1..12
0.86110.18
0.00.10.00
4.55 10.51
0.14:10.07
7.9011..11
CREDIT
Base
1.06:0.199
1.06::0009
1.00+:0000 0.00
0.00
3.61=0.40
0.02
7.99
GS
HARE
0.811
0.401.0.06
1.00.1..00
0.00110.00
0.15:10.05
8.00110.00
Multi-HARE
0.83:10.21
00.07
1.00
0.00:10.00
4.13
0.21
8.00-10.00
Base
1.11::0.19
1.09
1.00:10.00
0.89:10.03
4.93 0.11
6.05 10
Faoe
HARE
0.90
0.42-L0.09
1.00
0.89-10.03
0.15:10.06
8..8
Muki-HARE
0.83++0.22
0.32
1.00=00.00
0.87=:0.03
3.67 #0.40
0.23+00.08
8.65+0.04
Table 4: Results with classical recourse metrics for the far g.t recourse scale. We show results on the ANN classifier on three datasets and three baselines. We use a budget of B = 30 and use T = 5 iterations for Multi-HARE. Our framework has low values of GTP and GTD, but has higher proximity score indicating that personalized recourses are not necessarily closest to the user's feature profile.
generales recourses that align strongly with the user's preferred direction of feature change. These results demonstrate that HARE and Multi-HARE are effective in incorporating wide variety of user preferences.
Other Metrics. In Table g we report classical recourse metrics for HARE and MAREi-HARE. Since our framework can be integrated with any recourse generalor, our Success Rale remains unchanged from the baseline recourse generator. We observe that our generated recourses have a large Prozimity score but a lower GPT and GTD score, indicating that user preferred recourses are not necessarily those closest to the user's feature profile. The generic proximity score is the Euclidean distance between the factual point and the generated recourse. This simple user-agnostic metric does not account for the preferences of different
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Published in Transactions on Machine Learning Research (03/2025)
Base
Boundary PointSearch
ADULT
COMPAS
CREDIT
Ours
Recourse
Best
Btvvery
GTP +
GTD 4
GTP
CTD
GTP
GTD
Base
0.93....8
0.87
0.394
0.75.10.30
1001
0.97.0..2
Wachter
MuR--HARE
X
0.64
0.11=0.03
0.33:10.25
0.54
0.95=0.19
0.47::0.09
X
0.58 0.28
0.08+0.03
0.27:0026
0.26=0.15
0.90+0.22
0.38
Base
0.92
0.82
0.40-10.31
0.80:10.10
1.06
1.06:10.09
GS
+ MuRE-HARE
x
V
0.56=0.22
0.08+00.0
0.08
0.12=0.04
0.30++0.22
0.25-
0.47-00.22
0,21#0.11
0.89=0.19
0.83
0.30=#0.07
0.39++0.02
Base
1.06 10.36
1.06 0.009
0.42.10.32
0.821
1.11
1.09
Face
+ MURE-HHRE
x 0 0.70:+0.29
0.13=0007
0.31+0.23
0.50+#026
0.90=0.19
0.40
X
0.56 0.22
0.08 10.02
0.25 10.24
0.21 10.10
0.83:10.25
0.32-10.08
Table 5: Study of choice of BoundaryPointteeree for the for g.t recourse scale. We show results 3 datasets and 3 baselines on ANN classifier for Multi-HARE. with T = 5 iterations. We study the impact of calibrating just the best candidate using BoundaryPointtearch each iteration (indicated by @Best in this table). Results show that calibrating all candidates leads to better user satisfaction.
Dataset
Base Recourae
Oura
Near
Intermediate
Far
GTP
GTD 4
GTP 4
GTD
GTP +
GTD J.
Base
0.18::0.06
0.70=:0.26
0.58=0.23
0.89=0.09
0.93+:0.38
0.87:
Wachter
+ HARE
0.08-00.22
0.09::0.02
0.364:0117
0.10#0.02
0.69
0.09::0 04
+ Multi-HARE
0.07
0.06+00.3
0.29::011
0.06:0.02
0.58=0.28
0.08
ADULT
Base
0.15+0.06
0.50
0.56+0.22 25
0.74#0.13
0,92 .9
0
GS
+ HARE
0.094
0.10
0.39:10.17
0.10
0.71
0.10
+ Malti-HARE
0.08100.3
0..04
0.30.10.15
0.08.10.4
0.27
0.08
Base
0.391
0.84
0.73:10.21
1.03
10.36
1.06:10.09
Face
+ HARE
0.12.00.3
0.23 L0.02
0.44
0.224
0..35
0.24110.05
+ Multi-HARE
0.10:0001
0.15
0.31 00.13
0.09
0.56 26
0.08-10.02
Base
0.12=00.07
0.45
0.27:0021
0.72 29
0.39
0.75 30
Wachter
+ HARE
0.0520.02
0.12-:0.03
0.17:10.13
0.26
0.27 -0.000000
0.27
+ Mult--HARE
0.06-0002
0.11 00003
0.17
0.25=00112
0.27=0.26
0.26=+0.15
COMPAS
Base
0.10
0.44
0.29
0.74
0.40
0.80:E0.10
GS
+ HARE
0.002
10.03
0.16 10.10
0.20
10.22
0.21
+ Multi-HARE
0.05::0.01
0.10
0.16::.111
0.20:00.07
00.2
0.21::0.10
Base
0.15
0..01
0.31
0.731
0.42
0.62.10.10
Face
+ HARE
0.06:0.02
0.114:0.04
0.16
0.21
0.26-10022 22
0.21 1:0.
+ Mult--HARE
0.06=0.01
0.10+0004
0.16+:0.12
0.20=2007
0.25
0.21
Base
0.20-10.03
0.68 19
0.65-10.03
0.88
1.07 18
0.97
Wachter
+ HARE
0.15
0.30
0.52 03
0.36-20088
0.89=0.17
0.37
+ HARE
0.15
0.34 00.02
0.52:10.08
0.36:10.10
0.90-:0.22
0.38:10.12
CREDIT
Base
0.21#0.02
0.90 13
0
1.04
1.06+:0.11 19
1.06=:0 0.0
GS
+ HARE
0.17:00.2
0.43
0.53:00004
0.39
0.89 18
0.40::0.00 06
+ Multi-HARE
0.39 10.11
0.49 10.06
0.30.10.00
0.83 0.22
0.30 1.000
Base
0.33-10.03
0.83 18
0.73
1.06
1.11::0.1 19
1.09:10.17
Face
+ HARE
0.17= 01
0.41
0.53 04
0.42 08
0.90 18
0.42:10 09
+ Mult--HAR
0.16110.01
0.36.10.16
0.49-10.06
2..04
0.83=0.22
0.32-10.08
Table 6: Study of change in ground-trui distance. We study the effect of simulating the ground-truth recourse at three scales near, intermediate, and far. We show results three datasets and three baselines for both HARE and Multi-HARE with B = 30 & T = 5. Both methods show good performance across all scales, indicating their effectiveness in incorporating wide variety of human feedback. We observe siginificant gains in the GTD metric, especially for the difficult far scenario.
users, and is not good measure for the effort the user has to undertake in order to accomplish the recourse (since the "true" cost function of the user is unknown). For instance, in Figure I of the main paper, user who strongly prefers to increase their level of income would be willing to choose a recourse with a higher L2 cost. For a concrele example from the ADULT dataset, we take a random user with the feat pro- file (excluding other features for brevity) of { "'capitalggann' ' "marital
o, 'hours': 35, 'capital_lost "lssslss'' :0, status': imarital_statu': Non-Married, 'age": 54). The simulated ground-truth recourse for this indi- vidual { ('capital_gain' 17184.48, 'hours': 2, hapital_losst :4247.444 .8,, "marital_status" Non Non-Married, 'age': 54) indicating that such an individual prefers to boost capital-gains ie make
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Published in Transactions on Machine Learning Research (03/2025)
money from investments and assets while drastically reducing the number of hours worked. The base recourse generator (Wachter in this case) suggests recourse of { d'capital_gan' 6907.8. .5, 'hours': 41.7, "capital_ tatttalloss':3000 :0000'300.3..
: Non-Married, 'age": 54} which is umsatisfactory for this user's strong preference for reducing hours and increasing income through cap- ital gains. However, HARE suggests the recourse { pappital_gain' 12536.8, 8, 'hours': 20.5, (capital_loss" 1020.72 :020
sarrtttlssstus':: Non-Married, 'age': 54} which is more reasonable suggestion for this user. HARE proposes a recourse that allows the user to reduce their work hours while allowing them to increase capital gains. This makes the recourse more practical and better aligned with the user's s preferences, though it is far away from the user's s original features in a euclidean sense.
5 Conclusions and Future Work
In this work, we address the important challenge of generating actionable explanations for model through algorithmic recourse. In particular, we focus on personalizing algorithmic recourse through human-in-the- loop framework that allows a user to choose appropriate counterfactuals that may work for them. To the best of our knowledge, this is a first effort on human-in-the-loop recourse that considers user preferences over counterfactuals. Our proposed framework, HARE, enhances traditional recourse generating algorithms by eliciting user preferences over candidate counterfactuals, which are used to generate better recourses. Our method can be used on top of any existing recourse generation methods, thus making highly generalizable. Considering the limited efforts so far, also propose an evaluation scheme to measure user satisfaction by simulating a ground-truth recourse. Our experimental evaluation shows that HARE generates recourses that significantly outperform earlier methods in terms of acceptance by end-users. HARE can generate better personalized recourses with as few as 10 comparison queries to the user. We hope that our framework can aid in the practical deployment of personalized recourse generation.
Broader Impact and Limitations. HARE terminates when the user is satisfied with a presented recourse, or when the user's budget is exhausted. HARE is flexible, allowing end users dictate the kinds of choices available them. For example, the ActionableSamplinn algorithm [ ensures that candidates also allows users to specily new actionability constraints the fly, and to restrict the range of sampling by tweaking y. For consequential decision making systems (loans, jobs, crime etc), it is in the end user's best interest to achieve a recourse that they can act on, and our method presents a simple and modular framework to the end user, where user preference can be integrated with any existing recourse generation method. However, addressing the limitations of base recourse generation methods is beyond the scope of this work. Furthermore, care must be taken to ensure that the presented options do not bias the end-user into choosing an unfavorable recourse. This can be ensured by having transparent candidate generation algorithm, and by having a mechanism where the end user can abstain from selecting any suggested candidate recourse. Further work is needed to ensure that personalization enhances end-user agency without introducing unfair or misleading recourses.
diverse. It
Sai Srinivas Kancheti would like to thank MoE for the PMRF fellowship support; he along with Vinceth N Balasubramanian, would also like to thank Microsoft India for the MAPG 2022 grant, under which this work was carried out. Rahul Vigneswaran would like thank the Reliance Foundation for their Post gratgraduate Fellowship. We thank the anonymous reviewers for their valuable feedback that improved the presentation of this paper.
Acknowledgementss.
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Reviews:
[{"review_id": "D4clXyIjh0", "reviewer": "Reviewer_Y1VJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "# Strenghts\n- Personalization of algorithm recourse is fruitful, important topic. The proposed approach is sensible in that preference elicitation is used to find the best match\n- Evaluation is done with numerous non-agnostic approaches, showing that method for personalization works well throughout\n\n# Weaknesses\n- Main claim for delta to related work (Toni et al. 2024) not clear. It is not clearly discussed how the competitor works. While feature interactions are modeled, the paper also proposes to have end-users select the best candidate option in multiple rounds.  \n- Main competitors (Toni et al. 2024 and Yetukuri et al. (2023)) are not compared against in empirical evaluation. If one does not give explicit reasons why a comparison is not needed / possible, one should evaluate against them. \n- Dataset and personalization of evaluation differs from related work. While datasets overlap, it is not argued why one does not exactly replicate prior evaluations - also wrt to the personalization simulation.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a method for personalized algorithm recourse, where low-cost, peronsalized propositions for feature value changes are recommended in order to change the output of a black-box classifier.\nThe authors generate possible recourse options from exisiting, re-usable recourse recommenders and use preference elicitation to learn which option is preferred by the end-user. The options are chosen to be maximally diverse and the preferred option is finally calibrated to be of minimal cost.\nThe approach is evaluated against several agnostic baselines, where the proposed approach is put on top. The chosen datasets are Adult Income, GiveMeSomeCredit, COMPAS. Preferences are artificially sampled, where the authors distinguish between differences distances.\nThe results show that the proposed approach generates more personalized recourses compared to the non-personalized baselines.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Discuss how it can be ensured that the preference elicitation loop converges. This might not happen, even if options are diverse. What if the base algorithms are not good enough for some cases?\n- Argue why one does not have to evaluate against the personalized competitors or perform empirical comparison\n- Dataset and personalization of evaluation differs from related work - what is the motivation for it?\n- Please better discuss the differences to Toni et al. 2024 - it may be that the preference elicitation step is also conducted based on options in their approach.", "broader_impact_concerns": "- It should be discussed what biases personalized recourses might have on the end-user. By presenting multiple options during the interactions, one could manipulate the end-user.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "2GNCbl0aRc", "reviewer": "Reviewer_1uEz", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "When describing the strengths and weaknesses of this approach, I am not able to quantify the novelty of the proposed technique with respect to the state-of-the-art, and the reasoning behind some design choices I feel are too abstract and not realistic. \n\n- **[Differences with De Toni et al., (2024)]** I do not understand the qualitative and quantitative differences between HARE and the cited approach proposed by De Toni et al. (2024). The paper cited by the authors **does** provide a **human-in-the-loop** approach for algorithmic recourse. In practice, their “interventions” $\\mathcal{I}$ are the actions the user needs to perform to obtain the counterfactual $x_{cf}$ from $x_{f}$. Thus, claiming it concerns “feature-level” preferences is incorrect since the cost is computed over the entire intervention. As far as my understanding goes, their approach could be used with any counterfactual generator and cost function $c$, since the contribution is the interactive preference elicitation procedure for recourse. Indeed, the same authors performed also an initial evaluation with real humans integrating their approach with a simpler cost function [1].\nThus, the TMLR author’s claim that _“[...] this is the first of such effort to propose a framework to incorporate user feedback over counterfactuals to generate recourse”_ could be an overstatement of the contribution.\n\n- **[Ground-truth recourses]** In Section 4.1, the authors define the “ground truth” recourses based on their $\\ell_2$ “distance” from the initial instance $x_f$. However, it feels somewhat unrealistic, since users usually do not have one preferred recourse, but might favour different counterfactuals.\n\n- **[Robustness of Algorithm 4 to user’s noise]** Algorithm 4 does not seem robust to a realistic interaction with a human. What if the human is noisy, and it provides a wrong comparison between $(r_j, r_i)$? Then, Algorithm 4 will not converge to the optimal recourse. Since Algorithm 4 does not learn a parametrized function (e.g., a Bradley-Terry model) leveraging previous user’s answers, HARE seems very impractical to use. \n\n**[Questions]**\n\n- Could you better qualify the quantitative difference between your approach and De Toni et al., (2024)?\n- Could you better explain your notion of the “cost” of achieving recourse? How do you simulate the user’s feedback CE($r_i$, $r_j$) practically?\n- How did you simulate the user interaction with HARE in your experiments (e.g., the relative/absolute feedback)?\n- Does HARE provide any guarantees that we are indeed offering the user the cheaper recourse option? Namely, do Algorithms 2 and 3 provide the set of options minimizing the regret at every turn $t$?\n- Why by increasing the number of iterations T (Table 3) we do not get improvements in certain scenarios in GTP (e.g., Adult)?\n\n**[Minor Nitpicking]**\n- In the Abstract, I would change the phrase _“[...] we observe that our framework significantly increases human satisfaction”_ since the experiments are all synthetic and the metrics used do not really model a realistic “user satisfaction”. \n- What is the meaning of line 6 in Algorithm 4? $CE_u$ is a function, while $r_j,r_i$ are recourses, so the set definition is not very rigorous.\n\n[1] Seyedehdelaram, et al. \"Preference elicitation in interactive and user-centered algorithmic recourse: an initial exploration.\" Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. 2024.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents HARE, a human-in-the-loop method to generate personalized recourse recommendations by eliciting user’s preferences from binary comparison between candidate recourses. The authors evaluated their approach with simulation studies on multiple real-world datasets using different recourse generators.\n\nThe paper does a good job of presenting the scenario, the solution and the evaluation. I also believe the topic is interesting and in scope with TMLR. However, there is room for improvement, for example by considering more realistic cost functions or user models. Moreover, it is not clear how HARE departs significantly from current solutions for personalized recourse.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "**[Required]** Perform an ablation experiment by considering Algorithm 4 with a more realistic user’s model of the relative/absolute feedback (e.g., noisy user).\n\n**[Required]** Perform an ablation experiment with a more realistic cost function (e.g., weighted distance metric $c(x, w) = w^{\\top}x$, where $w$ describes the preference of a user).\n\n**[Required]** Better clarify the difference between HARE and De Toni et al. (2024) and the novelty of the proposed approach. \n\n**[Optional]** Compare HARE with Yetukuri et al., (2023) and De Toni et al., (2024) since they seem to be reasonable baselines.", "broader_impact_concerns": "I do not have concerns regarding the work's broader impact.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "x8kLREizr0", "reviewer": "Reviewer_uV7n", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths \n\n* Paper is well written and easy to follow. \n* The study is well placed within the literature and covers the existing works very well. \n* Sufficient analysis with experiments are included using real world datasets.\n* I appreciate the detailed experimentation section.\n\n### Weaknesses\nListed in Requested Changes section below", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper improves upon the human centric actionable recourse literature. The paper proposes a human in the loop approach to provide an iteratively customizable recourse for improved actionability. This eventually leads to improved human satisfaction. This is achieved using a two way channel between the adversely affected individual and the ML model. To achieve this, the authors considers preferences over the counterfactuals rather than the individual features.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. It would be interesting to see the results for a much lower budget (B= 1, 2, 3 ... 10). This would help understand the difficulty of obtaining a cheaper recourse from an ML model.\n2. A quick discussion (on the alternative options) would be helpful when user is unable to accept any of the candidates presented. \n3. The proximity scores seems to be affected with the proposed system. Can the authors give some examples of the recourses generated and elaborate on how they are better.", "broader_impact_concerns": "This study is focussed on improving user acceptance of ML models by developing a human in the loop approach to modify the models decision. According to me, there are no major concerns related to ethical implications of this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: After the author rebuttal and revision, all reviewers were in favor of acceptance. It is also my view that their main concerns noted above were well addressed.

I recommend a minor revision for the camera-ready version to address the following remaining points:
- Reviewer 1uEz brought up a recent work [1] after the rebuttal, whose title is clearly similar to this submission's and should be discussed in the paper.
- I think that Appendix A in the revision makes clear the distinction from PEAR (Toni et al., 2024). However, in my reading, the last paragraph of Section 1 is not as clear regarding the assumption by Toni et al. (2024) of a particular form for the user's cost. I think this is an important point that should also be clear in the main paper, specifically Section 1.
- The authors should consider responding to Reviewer Y1VJ's last comment about possible user manipulation.

[1] Abrate, Carlo, et al. "Human-in-the-loop personalized counterfactual recourse." World Conference on Explainable Artificial Intelligence. Cham: Springer Nature Switzerland, 2024.
Decision: Accept (poster)