Actionable Recourse Guided by User Preference
Authors: Jayanth Yetukuri
Ian Hardy
Yang Liu
Venue: Submitted to ICLR 2023 (2023)
Keywords: Actionable recourse

Abstract: The growing popularity of machine learning models has led to their increased application in domains directly impacting human lives. In critical fields such as healthcare, banking, and criminal justice, tools that ensure trust and transparency are vital for the responsible adoption of these models. One such tool is \emph{actionable recourse} (AR) for negatively impacted users. AR describes recommendations of cost-efficient changes to a user's \emph{actionable} features to help them obtain favorable outcomes. Existing approaches for providing recourse optimize for properties such as proximity, sparsity, validity, and distance-based costs. However, an often-overlooked but crucial requirement for actionability is a consideration of \emph{User Preference} to guide the recourse generation process. Moreover, existing works considering a user's preferences require users to precisely specify their costs for taking actions. This requirement raises questions about the practicality of the corresponding solutions due to the high cognitive loads imposed. In this work, we attempt to capture user preferences via soft constraints in three simple forms: \textit{i) scoring continuous features, ii) bounding feature values} and \textit{iii) ranking categorical features}. We propose an optimization framework that is sensitive to {user preference} and a gradient-based approach to identify \emph{User Preferred Actionable Recourse (UP-AR)}. We empirically demonstrate the proposed approach's superiority in adhering to user preference while maintaining competitive performance in traditional metrics with extensive experiments.

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ACTIONABLE RECOURSE GUIDED BY USER PREFERENCE
Anonymous authors Paper under double-blind review
ABSTRACT
The growing popularity of machine learning models has led to their increased application domains directly impacting human lives. In critical fields such as healthcare, banking, and criminal justice, tools that ensure trust and transparency are vital for the responsible adoption of these models. One such tool actionable recourse (AR) for negatively impacted users. AR describes recommendations of cost-efficient changes to user's actionable features to help them obtain favorable outcomes. Existing approaches for providing recourse optimize for properties such as proximity, sparsity, validity, and distance- based costs. However, an often- overlooked but crucial requirement for actionability is consideration of User Preference to guide the recourse generation process. Moreover, existing works considering user's preferences require users precisely specify their costs for taking actions. This requirement raises questions about the practicality of the corresponding solutions due the high cognitive loads imposed. In this work. we attempt to capture user preferences via soft constraints in three simple forms: i) scoring continuous features, ii) bounding feature values and iii) ranking cate- gorical features. We propose optimization framework that is sensitive to user preference and a gradient-baat based approach to identify User Preferred Actionable Recourse (UP-AR). We empirically demonstrate the proposed approach's 's superi- ority in adhering to user preference while maintaining competitive performance in traditional metries with extensive experiments.
INTRODUCTION
Actionable Recourse (AR) (Ustun et al. 2019) is the ability of an individual to obtain the desired outcome from a fixed Machine Learning (ML) model. Several domains such as lending (iddiqi 2012). insurance (Scism) 2019), resource allocation (Chouldechova et al.) 2018 Shroff, 2017) and hiring decisions (Ajunwa et al. 2016) are required to suggest recourses to ensure the trust the de- cision system in place; in such scenarios, it is critical to ensure actionability in recourse (otherwise the suggestions are pointless). Consider an individual named Alice who applies for a loan, and the bank, which uses an MI-bbaed classifier, denies it. Naturally, Alice asks What can I do get the loan? The inherent question is what action she must take to obtain the loan in the future. Counter- factual explanation introduced in Wachter (Wachter et al., (2017) provides a what-if scenario alter the model's 's decision. AR further aims to provide Alice with a feasible action. A feasible action is both actionable by Alice (meaning she can reasonably execute the directed plan) and suggests as low-cost modifications as possible.
While some features (such as age or sex) are inherently inactionable, Alice's personalized constraints may also limit her ability to take action on the suggested recourse (such as a strong reluctance to secure co-applicant). We call these localized constraints User Preferences, synonymous to user- level constraints introduced as local feasibility by Mahajan et al. (2019). Figure | illustrates the motivation behind UP-AR. Notice how similar individuals can prefer contrasting recourse.
Actionability, we consider it, is centered explicitly around individual preferences, and similar recourses provided to two individuals (Alice and Bob) with identical feature vectors may not nec- essarily be equally actionable. Most existing methods of finding actionable recourse restricted to omission of features from the actionable feature set which Alice does not prefer to act upon, and box constraints Mothilal et al. 2020 in the form of bounds feature actions.

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Alice's
Table 1: A hypothetical actionable feature of ad- versely affected individuals sharing similar features and corresponding suggested actions by AR and UP- AR. UP-AR provides personalized recourses based on individual user preferences.
preferred
space
Bob's
preferred
space
Alice Beb
Actionable Curr. val.
UP-AR
Features
values
Decision
Alice
Bob
houndary
LoanDuration
18
17
LoanAmount
$1940
$1840
$1200
Illustration of UP-AR. Similar individuals Alice and Bob with contrasting preferences can have different regions desired feature space recourse.
Figure 1:
HasGuarantor
0
HasCoapplicant
0
In this study, we discuss three forms of user preferences and propose formulation capturing such idiosyncrasies. The proposed score- based preference mechanism can easily communicate with an individual user, providing seamless feasible recourse. We argue that communicating in terms of preference scores improves the explainability of a recourse generation mechanism, which ultimately improves trust in an ML model. We provide hypothetical example of UP-AR's ability to adapt to individual preferences in Table However, there is a lack of contemporary research in under- standing Alice's individual preference, which may be contrary to Bob's s preference. It is also worth mentioning that the existing method's 's rigid modifications on actionable features limit the possibility of identifying recourse due diminished actionable space.
Motivated by the above limitation, we intend to capture soft user preferences along with hard con- straints and identify recourse based on local desires without affecting the success rate of identifying recourse. For example, consider Alice prefers to have 80% of fractional cost from loan duration and only 20% from the loan amount, meaning she prefers to have recourse with a minor reduction in the loan amount. Such recourse enables Alice get the benefits of loan her terms. We study the problem providing user preferred recourse by solving a custom optimization for individual user-based preferences. Our contributions are consolidated as follows:
We start by enabling Alice to provide three types of user preferences: i) Scoring, ii) Ranking, and iii) Bounding. We embed them into an optimization function to guide the recourse generation mechanism.
We then present our approach called User Preferred Actionable Recourse (UP-AR) to identify recourse instead of overwhelming her with a variety recourse options. Our approach highlights cost correction step to address the redundancy induced by our method.
We also consolidate performance metrics with empirical results of UP-AR across multiple datasets and compare them with state-of-art techniques.
1.1 RELATED WORKS
Several methods exist identify counterfactuals, such as FACE (Poyiadzi et al. 2020). which uses the shortest path to identify counterfactual explanations from high-density regions, and Growing Spheres (GS) (Laugel al. 2017) which employs random sampling within increasing hyperspheres for finding counterfactuals. CLUE (Antoran et al. 202T) identifies counterfactuals with low un- certainty in terms of the classifier's entropy within the data distribution. Similarly, manifold-based CCHVAE (Pawelczyk et al.) 2020) generates high-density counterfactuals through the use of la- tent space model. However, there is often no guarantee that the what-if scenarios identified by these methods are attainable.
AR observes that a feasible recourse achieves good results in all the performance metrics. Existing research focuses on providing feasible recourses, yet comprehensive literature understanding and incorporating user preferences within the recourse generation mechanism is lacking. It is worth men- tioning that instead of better understanding user preferences, Mothilal et al. (2020) provides user with diverse recourse options and hopes that the user will benefit from at least one. The importance of diverse recourse recommendations have already been explored in recent works (Wachter al.
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2017 Mothilal al., 2020: Russell 2019), which be summarized as increasing the chances of actionability as intuitively observed in the domain of unknown user preferences (Karimi et al. 2021). Karimi et al. (2000) and Cheng et al. (2220) resolves uncertainty in user cost function by inducing diversity in the suggested recourses. Interestingly, only 16 out of the 60 recourse meth- ods explored in the survey by Karimi et al. (202T) include diversity constraint where diversity is measured in terms of distance metrics, Alternatively, studies like [Ustun et al. (201);;[] Rawal & Lakkaraju (2020); Cui et al. (2015) optimize on a universal cost function. This assumption does not capture individual idiosyncrasies and preferences crucial for actionability.
Initial efforts of eliciting user preferences include recent work by De Toni al. 2022). The au- thors provide an interactive human cuman--n--heee in-the-loop approach, where a uset continuously interacts with the system. However, learning user preferences by asking them to select from one of the partial interventions (De Toni et al.] 2022) provided is a derivative of providing diverse set of recourse candidates. In this work, we consider fractional cost as a means to communicate with Alice instead of recourse generation procedure or the nature of the cost function. The fractional cost of a feature refers to the fraction of cost incurred from a feature out of the total cost of the required intervention.
The notion of user preference or user-level constraints was previously studied as local feasibility (Mahajan et al. 2019). Since users can not precisely quantify the cost function (Rawal & Lakkaraju, 2020). Yadav et al. (202T) diverged from the assumption of universal cost function and optimizes over the distribution of cost functions. We argue that the inherent problem of feasibility can be solved by capturing and understanding Alice's recourse preference and adhering her constraints which can vary between Hard Rules such as unable to bring a co-applicant and Soft Rules such as hesitation reduce the amount, which should not be interpreted as unwillingness. This study aims to capture individual constraints and provide a user-preferred recourse. To the best of our knowledge, this is the first study to capture individual idiosyncrasies in the recourse generation optimization problem to improve feasibility.
2 PROBLEM FORMULATION
Consider binary classification problem where each instance represents an individual's feature vec- tor x = [x1, X2..... ...... Xp] and an associated binary label y E {-1,+1]. We are given a model fx) to classify into either -1 +1. Let fxx +1 be the desirable output of f(x) for Alice. How- ever, Alice was assigned an undesirable label of -1 by f. We consider the problem of suggesting an action r [r. actions to be taken on actionable features denoted by FA. we have r; = 0 Vi & FA. We further split FA into continuous actionable features Feon and categorical actionable features Feat based on the feature domain. A low-cost action r is obtained by solving the following optimization, where userCost (r.x) is any predefined cost function of taking an action specified by Alice.
such that f(x - r) +1. Since suggested recourse only requires
min userCost (r,x) s.1. userCost (r,x)=
Σ KEFA
userCost (ri,x.) and f(x r) =1. (1)
2.1 CAPTURING INDIVIDUAL IDIOS NNCRASIES
crucial step for generating recourse identifying local feasibility constraints captured in terms of individual user preferences. In this study, assume that every provides their preferences in three forms. Every continuous actionable feature E Feon is associated with a preference score I; obtained from the affected individual. Additional preferences in the form of feature value bounds and ranking for preferential treatment of categorical features are also requested from Alice.
User Preference Type (Scoring continuous features): User preference for continuous features are captured in r, E [0, 1] Vi E Feon subject to Σ T, = 1. Such soft constraints capture the user's preference without omitting the feature from the actionable feature set. I refers to the fractional cost of action Alice prefers to incur from continuous feature i. For example, consider Fron = [LoanDuration LoanAmount) with corresponding user-provided scores I = (0.8, 0.2} im- plying that Alice prefers to ineur 80% of fractional feature cost from taking action on LoanDuration, while only 20% of fractional cost from taking action on LoanAmount, Here, Alice prefers reducing I LoanDuration to LoanAmount and providing recourse in accordance improves actionability.
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I: Iterative
Preferences
approach
Bi E Fea
ML model
Recourse
IT: Cost
20
Figure 2: Framework of UP-AR. Successful recourse candidates; r(), r are colored in pink.
User Preference Type II (Bounding feature values): Users can also provide constraints on val- ues for individual features in FA- These constraints in the form of lower and upper bounds for individual feature values represented by and 5, for any feature i respectively. These constraints are used to discretize the steps. For continuous feature i, action steps be discretized into pre- specified step sizes of A = {s s E 0.]) For categorical features, steps are defined as the fea- sible values a feature can take. For all categorical features we define, A, = f6,....., Vi Feat representing the possible values for categorical feature
User Preference Type III (Ranking categorical features): Users are also asked provide ranking function R. Feat + Z+1 Foot- Let R, refers to the corresponding rank for cat- egorical feature i. Our framework identifies recourse by updating the candidate action based on the ranking provided. For example, consider Feut = (HasCoapplicant, HasGuarantor, CriticalAc- LountOrLonsEllewwher) for which Alice ranks them by {3, 2, 2,1]. The recourse generation system considers suggesting action HasGuarantor before Has Caappppppi,, Ranking preferences can be easily guaranteed by a simple override in case of discrepancies while finding recourse.
2.2 PROPOSED OPTIMIZATION
We depart from capturing a user's cost of feature action and instead obtain their preferences for each feature. We elicit three forms of preferences detailed in the previous section and iteratively take steps in the action space. We propose the following optimization over the basic predefined steps based on the user preferences. Let us denote the inherent hardness of feature action r; for feature value X; using cost (r, x) which can be any cost function easily communicable to Alice. Here, cost (r." .x.)
refers to a "universal" cost of taking an action r(!)) for feature value x, at step t. Note that this cost function quantity differs from the userCost (:,-) function specified earlier. This quantity is capturing the inherent difficulty of taking an action.
I;
max
Σ
(Type I)
cost (ri, x;)
YEFA
s.l.
f(x = +1
I; 0: Vi F'A
(actionability)
rg 1: Vj Feat
r, A iE FA
(Type II)
1(r: > 0} 2 1(r, > 0} R4 2 R2 Vi,j j E Foat
(Type III)
The proposed method minimizes the cost of a recourse weighted by T; for all actionable features. We discuss the details of our considerations of cost function in Section 3.] The order preference of categorical feature actions can be constrained by restrictions while finding recourse. The next section introduces UP-AR stochastic solution the proposed optimization.
USER PREFERRED ACTIONABLE RECOURSE (UP-AR)
Our proposed solution, User Preferred Actionable Recourse (UP-AR), consists of two stages. The first stage generates candidate recourse by following connected gradient-based iterative approach. The second stage then improves upon the redundancy metric of the generated recourse for better actionability. The details of UP-AR are consolidated in Algorithm and visualized in Figure
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3.1 STAGE 1: STOCHASTIC PRADIENT-BASED APPROACH
Poyiadzi et al. (2020) identifies a counterfactual by following a high-density connected path from the feature vector With similar idea, follow a connected path guided by the user's preference to identify a feasible recourse. We propose incrementally updating the candidate action with predefined step size to solve the optimization. At each step a candidate intervention is generated, where any feature i is updated based on Bernoulli trial with probability 1 derived from user preference scores and the cost of taking a predefined step o using the following procedure:
1 Bernoulli (o (z()) ) where
(2()) EjEFA /
r; cost
rr
With precomputed costs for each step, weighted inverse cost is computed for each feature, and these values are mapped to a probability distribution using a function like softmax. Softmax gives a prob- abilistic interpretation P (I 1|=(")
(2(0)
by converting 2, scores into probabilities.
We leverage the idea of log percentile shift from AR to determine the cost of action since it is easier communicate with the users in terms of percentile shifts. Specifically, we follow the idea and formulation in Ustun et al. (2019) to define the cost:
cost (r,, x:) = log 1- Qi (x: +r:))
(3)
1 Qi (x.)
were Qi (x:) representing the percentile of feature i with value x; is a score below which Q; (x,) percentage of scores fall in the frequency distribution of feature values in the target population.
We adapt and extend the idea that counterfactual explanations and adversarial examples (Szegedy)
al., 2014) have similar goal but with contrasting intention (Pawelezyk al. 2022). popular approach to generating adversarial examples (Goodíellow et 20144) is by using a gradient-based method. We employ the learning of adversarial example generation to determine the direction of feature modification in UP-AR. Jacobian matrix is used to measure the local sensitivity of outputs with respect to each input feature. Consider that ʃ : IRD + R.K maps D-dimensional fea- ture vector to K K-dimensional vector, such that each of the partial derivatives exists. For given x , ....... [pl] and fxx = f())
the Jacobian matrix of f 5x
#fp(() For
defined to be a D x K matrix denoted by J, where each (j. entry is Jj,+ = neural network (NN) with at least one hidden layer, Jjs is obtained using the chain rule during backpropagation. For NN with one hidden layer represented by weights {w}, we have:
(x) 0x;
0ff day Ōx;
J
where at = Σ wXX;
Bor
Where in Equation 4 a the output (with possible activation) of the hidden layer and wy is the weight of the node I. Notice line 4 in Algorithm which updates the candidate action for a feature i step /. as:
( = (((()) sign (J T,(0) 1,0000 600
Here mon the Hadamard product of any two vectors and n. Following the traditional notation of a binary classification problem and with a bit of abuse of notation -1 + 1, +1 - +1, sign (I(
+1,s
captures the direction of the feature change at step t. This direction iteratively calculated, and additional constraints such as non-increasing non-decreasing features can be placed at this stage.
3.1.1 CALIBRATING FREQUENCY OF CATEGORICAL ACTIONS
We employ temperature scaling (Guo et al. 2017) parameter observed in Equation 2 to calibrate UP-AR's recourse generation cost. Categorical actions with fixed step sizes are expensive, espe- cially for binary categorical values. Hence, tuning the frequency of categorical suggestions can significantly impact the overall cost of a recourse. 7 controls the frequency with which categorical actions suggested in UP-AR.
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Algorithm User Preferred Actionable Recourse (UP-AR)
Input: Model f. user feature vector x, cost function cost (:, 1) step size A, : Vi Fs. maximum steps 7", action initialized to r()), fixed T,
I: while - T or f (x + =r() + +1 do
2:
r, (x
/
3:
10th Bemllo ((())) where (0 ΣjEPa 1x
1 = (t-1) + sign (J (1,1) 1/t) o 00 Vi E FA
4:
5:
1=1+1
6: Let be the smallest step such that f(x + r()) = +1 and initialize
7: if 3i Feat : :!()) >
0 then
8: while f(x + +1) do
F(O) = r();; F = ( ViE Feats t=t-1
9:
10: return F(t) action
To study the effect of r on overall cost, we train a Logistic Regression (LR) model on a processed version of German (Bache Lichman 2013) dataset and generate recourses for the 155 individuals who were denied credit. Figure 10 in Appendix C.3 depicts that the cost gradually decreases with decreasing r since the marginal probability of suggesting a categorical feature change diminished. Hence, without affecting the success rate of recourse generation, the overall cost of generating re- courses can be brought down by decreasing т. In simple terms, with higher т, UP-AR frequently suggests recourses with expensive categorical actions. After the strategic generation of interven- tion, we move to the next step of cost correction to improve upon the redundancy metric.
3.2 STAGE 2: REDUNDANCY AND COST CORRECTION (CC)
In experiments, observe that once expensive action is recommended for a categorical fea- ture, some of the previously generated action steps might become redundant, Consider an LR model trained on the processed german dataset. Let FA LLanDuration, LoanAmount, HasGuarantor) out of all the 26 features, where HasGuarantor is a binary feature which represents the user's ss ability get a guarantor for the loan. Stage takes several steps over LoanAmount and LoanDuration before recommending to update HasGuarantor. These steps are based on the feature action prob- ability from Equation? 2 Since categorical feature updates are expensive and occur with relatively low probability, Stage I finds low-cost recourse by suggesting low-cost steps more frequently in comparison with high-cost steps.
Table 2: Redundancy corrected recourse for hypothetical individual.
Features
Current values
Stage values
Stage 2 values
change
LesnDuretion
12
SoanAmount
$1940
$1340
$1540
BasGuarantor
Once a high-cost feature such as HasGuarantor recommended to update, some of the previous low-cost steps may be redundant, which can be rectified by tracing back previous steps. Notice that the cost correction (CC) procedure is only triggered when UP-AR suggests categorical action. Consider scenario such that 3i E Fout (T) > 0 for recourse obtained after T steps in Stage 1. The CC procedure updates all the intermediary recourse candidates to reflect the categorical changes , perform a linear retracing procedure to return (()) such that ʃ (x+ (x+)(t) +1 for the smallest L.
i.e., Vi E Feat r > O, we update (t) (T) Vi E {1, ,,...,T - 1} to obtain f(?) We then

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Toootatte
1.0
0.75
0.5
0.50
AR
UP-AR
GS
UP-AR
Recourse Method
Recourse Methods
Figure 3: AR and UP-AR's distribution I LomnDoraatioe
Figure 4: GS and UP-AR's distribution of i DedoRatio
for a Logistic Regression model trained on German.
for a Neural Network model trained on GMSC.
DISCUSSION AND ANALYSIS
In this section, we analyze the user preference performance of UP-AR. For simplicity, stands cost terms of log percentile shift from her initial feature vector described in Section 3 Let I be the observed fractional cost for feature i formally defined in Equation 6 Any cost function can be plugged into UP-AR with restrictions.
under-
prefers have T; fraction of the total desired percentile shift from feature i. Consider Fa {LoanDuration, LoanAmount] and let the corresponding user scores provided by all the adversely affected individuals be: I {0.8. 0.2). Here, "Denied loan applicants prefers reducing LoanDuration to LoanAmount by 8 2." Figure shows the frequency plot of feature cost ratio for feature LoanDuration out of total incurred cost from LoanDuration and LoanAmount. i.e., V-axis represents F, Also, Figure 4 further shows the fractional cost of feature DebiRatio for recourses obtained for NN based model trained on Give Some Credit (GMSC) dataset. These experiments signify the adaptability of UP-AR to user preferences and provides evidence that UP-AR's distribution of I; is centered around Ts.-
Lemma (Cost guarantee) Consider UP-AR identified recourse for individual If C
(T") and mmma represent the minimum and maximum cost of any step for feature i until T then:
E [cost x.), T
((T")
C(T
Lemma immples that the expected cost E |cost (I,, x,)], specifically for continuous feature action is positively correlated to the probabilistic interpretation user preference scores. Hence satisfies users critical Type I constraints in expectation. Recall that Type II and III constraints also applied at each step f. Lemma signifies that UP-AR adheres to user preferences and thereby increases the actionability of suggested recourse.
Corollary For UP-AR with linear a (.), predefined steps with equal costs and cost (r,x) = EiEPa cost (ri, total expected cost after T steps is E [cost (r,x)] 7 EiEF, o (I:).
Corollary states that with strategic selection of (-), 8!-) and cost (: UP-AR also tune the total cost of suggested actions. In the next section, we will compare multiple recourses based on individual user preferences for randomly selected adversely affected individual.
4.1 CASE STUDY INDIVIDUALS WITH SIMILAR FEATURES BUT DISPARATE PREFERENCES
Given an LR model trained on german dataset and Alice, Bob and Chris be three adversely affected individuals. FA (LoanDuratiin, LoanAmount, HasGuarantor) and corresponding user prefer- ences are provided by the users. In Table we consolidate the corresponding recourses generated for the specified disparate sets of preferences. From Table we emphasize the ability of UP-AR to generate a variety of user-preferred recourses based their preferences, whereas AR always pro- vides the same low-cost recourse for all the individuals. The customizability individual users can be found in the table. When the Type I score for LoanAmount is 0.8, UP-AR prefers decreasing loan amount to loan duration. Hence, the loan amount is much lesser for Chris than for Alice and Bob.
feature actions for

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Table 3: Recourses generated by UP-AR for similar individuals with a variety of preferences.
Alice
Bob
Chris
Features to
Current
AR
User
UP-AR
User
UP-AR
User
UP-AR
change
values
values
Pref.
values
Pref.
values
Pref.
values
IoanDuration
30
25
0.8
10
LeanAmouat
$3072
$5669
0.2
$7372
0.2
$6472
0.8
$5272
HasCuarentor
EMPIRICAL EVALUATION
In this section, we demonstrate empirically: 1) that UP-AR respects f--fractional user preferences the population level, and 2) that UP-AR also performs favorably traditional evaluate metrics drawn from CARLA Pawelczyk et al. (2021). We used the native CARLA catalog for the Give Me Some Credit (GMSC) Kaggle 2011). Adult Income (Adult) (uua & Graff 2017) and Correctional offender Management Profiling for alternative Sanctions (COMPAS, AS) (Angwin et al. 2016) data sets as well as pre-trained models (both the Neural Network (NN) and Logistic Regression (LR)). NN has three hidden layers of size [18, 9, 3], and the LR a single input layer leading to a Softmax function. Although AR is proposed for linear models, it can be extended to nonlinear models by the local linear decision boundary approximation method LIME Ribeiro et al. (2016), which we refer to as AR-LIME.
PERFORMANCE METRICS:
For UP-AR, we evaluate: 1) Success Rate: The percentage of adversely affected individuals for whom recourse was found. 2) Average Time Taken: The average time (in seconds) to generate recourse for a single individual. 3) Constraint Violations: The aver- age number of non-actionable features modified. 4) Redundancy: A metric that tracks superfluous feature changes, For each successful recourse calculated on a univariate basis, features are flipped their original value. The redundancy for recourse is the number of flips that do not change the model's classification decision. 5) Proximity: The normalized l2 distance of recourse to its original point. 6) Sparsity: The average number of features modified.
We provide comparative results for UP-AR against state-of-the-ard coumterfactuul//rcouur gener- ation techniques such as GS. Watcher, AR(-LIME), CCHAVE and FACE. These methods were selected based on their popularity and their representation of both independence and dependence based methods, as defined in CARLA. In addition the traditional performance metrics, we also measure Preference-Row mean squared error (pRMSE) between the user preference score and the fractional cost of the suggested recourses. We calculate pRM S, for a randomly selected contin- uous valued feature i using: We evaluate PRMSE, across the recourses generated for adversely affected individuals.
PRMSE
((0) -r()) where r(1)
cost XI), EkeFeon cost (Tk Xx:)
Here 1'0' and 1()) are user provided and observed preference scores feature i for an individual In Table 4 we summarize pRMSE, which is the average error across continuous features such that: PRMSE Feaml Eieroon PRMSE,
We train an LR model on the processed version of german Bache Lichman 2013) credit dataset from sklearn's 's linear model module. We replicate [Ustun et al. (2019)'s model training and recourse generation on german. The dataset contains 1000 data points with 26 features for loan application. The model decides if an applicant's credit request should be approved not. Consider Foom = (LoanDuration, LoanAmount] and Feat = CertiiclAcccmmtOrLnnneeeeeee HasGuarantor, ) Let the user scores for From be r (0.8, 0.2) and ranking for Feat be {3. 1,2} for all the denied individuals. For this experiment, we set T = 4. Out of 155 individuals with denied credit, AR and UP-AR provided recourses to 135 individuals.
DATASETS:
Cust Correction: Out of all the denied individuals for whom categorical actions were suggested, an average of ~ $400 in Aoantm recovered by cost correction.
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Table 4: Summary performance evaluation of UP-AR. Top performers are highlighted in green.
Neural Network Model
Logistic Regression
Data.
Recourse
Succ Rate
pRM se
Avg Tim.
Con. Vio.
Red.
Pro.
Spa.
Succ Rate
pRM SB
Avg
Con.
Red.
Pro.
Spa.
Method
Tim.
Vio.
58
0.75
0.16
0.02
0.00
6.95
1.01
8.89
0,62
0.18
0,03
0.00
4.08
1.39
8.99
Wachtar
1.00
0.18
0.02
1.49
6.84
1.08
8.46
0.0
0.17
0.03
3.51
7.18
GMBC
ARI-LIME
0.03
0.17
0.45
0.00
0.00
0.17
1.72
0.17
0.17
0.73
0.00
0.00
0.33
1.91
OCHVAE
1.00
0.18
1.05
2.0
9.99
10.1
2.00
2.00
8.64
2.05
11.0
FACE
1.00
0.17
8.05
1.57
6.65
1.20
6.69
0.00
0.18
11.9
1.65
7.47
2.30
8.45
UP-AR
0.31
0.07
0.08
0.00
1.30
0.49
3.22
2.00
0.07
0.12
0.00
1.47
0.88
3.52
0.84
0.10
0.03
0.00
2.86
1.30
1.76
2.05
5.85
Machtez
0.55
0.10
0.04
1.44
3.05
0.74
4.90
0.00
0.11
0.10
1.68
0.90
1.44
5.81
Adult
(-LIHE
0.42
9.20
0.30
2.10
2.54
7.37
0.00
0.03
2.31
OCHVAE
0.84
0.11
0.73
4.47
5.83
3.95
9.40
0.84
0.10
1.08
4.22
6.85
3.96
9.45
FACR
1.00
0.10
6.78
4.58
7.54
4.11
7.91
0.
0.10
8.37
4.83
9.91
4.28
7.81
UP-AR
0.82
0.10
0.76
0.00
0.78
1.77
2.78
0.62
0.05
0.G7
0.00
0.55
1.78
2.88
38
1.00
0.15
0.03
0.00
1.39
0.47
3.35
00
O. 24
0.00
0.00
0.34
1.112
3.98
archter
1.00
0.05
1.61
4.35
2.00
1.00
4.83
COMPAS AR LITEI
0.65
0.13
0.20
0.00
0.30
0.78
0.90
0.52
0.
0.24
0.03
0.00
1.45
1.57
CCHVAE
1.00
0.14
5.09
2.27
4.31
1.70
4.91
0.00
0.14
0.02
1.82
2.70
1.74
4.52
FACE
1.00
0.15
0.37
2.39
3.96
2.35
4.72
0.00
0.15
0.40
2.47
4.36
2.46
1.81
UP-AR
0.92
0.08
0.04
0.00
0. 60
0.63
1.82
00
0.10
0.05
0.08
0.81
0.82
2.74
For the following datasets, for traditional metries, user preferences set to be uniform for all actionable features to not bias the results to one feature preference over another: 1) GMSC: The data set from the 2011 Kaggle competition is credit underwriting dataset with 11 features where the target is the presence of delinquency. Here, we measure what feature changes would lower the likelihood of delinguency. We again used the default protected features (age and number of dependents). The baseline accuracy for the NN model is 81%, while the baseline accuracy for the LR is 76%. 2) Adult Income: This dataset originates from 1994 census database with 14 attributes. The model decides whether an individual's s income is higher than 50, 000 USD/year. The baseline accuracy for the NN model is 85%, while the baseline accuracy for the LR 83%. Our experiment is conducted a sample of 1000 data points. 3) COMPAS: The data set consists of 7 features describing offenders and a target representing predictions. Here, we measure what feature changes would change automated recidivism prediction. The baseline accuracy for NN is 78%, while baseline accuracy for LR 71%.
We find UP-AR holistically performs favorably to its counterparts. Critically, it respects feature constraints (which we believe is fundamental to ac- tionable recourse) while maintaining a significantly low redundancy and sparsity. This indicates that it tends to change fewer necessary features. Its speed makes it tractable for real-world use, while its proximity values show that it recovers relatively low-cost recourse. These results highlight the promise of UP-AR as performative. low-cost option for calculating recourse when user prefer- ences are paramount. UP-AR shows consistent improvements over all the performance metrics. The occasional lower success rate for NN model is attributed to 0 constraint violations.
PERFORMANCE ANALYSIS OF UP-AR:
pRMSE: We analyze user preference performance in terms of pRM SE. From Table we ob- serve that UP-AR's PRMSE is consistently better than the state of art recourse methods. The corresponding experimental details and visual representation of the distribution of pRMSE is deferred to Appendix C.2
CONCLUDING REMARKS
In this study, we propose to capture different forms of user preferences and propose optimiza- tion function to generate actionable recourse adhering to such constraints. We further provide an approach to generate a connected (Laugel et al., 2019) recourse guided by the user. We show how UP-AR adheres to soft constraints by evaluating user satisfaction in fractional cost ratio. We empha- size the need to capture various preferences and communicate with the user in comprehensible form. This work motivates further research on how truthful reporting of preferences can help im- prove overall user satisfaction.

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7 ETHICS STATEMENT
We proposed a recourse generation method for machine learning models that directly impact human lives. For practical purposes, considered publicly available datasets for our experiments. Due care was taken not to induce any bias this research. We further evaluated the primary performance metric for sensitive groups (males and females) for german dataset.
This study reflects our efforts to bring human subjects within the framework of recourse generation. Comprchensible discussion with the users about the process improves trust and explainability of the steps taken during the entire mechanism. With machine learning models being deployed in high- impact societal applications, considering human inputs (in the form of preferences) for decision- making is a highly significant factor for improved trustworthiness. Additionally, comprehensible discussion with human subjects another crucial component of our study. Our study motivates further research for capturing individual idiosyncrasies.
Gathering preferences from an individual could be another potential source of bias for UP-AR re- courses, which needs to be evaluated with further research with human subjects. Preferential re- courses will have significant positive impact on humans conditioned on truthful reporting of vari- ous preferences. Preference scores are subject to various background factors affecting an individual, some of which can be sensitive. Additional care must be taken to provide confidentiality these background factors while collecting individual preference scores, which have the potential to be exploited.
REPRODUCIBILITY STATEMENT
We have added the experiment and implementation details both in the paper and the supplementary materials to reproduce our reported results and our methodology. We have faithfully followed the proposed the algorithms to implement our solutions. We have clearly stated and cited the source of our data, as well as the evaluation metrics. We have also reported the computational time required perform the experiments.
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A
APPENDIX
We have added the following updates in the Appendix to the latest document
1. Section B on User acceptance survey.
2. Section C.2
on Random user preference study.
3. Section C.7
on UP-AR scalability with actionable features dimension.
A.1 ANALYSIS
Interpretable and Incremental steps: In this study, each step o!") is predefined minimal feature modification inherently derived from the feature vector x. A recourse suggested by UP-AR can be broken down into interpretable actions. Alice recourse is to decrease the loan amount from $8072 to $6472 and decrease the loan duration from 30 years 10 years. Here the recourse is broken down into reducing the loan amount by 16 steps of $100 each, implying that the loan amount is 16 steps connected with the original feature value. Such steps increase the comprehensibility of recourse.
denied loan application, and her suggested
B USER ACCEPTANCE SURVEY
We conducted a survey with 40 random students and employees from a mailing list. The survey included one question with four options as follows: you are denied a loan application. What do you expect from the bank to get your loan approved ?
1. A single list of suggestions to your profile. Example: (increase income by 100$ and reduce loan duration by year)
2. A set with multiple lists of suggestions to your profile. Example: (i) increase income by 100S and reduce loan duration by year OR increase income by 500S OR iii) reduce loan duration by year OR iv) bring a co-applicant)
3. Inftuence the bank's suggestions by providing preferential scores for actions you can take. Example: (preferring to increase loan duration more than loan amount by 8:2, or preferring bring guarantor before a co-applicant)
4. Any other form of preferences
Every individual in the survey was asked select one of the four choices provided. In this survey, is identified that majority of 60% of individuals preferred influencing the bank's decision by providing preference scores for individual features, followed by 30% of individuals who wanted multiple recourses from the bank. The remaining 10% of individuals preferred single recourse or any other form of preference.
C ABLATION STUDIES
In this section, we perform multiple experiments to understand several properties of UP-AR. First, run an experiment to measure the disparities in PRMSF between the two gender groups. Secondly, we run experiments to understand the effects of the temperature parameter 7 on UP-AR. Thirdly, we try to understand the relation between T and I" if any.
C.1 UP-AR USER PREFERENCE DISPARITIES
UP-AR satisfies user Type user preferences as observed in Section 4 For the following experiment, we consider similar setup in Section 4 We now evaluate similar performance among males and
females separately in terms of PRMSE. With similar setup as Section 4 Figure 6 shows a distribution of cost between the two gender groups. Observed PRMSE, Duration
Loann for males is 0.09, whereas for females it is 0.11. With this simple experiment, we conclude that UP-AR does not show any significant disparities in terms of adhering preferences.
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If you are denied loan application. What do you expect from the bank to get your loan approved
Answered:
Skipped:
100%
80%
60.00%
60%
30.00%
30%
Single ruggestion
Muttille euggedtion
Preferotial. suggestion
Othera
ANSWER CHOICES
RESPONSES
Single suggestion
6.00%
suggrstion
Preferential suggution
600 00%
06
Others
5.00%
Figure Snapshot of the human acceptance survey.
1.0
LoanDuration
0.8
0.6
0.4
LoanDuration
0.2
Female
Male
Gender Group
Figure 6: Comparison of UP-AR's distribution of I LoanDuration between males and females for
Logistic Regression model trained on German.
C.2 RANDOM USER PREFERENCE STUDY
We performed an experiment with increasing step sizes on German dataset with similar exper- imental setup as previous section. We observed that, with inereasing step sizes, pRM SE, increased from 0.09 to 0.13, whereas it was consistent for AR.
In the next experiment, we randomly choose user preference for LoanDuration from [0.4, 0.6, 0.7, 0.8). The rest of the experimental setup is identical to the setup discussed in Section 4 In this experiment, we observe PRMSE with non-universal user preference for adversely affected individuals. Here the average pRMSE of both LoanDuration and Load- Amount for UP-AR is 0.19, whereas for AR it is 0.34.
generated recourses for set of 1000 individu- als and I' for two continuous features was randomly selected from [0.3, 0.6, 0.9]. Figure 9 provides visual analysis of the distribution of average pRMSF using violin plots. The
Further, using the CARLA package,
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experiments performed the 3 datasets discussed in Section for both the LR and NN models. For GMSC dataset, Feon = DDebtRatio, Monthlylncome! and FA = Saap:iiinuUuliiattooooossseeeee tio, MonthlyIncome, NumberOffOpenCredttlii Loans NumberQffiimes00aaytla Num- For COMPAS dataset, Foon (priors-count, length-of-stay) and FA = (two-year-recid, priors-count" length-of-stay). For Adult dataset, Foon (education-num, capital-gain) and FA leducation-num, capital-gain, capital-loss, hours-per-week, workclass-Non-Priin workclass- Private, marital-status Married, naridllstatus--Nn--Maarra occupation-Other;
DebtRa-
berRedlFstatel.onnSOro..
accupation-Manngeial--Seeee
With these experiments we conclude that UP-AR's I deviation from the user's I' is consistently lower than the existing recourse generation methodologies. We observe that AR is unaffected by the varying
preference due to the fact that AR and other state-of-the-art recourse methodologies lack the capability of capturing such idiosyncrasies. On the other hand, UP- AR is driven by those preferences and has significantly better pRMSE in comparison to AR.
0.3
0.25
0.20
pRNSE
0.1
0.0
COHNNE
Face
We
AR
COHNA
Face
UP-AR
GMSC
GMSC
0.25
DRMSEE2
0.20
DRMSENS
0.05
65
Ws
AR
CCHNAE
Face
UP-AR
G5
Wa
OCHVAE
Face
UP/AR
COMPAS
COMPAS
0.15
pRMSE.0
DRMGE
COHNAE
Face
LP-AR
G5
We
OCHNNE
Face
UP-AR
Adult
Adult
Figure 7: Logistic Regression model
Figure 8: Neural Network model
Figure 9: Distribution of the average pRMSE of UP-AR and other recourse methodologies.
C.3 ABLATION STUDY ON T
For the following experiment, we again consider a similar setup as in Section 4 Each data point in the plot represents the mean total cost of recourses for the target population for 20 independent runs of UP-AR, and the shaded region represents the + standard deviation of the 20 runs. We observe:
1. Effect of calibrating the overall cost of target population using controls the frequency categorical actions detailed Section 3.I.I
2. I' LounDuration is not affected by any setting of r as observed in Figure
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2000
1500
FLaandration
0.8
Total Cost
0.6
1000
500
0.2
0.0
1
2
3
4
5
6
1
2
3
4
5
6
1/T
1/T
Figure 10: Total cost of the recourses generated for target population for varying The user preference scores are fixed for the individuals.
Figure 11: Mean fractional feature cost ratio of LoanDuration for varying т. For this experiment, T LounDuration is set for the target population.
C.4 RELATION BETWEEN I AND
visualizes the relation between the observed I' Loon and the number of steps taken to identify a recourse T*, We conclude that I LLonDDuration Duration not affected by the number of steps taken to identify a recourse by UP-AR.
Again considering similar setup as Section Figure 12
1.0
LoanDuration
0.6
0.4
0
100
200
300
400
T
Figure 12: Scatter plot between L'onnn Duration and T the recourses generated for adversely affected target population.
C.5 REAL COST VS EXPECTED COST
In this experiment, we compare the expected cost and the actual observed cost of the recourses gen- erated. Figure 13 visualizes the expected cost and observed cost for actionable features. We observe that with increasing т, the total cost of recourses increases suggesting high categorical actions sug- gested in the generated recourses. Additionally, We also notice the consistency in I L.oon varying т. Please note that careful calibration of can help individuals who prefer categorical feature actions over continuous features.
C.6 ABLATION STUDY ON CC
5 we explore the effect of UP-AR's cost correction procedure on the Adult and COMPAS datasets. We do not include the GMSC dataset as it does not include binary features, and therefore does not utilize the cost correction procedure. Table 5 we show the number factuals, the per- found, the percentage of recourse found which contained
centage of factuals for which recourse at least one binary action, the percent of recourse found which underwent cost correction, the av- erage percentage of steps saved by the cost correction procedure, and the average percent of cost
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300
Elcost(a; x)) :/ Fron
cost(ai, Xi) :/ E Fcon
200
Cost
100
0
40
80
100
Individuals
Figure 13: Expected and observed cost of modifications Feon for all the recourses generated on the adversely affected target population.
savings, measured as the percent reduction in continuous cost (l2 distance) between a factual and its recourse before and after the cost-correction procedure.
Table 5: The Frequency and Effect of Cost Correction
Metrics
Adult
COMPAS
Number of Factuals
1000
568
Success Rate
79.38
99. 68
Percent of Recourse with a Binary Action
71.98
82.6%
Percent of Recourse with Cost Correction
38.4%
25.5%
Average Percentage of steps Saved
67.9%
63.5%
Average Percentage of Continuous Cost Saved
83.18
76.0%
C.7 ABLATION STUDY ON ACTIONABLE FEATURE SET
We conducted an experiment on the average computational cost (modeled by execution time) of UP-AR and GS across varying number of actionable features to explore how their perfor- mance changes as the actionable set size increases. Figures 14 and [5]show the performance trends for an LR model and NN model on the Adult Income dataset, while figures I6 and I7 show the performance trends for an LR model and NN model on the German Credit dataset.
Avg. Time Taken
12
UP-AR
0.066
Avg. Time Taken
Avg. Time Taken
12
UP.AR
0.055
Avg. Time Taken
1.0
0.050
0.042
0.8
0.045
0.6
0.038
0.040
0.4
Number of Actionable Features
Number Actionable Features
Figure 14: Average time to find recourse for an LR model on the Adult dataset with a variable number of actionable features.
Figure 15: Average time to find recourse for NN on the Adult dataset with a variable number of actionable features.
We observe that UP-AR's Rss average time increases as the actionable feature dimension increases whereas gradient based GS remains relatively consistent. This can be attributed to the addi- tional user scoring preference and ranking preference constraints while identifying a recourse, well as the cost correction procedure the number of binary changes increases.
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0.0225
Avg. Time Taken
UP-AR
Avg. Time Taken
Taken
UPAR 00230
Taken
0.0220
0.0225
0.0215
02
0.0210
Avg.
0.1
0.0220
Avg.
0.0205
0.0215
Number Actionable Features
Number Actionable Features
Figure 16: Average time to find recourse for an LR model the Credit dataset with variable number of actionable features.
Figure 17: Average time to find recourse for NN the Credit dataset with variable number of actionable features.
C.8 ADDITIONAL PROOFS OF RESULTS DISCUSSED IN SECTION 4
C.8.1 PROOF OF LEMMA
Consider that recourse suggested by UP-AR for Alice represented by feature vector Let (8)
r was obtained at time step 7 Here cost
X; + r; ) measures the cost of taking an action
at time for feature i.
T
1 cost (I,(1) (r; ;; + r r(((-11)
coott (I,, x,)]
E
I
(0)
cost (t) +r;
+x( 11(-11)
E
1=
Σ
P (100 cost T x, +(((t(
Steps for each feature action at time t are decided by the inverse cost weighted by user preference score r, Let call this weighted inverse cost which is then mapped to probability distribution using usual choices such as normalization or a softmax function. Let (-) be a function which maps weighted inverse cost a probability distribution. We have,
T
E [cost (r,, x;)
Σ
r
cost (r: Xi + +
+rrr( )
cost
With C.TT) immn and C (T')
imar representing the minimum and maximum cost of any step for feature until
time step T we obtain the result in Lemma
C.8.2 PROOF COROLLARY
For simplicity, consider a cost function where the overall cost of recourse is the sum total of individ- ual feature action costs, i.e., cost (r,x). x) LieFy cost (ri, x;). The total expected cost of a recourse r is:
E [cost (r,x)]=
cost (r.0) x; + r (!-11)
(t)
+ r ((11)
1ET REFA
cost
Considering that all the steps are of equal cost and linear function ((:), we get Corollary
E [cost (r, x)] T Σ a (T,)
SEFA
18


Reviews:
[{"review_id": "y4WvCNWEEvb", "reviewer": "Reviewer_7UMN", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "", "summary_of_the_paper": "This work studies Actionable Recourse (AR) and proposes to incorporate user preferences as constraints into the recourse generation process. The authors propose three forms of user preferences: scoring continuous features, bounding feature values, and ranking categorical features. An optimization approach is provided to find the recourse. Numerical comparisons were conducted to evaluate the proposed method.", "summary_of_the_review": "This work proposes an interesting idea that models three user preferences for actionable recourse and designs an optimization method to generate the recourse. Some analysis is given and experiments were conducted on 2 datasets to verify the effectiveness. Nevertheless, I have some concerns (see Weakness) that need to be addressed by the authors. ", "strength_and_weaknesses": "Strength: This work provides three types of user preferences and adopt them as soft constraints into the actionable recourse formulation. This idea is interesting and has shown helpful in generating actionable recourse. A two-stage UP-AR and some theoretical analysis are also provided to consolidate this work. \n\nWeakness: \n1) In Tab 5, though UP-AR performs well in terms of indices such as Sparsity and Redundancy, the success rate is not always the highest among baselines. For example, on the Adult dataset, its success rate is even much worse than other methods. I am wondering whether it’s fair to make a comparison among different methods under this setting.\n2) This work adopts the logistic regression model during experiments evaluation, while more sophisticated models remain unexplored. \n3) Though user preference is interesting, I’m not clear whether such information is always available to be used. This may limit the applicability of the method.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "_uvQ2onKRg", "reviewer": "Reviewer_zS7o", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper studies how to generate actionable resources for different individuals, with their specific individual user-based preferences. Specifically, it discusses three types/formats of user preferences: (1). Scoring for continuous features; (2). Ranking for categorical features as well as (3). Bounding for feature values. Based on these three types of constraints, it proposes an optimization framework which minimizes the cost of the overall change. It further instantiated the method with a UP-AR algorithm, which consists of two stages: candidate generation using gradient-descent, followed by the redundancy and cost correction. Empirical results also show superior performance compared with baseline.", "summary_of_the_review": "This paper is a nice paper in studying effective methods to incorporate user-level preference into the actionable resource optimization problem. The proposed method is novel and well-motivated. Empirically, it also shows strong performance compared with baselines. However, there are some issues in the current set-up of the experiment, such as the universal user-preference, the availability of the user-preference, etc. ", "strength_and_weaknesses": "Strength:\n\n<1>. This paper is very well-written and well-motivated. I enjoy reading the paper and learning from it.\n\n<2>. The problem setup it studied is novel, which considers the usage of user-preference in actionable resource optimization, and it is closely related with lots of real-world scenarios. \n\n<3>. The proposed method is solid, easy to implement, and shows great empirical performance.\n\nWeakness:\n\n<1>. The main argument of the paper is the individual user-level performance, and the proposed method is tailored to this. However, in the experiments, if i understand it correctly, it seems the set-up uses the universal user preference function? This might limit the argument from the empirical experiments. Could the authors comment more about this?\n\n<2>. The current empirical set-up for UP-AR is based on linear models, could the authors comment more about the feasibility of scaling it to more complicated neural networks? \n\n<3>. Another aspect is the actionable feature dimension, how the proposed method scale with that?\n\n<4>. Getting user preference might be tricky in real-world settings, could we utilize any signal from training data to get this information? Could the authors comment more on this?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Ir3XMaQFi3", "reviewer": "Reviewer_uWp4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "main_review": "", "summary_of_the_paper": "Most recourse generation methods optimize for properties such as proximity, sparsity, validity, etc, but often ignore the user’s preference. Recent effort in taking into account the individual preference requires a shared cost function on feature changes or user-specified feature change costs that can be burdensome on the users. The authors propose to capture user preferences in terms of user-specified- scoring for continuous features, bounds on feature values, and ranking of categorical attributes. The fractional scores on continuous attributes allow the user to indicate the contribution of each feature on the overall cost. These preferences result in a customized optimization problem that is solved by the UP-AR algorithm. The UP-AR algorithm is a coordinate wise gradient descent/ascent where the step size is influenced by the user-specified cost. The authors also propose a roll-back mechanism to reverse changes to features that are unnecessary due to a change to a categorical attribute. Experiments conducted on benchmarking datasets indicate the effectiveness of the proposed approach.\n", "summary_of_the_review": "Overall, the paper has interesting ideas that might help to push the frontiers of algorithmic recourse research. However, I have major concerns related to the preference definition, guarantees of the algorithm and lack of experiments. \n", "strength_and_weaknesses": "Strengths\n\nGenerating recourses that satisfy user-preference is an important research problem. While the cost function is shared across all the users, the authors propose a novel way to define a user’s preference in terms of contribution of a single feature to the overall cost. \n\nThe proposed technique is sound and quite easy to understand. The authors show that the expected cost of a feature is proportional to the user-preference. \n\nExperiments on multiple datasets indicate that the proposed method is capable of generating recourses with minimal constraint violations.\n\nWeaknesses\n\nThe fractional-score based user-preference, although novel, may not be as simple and easily expressible as proposed, especially, when the number of continuous features is large. Moreover, the preference input process puts an extra cognitive burden on the users. Evidence supporting the acceptability of such a preference elicitation process will go a long way in strengthening the paper. Separating continuous and categorical features may result in a manageable modeling task, but I wonder if a user would find it difficult to separate the two sets of features while providing the preferences.\n\nHow are the continuous features discretized? Does this have a bearing on the algorithm? \n\nI recognize the challenges in modeling heterogeneous features (categorical and continuous). However, I am not convinced with the automatic assumption that the fractional cost of categorical features changes should be higher than that of continuous features (for all categorical features listed in the user’s preference rank, $ \\Gamma_i = 1 $). A user might actually prefer changes to continuous features over categorical features. How does UP-AR handle such situations?\n\nWhile the proposed method ensures that the expected cost follows the user-preference, I am unsure how this translates into practice. A user would expect a single (or at best a few diverse) recourse(s) satisfying the indicated preferences. Thus, usability of UP-AR is significantly hampered, as the preference compatibility of a single recourse cannot be guaranteed. \n\nI am also concerned about the scanty experiments on preference-enforcement. The authors’ have shown pRMSE results only for single features (LoanDuration and DebtRatio). Evidence on other features, perhaps an average over all continuous features could have been reported for better insights into the empirical effectiveness of UP-AR.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject