Title: Grounding and Validation of Algorithmic Recourse in Real-World Contexts: A Systematized Literature Review
Venue: NeurIPS 2024
Accepted: no
Keywords: Algorithmic Recourse

Counterfactual Explanations

Explainable AI

Real-world Systems
Abstract: The aim of algorithmic recourse (AR) is generally understood to be the provision of "actionable" recommendations to individuals affected by algorithmic decision-making systems, in an attempt to offer the capacity for taking actions that may lead to more desirable outcomes in the future. Over the past few years, AR literature has largely focused on theoretical frameworks to generate "actionable" counterfactual explanations that further satisfy various desiderata, such as diversity or robustness. We believe that algorithmic recourse, by its nature, should be seen as a practical problem: real-world socio-technical decision-making systems are complex dynamic entities involving various actors (end users, domain experts, civil servants, system owners, etc.) engaged in social and technical processes. Thus, research needs to account for the specificities of systems where it would be applied. To evaluate how authors envision AR "in the wild", we carry out a systematized review of 127 publications pertaining to the problem and identify the real-world considerations that motivate them. Among others, we look at the ways to make recourse (individually) actionable, the involved stakeholders, the perceived challenges, and the availability of practitioner-friendly open-source codebases. We find that there is a strong disconnect between the existing research and the practical requirements for AR. Most importantly, the grounding and validation of algorithmic recourse in real-world contexts remain underexplored. As an attempt to bridge this gap, we provide other authors with five recommendations to make future solutions easier to adapt to their potential real-world applications.

Abstract

The aim of algorithmic recourse (AR) is generally understood to be the provision of 'actionable' recommendations to individuals affected by algorithmic decisionmaking systems, in an attempt to offer the capacity for taking actions that may lead to more desirable outcomes in the future. Over the past few years, AR literature has largely focused on theoretical frameworks to generate 'actionable' counterfactual explanations that further satisfy various desiderata, such as diversity or robustness. We believe that algorithmic recourse, by its nature, should be seen as a practical problem: real-world socio-technical decision-making systems are complex dynamic entities involving various actors (end users, domain experts, civil servants, system owners, etc.) engaged in social and technical processes. Thus, research needs to account for the specificities of systems where it would be applied. To evaluate how authors envision AR 'in the wild', we carry out a systematized review of 127 publications pertaining to the problem and identify the real-world considerations that motivate them. Among others, we look at the ways to make recourse (individually) actionable, the involved stakeholders, the perceived challenges, and the availability of practitioner-friendly open-source codebases. We find that there is a strong disconnect between the existing research and the practical requirements for AR. Most importantly, the grounding and validation of algorithmic recourse in real-world contexts remain underexplored. As an attempt to bridge this gap, we provide other authors with five recommendations to make future solutions easier to adapt to their potential real-world applications.

1 Introduction

Algorithmic decision-making (ADM) tools are frequently seen as a way to improve decision processes 23

in a variety of high-stakes domains such as public administration [47, 146] or healthcare [45, 87]. 24

Deep learning models have attracted much attention due to their perceived high performance, but 25 the predictions of such models cannot be interpreted by humans, hence end users - both individuals 26 subjected to algorithmic decisions and decision-makers operating on them - are placed in a position 27 where they are unable to understand the grounds of a prediction, act on it, or trust it [159]. 28

To help address this problem, a variety of explanation methods has been proposed. Of particular 29

interest for this paper are counterfactual explanations (CEs) that attempt to explain the predictions for 30

individual instances of data, taking the form of conditional statements such as 'if the value of feature 31

x was a instead of b , the model would have predicted class y instead of z ' . They are perceived to be 32

an attractive approach to explanation that does not require 'opening the black box' [151] and have 33

been argued to align with the ways that humans naturally reason about events [84]. 34

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CEs are also seen as the go-to method for algorithmic recourse (AR), or the generation of actionable recommendations that provide people with the knowledge needed to achieve more desirable predic-

tions in ADM systems. Recourse is distinct from the 'explanation' or 'justification' of algorithmic decisions, and more closely related to the notion of contestability of Artificial Intelligence [7] in that

it aims not only to improve the trust

in the algorithm, but also embrace human agency

[142].

Algorithmic recourse is an inherently practical problem in that it resembles a bureaucratic complaint process: an individual unhappy with some decision engages with a representative of the issuing organization, in an attempt to overturn it. Yet, we observe that much of the existing work is highly theoretical, with little consideration of whether it could be applied in organizational settings [see also 18]. Deploying AR in realistic systems without analyzing its mechanics in a broader context and without knowing what types of dynamics are expected to arise is bound to lead to unanticipated outcomes. Many of them will be undesirable and even potentially unsafe, and impossible to validate with respect to a set of requirements because the requirements for AR are necessarily socio-technical.

Societal and institutional components of algorithmic recourse are the focal point of our work , as we look beyond the typical technical considerations to assess the practical aspects of the problem.

To that end, we contribute a systematized review of 127 publications that address the goals of algorithmic recourse and we evaluate to what extent they incorporate such practical considerations. We characterize our approach as systematized because we follow a fully systematic approach to the collection of publications, but their selection is not necessarily exhaustive [46] as many impactful ideas in computer science are published only in the form of pre-prints. Based on our analysis, we also provide other authors with five recommendations on how to improve the practicality of AR research.

The rest of the manuscript is structured as follows. In Section 2 we elaborate on the background of our work. Then, in Section 3 we describe our approach to this review. Next, Section 4 introduces our findings. Section 5 provides a discussion of our results, introduces our recommendations, and addresses the limitations of the current work. Finally, Section 6 forms the conclusion to this paper.

2 Background

2.1 On algorithmic recourse

Algorithmic - or actionable, individual - recourse was introduced in [138] as 'the ability of a person to change the decision of the model through actionable input variables' , building on the earlier work of [151] who argued that CEs are a psychologically-grounded way to (1) help decision-subjects understand an algorithmic decision, (2) provide them with information needed to contest it, and (3) inform about actions that could be taken to overturn it. For instance, consider a person who has unsuccessfully applied for a loan; they may then receive AR such as 'if you requested $5000 less, you would qualify for this loan' . The key consideration for AR is 'actionability', which entails that the recipient of the recommendation should be capable of implementing it. If they had been informed 'if you were 10 years younger, you would qualify for the loan' , they would have still received a valid CE, but not recourse. More recently [69] has recast the problem as reasoning about minimal interventions on the structural causal model. This formulation (at least theoretically) addresses an important shortcoming of 'correlational' recourse. Without accounting for the downstream causal effects of actions, an individual may exert more effort than necessary and still fail to achieve the target outcome. Indeed, counterfactuals are an inherently causal concept [103].

We note that problems similar to AR have been studied under a variety of different names: actionable knowledge discovery [e.g., 2], action rules mining [e.g., 110], inverse classification [e.g., 5], why not questions [e.g., 58], or actionable feature tweaking [134]. These alternative formulations have generally focused on 'business' knowledge, rather than individual recommendations, but ultimately the goal of all these approaches is to extract information from a (black-box) model that allows the user - an individual or a decision-maker - to act. We highlight them to emphasize that AR does not have to be achieved through the means of CEs. Rather CEs should be seen as one of the means to achieve AR, particularly promising in that they do not require expert-level understanding of the model to be useful. Nonetheless, we decide to distinguish between the literature on AR (commonly equated with actionable CEs), and these alternative formulations in our work.

Existing research has generally considered AR in simplistic settings that are far removed from 86 real-world socio-technical decision-making systems, where it would be implemented as a process. 87

For example, such systems are dynamic [113, 137], must support the implementation of AR at scale 88 [9, 94], and involve various stakeholders beyond the end users [17, 151]. Moreover, if the intended 89 goal of AR is to help individuals subjected to algorithmic decisions in an effective manner, research 90 must entail a rich understanding of 'actionability' to account for the differences between them [142]. 91

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2.2 On the position of our review

Several groups of authors have previously surveyed the landscape of counterfactual explanations in general, and algorithmic recourse specifically. Perhaps the most relevant to our work is [71], which discusses five deficits of research on CEs, with a special focus on the (lack of) psychological grounding. Another pertinent publication is [70], which attempts to unify the definitions and formulations of AR in existing literature, but the work primarily focuses on technical aspects. Next, [143] develops a rubric to compare counterfactual explainers (equated with AR) and identifies 21 research challenges. While these also remain mostly technical, several of them are relevant to our work, for instance, CEs 'as an interactive service to the applicants' or reinforcing 'the ties between machine learning and regulatory communities' . More recently, [48] reviewed and benchmarked a number of CE generators, but AR is only a secondary consideration in the work. We also highlight [130], which is the only systematic review of counterfactual and contrastive approaches to date. The authors understand CEs as a way to justify model predictions (i.e., they are different from AR). We agree with this distinction in that CEs can be useful for reasons other than recourse, such as model debugging [e.g., 1, 122]. Finally, although not reviews, [13] and [142] are particularly relevant to our work, offering critical perspectives on AR and addressing multiple shortcomings of recourse literature.

3 Methods

In this section, we briefly discuss our approach to the literature review following the SALSA - Search, Appraisal, Synthesis, Analysis - framework introduced in [46]. We also provide a more detailed description to allow for the reproduction of our process in the supplementary materials. Figure 1 presents our process in the form of a PRISMA flow diagram [97].

3.1 Search

We make use of three search engines to collect the initial set of studies: ACM Digital Library, IEEE Xplore, and SCOPUS. Given the previously mentioned blurry distinction between AR and CEs, we consider the papers discussing either problem. In a small scoping review, we identify several keywords common to publications on recourse, as well as several equivalent terms to build the query. We search in titles, abstracts, and keywords, arriving at 3092 records after de-duplication. To facilitate the screening process, we employ the open-source ASReview tool, which makes use of an active learning approach to re-order the set of publications, such that the most relevant ones are always 'at the top of the stack' [139]. The researchers behind the tool suggest employing a stopping rule measured in the number of consecutive irrelevant records, which we set to 30, or 1% of the entire dataset. We accept all papers that focus on algorithmic recourse and counterfactual explanations, completing the screening after evaluating 1040 abstracts, leading to 499 relevant records.

We observe that some important publications may be missing from our results. For instance, [151] was published in a legal journal that is not indexed by computer science search engines. Thus, we decide to augment the set of records by applying snowballing, which has been shown as a good alternative to databases in systematic reviews in software engineering [162]. We collect the references for the top 50 (10%) 'most impactful' publications, measured by the number of citations. While this introduces several pre-prints into our result set [52, 61, 91, 113, 143, 150], we decide not to exclude them. Our review remains primarily concerned with peer-reviewed work. After adding the snowballed references to our dataset, we are left with 2018 records for the second screening with ASReview. This time, we look for publications that specifically refer to the problem of AR, 'actionable' CEs, or modifying outcomes of automated decision-making systems. We employ a stricter stopping rule to minimize the risk of false negatives, completing the screening after 60 consecutive irrelevant records with 203 records considered for full-text appraisal. To allow for complete reproducibility of the search process, we provide an extended discussion (including queries) in the technical Appendix A.

138

3.2 Appraisal

We were able to retrieve all of the remaining 203 documents. For each document, we require that the 139 authors explicitly cite recourse as the center of interest, or look at (1) explanations (2) provided for 140 individual instances (3) with the goal of acting upon them (4) in an attempt to modify the predictions 141 (5) of a classification model. We exclude 51 publications as they are not on topic, primarily because 142 they focus on CEs for the sake of explanation. Four works in this category look at (what they 143 call) recourse but extend the problem to settings beyond the scope of this review: recommender 144 systems [31, 43, 145], text classification [37], and anomaly detection [27]. Further 15 publications 145 are duplicates, typically pre-prints of other documents that were included in the review. Next, 8 146 documents were published before [151] that sparked the research on AR, and thus we exclude them as 147 well. These look at the alternative formulations discussed earlier in Section 2.1. Finally, 2 documents 148 are not publications: one is an abstract of a talk, and the other is a student poster. For each document, 149 we answer a number of questions relating to the practical considerations introduced by the authors. 150

Identification

Records identified from:

•

ACM Digital Library (

n

= 1267

)

•

IEEE Xplore (

n

= 513

)

•

SCOPUS (

n

= 2139

)

Records removed before screening:

•

Duplicates (

n

= 783

)

•

Only meta-data (

n

= 44

)

Screening

First screening by abstract (

n

= 3092

),

screening completed after

1041

records

Records excluded (

n

= 2593

),

after encountering

30

irrelevant records

Records from snowballing (

n

= 1519

),

using top

10%

of records by citation count

Records before second screening (

n

= 2018

)

Second screening by abstract (

n

= 2018

),

screening completed after

538

records

Records excluded (

n

= 1815

),

after encountering

60

irrelevant records

Records assessed for eligibility (

n

= 203

),

appraisal using the full text

Records excluded (

n

= 76

):

•

Not on topic (

n

= 51

)

•

Duplicates (

n

= 15

)

•

Year of publication (

n

= 8

)

•

Other formats (

n

= 2

)

Included

Studies included in the review (

n

= 127

):

3, 4, 6, 8, 9, 12-16, 19-22, 24-26, 28-30, 34-36, 38, 39, 41, 42, 44, 48-57, 60-67, 69, 70, 72-83,

85, 86, 88-96, 98-102, 104-109, 111-118, 120, 121, 123-129, 131-133, 135-138, 141-144, 147-

158, 160, 161, 163-165

3.3 Synthesis

151

To compile the results we carry out a standard thematic content analysis following the approach 152 presented in [40]. First, we explore the data extracted from the set of publications relevant to each 153 question to find the commonalities, which serves as the grounds for creating the initial set of codes. 154 We evaluate the documents against these codes and keep track of any other considerations. If such 155 considerations appear in multiple documents, we create new codes for them. Afterward, we re156 evaluate all documents against the new code. As the coding exercise is carried out by one author, they 157 do a third pass over all documents to double-check for potential errors. Finally, where relevant, we 158 cluster the codes into larger themes. In this analysis we only look at the explicit statements provided 159 by the authors, we do not attempt to infer their understanding of the problem. Thus, the numbers 160 provided in Section 4 should be understood as describing how algorithmic recourse is discussed in 161 the literature. For brevity, we focus our discussion on the main themes, but we still highlight specific 162 publications if we observe that the authors introduce novel, highly relevant considerations that do not 163 fit into other themes. Finally, even though we also evaluated the technical aspects of the proposed 164 solutions - requirements for methods and datasets used in evaluations - they are not covered in this 165 review. Instead, we point the interested readers to [48, 70, 143]. 166

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4 Results

The following nine sections introduce the results of the thematic analysis. For each question, we explain why it is relevant to the analysis and examine the main themes. We also highlight highly important but underexplored themes. We start with the general points such as contributions and definitions in Sections 4.1 to 4.3. Then, in Sections 4.4 to 4.7 we investigate the societal components of AR research. Finally, in Sections 4.8 and 4.9 we look at the aspects relevant to practitioners.

4.1 What types of contributions do the authors choose to make to the AR research?

We start by looking at the main goals of the collected publications to validate our assumption that AR literature is primarily concerned with technical solutions. We annotate each entry with at most two codes based on the form of contributions. By far the largest group is propose methods , which applies to 88 (69.3%) out of the 127 publications. These are primarily generators for individual CEs, but we also find 18 (14.2%) documents that propose other methods. Next, 20 (15.7%) publications develop theoretical frameworks , for instance by grounding AR in user studies or providing critical perspectives on the problem. Further, 15 (11.8%) focus on empirical or theoretical analyses of the properties of AR and another 15 publications apply it in a variety of domains. We did not identify any applications evaluated with humans in the loop. Then, 5 (3.9%) publications benchmark existing methods, while 3 (2.4%) review them. We make our annotations available in technical Appendix B.

4.2 What are the criteria covered in the authors' definitions of AR?

We also evaluate what is understood as the problem to be addressed by AR mechanisms. In particular, what are the criteria to satisfy authors' definitions of recourse. A similar question was posed by [70] who combined six definitions into 'recourse can be achieved by an affected individual if they can understand and accordingly act to alleviate an unfavorable situation, thus exercising temporallyextended agency' , but this approach was far from systematic. Instead, we are interested in the underlying concepts. 74 (58.3%) publications explicitly define AR, 16 (12.6%) mention it but do not include a definition, while 37 (29.1%) do not mention AR, even though they align with its (overall) goals. The most common theme is overturning undesirable decisions , present in 47 definitions (63.5% of all definitions), but specifically overturning algorithmic decisions is mentioned only 43 (58.1%) times. It is generally understood that AR is provided to affected individuals (44, or 59.5%) but 4 (5.4%) definitions consider stakeholders more broadly. Actionability as a requirement for recourse is noted in only 39 (52.7%) definitions. Then, 20 (27.0%) publications specifically mention counterfactual explanations as means to AR, while 26 (35.1%) include various other technical considerations in the definitions, such as 'changes to actionable input variables' or 'desired classes'.

We also point to several themes that are, interestingly, underrepresented. Only 18 (24.3%) documents mention explanation, justification, or understanding of a decision as the pre-requisite for AR. Next, 10 (13.5%) highlight future-orientation or other temporal aspects of the provided recommendations. Although 'consequential settings' , typically bank lending, are given as examples in nine (12.2%) definitions, they are never explicitly mentioned as the scenarios where recourse ought to be provided, which may be akin to the 'enjoyment of recourse' as defined by [142] where people are aware that there exists a way to reverse undesirable decisions. 1 8 publications (10.8%) promote AR as an ability . Finally, only 2 (2.7%) publications require that recourse accounts for the preferences of its recipients.

4.3 What are the criteria covered in the authors' definitions of actionability?

As we observe, 'actionability' is a concept that underpins AR but we discover that, in general, its understanding is limited. 91 (71.6%) publications attempt to define what it means (for a CE) to be actionable. Most commonly, in 48 (52.7%) out of 91 definitions, it is understood as acting only on directly-mutable features , 6 (6.6%) distinguish that features may be indirectly-mutable but still not actionable, while 22 (24.2%) also highlight that feature values may need to be constrained . Next, 19 (20.9%) definitions rely on a tautology that actionability means people can take actions , 11 (12.1%) emphasize that these actions must be successful or lead to change , and 3 (3.3%) further require that they are aligned with people's real-world objectives . Only 14 (15.4%) definitions put users

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at the center stage, indicating that actionability depends on the user or their preferences , while 2 (2.2%) highlight the importance of the context [144, 156], for instance, that the ability to act on a recommendation may change over time. Importantly, ethical considerations are never mentioned as the pre-requisite for actionability, but we find some broader discussions about this [e.g., 142].

4.4 What is the role of end users? What other stakeholders are envisioned in the AR process?

Given that AR is to be implemented in socio-technical systems that include a variety of actors, we are interested in the types of stakeholders acknowledged in the literature. A total of 105 publications provide explicit consideration of this type. In general, end users subject to algorithmic decisions are envisioned to be the recipients of AR, but this is not always the case: it may also be provided to experts [e.g., 21, 22, 76] or organizations [e.g., 65, 72, 147], which highlights that in some cases AR may be carried out on behalf of the affected individuals. In any case, 47 (44.8%) publications in the subset agree that end users should inform actionability, but it is rarely clear how these preferences should be specified. User-friendly (interactive) interfaces are a consideration in only 14 (13.3%) documents. A total of 29 (27.6%) publications envision domain experts as someone who inform the recourse process. They are either expected to inform actionability in the AR system or provide other forms of knowledge, typically in the form of a causal structure. Besides the experts, authors of 35 (33.3%) papers have discussed a variety of stakeholders. Most commonly system owners [e.g., 20, 34, 38, 89], but also auditors [e.g., 138, 158], data scientists [e.g., 28, 82], developers [e.g., 22, 131], practitioners [e.g., 100, 156], regulators [e.g., 28, 120], or even potential attackers [102].

4.5 What types of real-world considerations motivate existing research?

With the multitude of challenges that stand ahead of real-world AR, we are interested in the considerations that motivate existing work. The main theme we find is ensuring proper individual actionability , which is addressed in 46 (37.4%) of 123 publications relevant to this question. This is typically achieved with the encoding of user preferences as constraints, but other means include providing diverse CEs. In fact, tackling specific desiderata for AR (beyond actionability) is the second largest area of research with 28 (22.8%) publications. Various other technical challenges are considered in 24 (19.5%) documents, for example, integrating background knowledge [e.g., 16, 62, 64, 98], or incorporating feature importance [e.g., 4, 6, 96, 116]. We also find 19 (15.4%) publications that discuss the problem of communicating recourse to the end users . 16 (13.0%) focus on the dynamics of real-world systems , typically addressing the robustness of AR [e.g., 75, 91, 93, 137], while 14 (11.4%) look at recourse in multi-agent systems . This also relates to performance considerations emphasized in 15 (12.2%) of documents. Causality drives research in 14 (11.4%) cases. We also find several themes that are under-emphasized: only 9 (7.3%) publications are directly motivated by research in psychology , while ethics of AR are emphasized in only 7 (5.7%) documents.

4.6 What types of real-world considerations are seen as challenges for future work?

While the previous section looked at the considerations that drive existing research, in this section we distill the recommendations for future research going beyond the improvement of own work, which are provided in 74 documents. Causality is highlighted as a challenge in 22 (29.7%) of them, while other technical considerations are given in 20 (27.0%) cases. These range from robustness [e.g., 51, 117, 137], support for categorical features [e.g., 36, 157], or distinguishing between valid CEs and adversarial examples [101]. Next, 19 (25.6%) documents highlight the importance of ensuring proper individual actionability , which also relates to communicating recourse to the end users (9, or 12.2%) and supporting realistic cost functions (8, or 10.8%). Ethics of AR are highlighted in 11 (14.9%) publications, for example, that AR research may detract from other obligations of model owners [77, 133]. The same number of publications emphasize the need to (1) ground research in user studies , and (2) accommodate for the dynamics of real-world systems . Privacy or security is highlighted in 10 (13.5%) documents, while the abuse of recourse , such as strategic behaviors, surfaces in 7 (9.4%) papers. Other challenges include improving performance (8, or 10.8%), considering multi-agent systems (4, or 5.4%), and developing legal frameworks (4, or 5.4%) for recourse. We also highlight several challenges particularly relevant to our work: (the usefulness of) recourse is perceived as difficult to evaluate in practice [41, 60, 115], it must account for individual, contextual, societal, and even cultural factors [123], which further means that engagement with recourse mechanisms and the likelihood of its implementation are context-dependent [e.g., 6, 42, 128].

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4.7 What types of (emergent) group-level dynamics are addressed in the existing research?

Real-world systems entail the implementation of recourse by more than one agent, which may introduce group-level dynamics. Nonetheless, out of 119 documents relevant to this question, 93 (78.2%) seem to understand recourse as a purely individual phenomenon. Among the remaining 26 documents we find considerations for several different group-level effects. Various perspectives on the problem of fair AR, covering both individual and group formulations are addressed by [12, 36, 52, 120, 121, 131, 149, 154]. Next, [9] shows that the implementation of AR on a large scale may lead to domain and model shifts, which introduce unexpected costs for the stakeholders. 2 In [42] the authors focus on another negative consequence of AR at scale, showing that it may reinforce social segregation. The impact of the 'right to be forgotten', where data deletion requests trigger model retraining that may invalidate existing recourses is addressed in [75]. Then, [94] develop a game-theoretic framework for AR in multi-agent settings, attempting to optimize for 'social welfare' rather than the profits of individual agents. We find two further similar perspectives on recourse: [38] proposes auditing and subsidies to minimize the risks of strategic behaviors in a multi-agent setting, while [136] attempts to incentivize actual improvement for a population of agents. Finally, [65] provides a framework that generates transparent and consistent recourses for a sub-population. We also note two other lines of research that account for the remaining documents with group-level considerations. First, in a causal setting [e.g., 68, 73] subpopulations are necessary to estimate the interventional effects on individuals. Second, several works highlight the importance of global insights into the data [22, 41, 44, 78, 108, 112, 152], such as recourse summaries [78, 112].

4.8 What are the approaches to the realistic evaluation of proposed methods?

We now explore the different forms of 'real-world' evaluations, going beyond quantitative experiments, which are present in 51 publications. Most commonly, in 28 (54.9%) of those, the authors make use of case studies presenting the methods in an end-to-end manner. Among those, the application of recourse in the Hired.com marketplace goes furthest in simulating real-world conditions for AR [89], but the recommendations are still not evaluated with humans in the loop. Further, 9 (17.6%) documents include other forms of short walk-through examples . We also identify 14 (27.5%) papers that evaluate the methods with user experiments , 10 of which involve non-expert users and 4 involve expert users. While we do not observe any interviews with non-expert users, we find 1 (2.0%) publication where experts are interviewed [22]. Other involvement of non-experts applies to [116], where they inform the development of methods. Other involvement of experts is featured in two documents where they evaluated the outputs of methods [25, 132]. Altogether, end users were involved in 17 publications, which is only 13.3% of all publications covered in our study, even more striking than the 21% of CE methods evaluated with user studies as reported in [71].

4.9 What are the open source and documentation practices in AR research?

Finally, we note that the lack of availability of well-documented open-source code may be an important obstacle to the application of AR in real-world systems. For all 116 publications that involve some form of computational experiments, we verify whether the source code is publicly available. If the authors do not explicitly link to their code in the paper, we attempt to find it independently. Ultimately, we collect open-source implementations for 64 (55.2%) publications. Then, for each of them, we evaluate the quality of documentation. The instructions on the general usage (such as installation and workflow) are provided with 27 (41.5%) repositories, while instructions on the reproduction of results in 23 (35.4%). In 19 (29.2%) cases we find walk-through tutorials , typically in the form of Jupyter Notebooks, although we note that they differ in quality. For instance, 5 repositories include code-only notebooks with no further textual explanation that could guide the practitioner. Implementations for 4 papers include more 'professionalized' documentation [9, 86, 100, 156]. The latter sets a golden standard as it further includes a tutorial video and a live demo. We do not find any additional materials for practitioners for 13 (20.0%) of the available implementations.

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5 Discussion

Regardless of whether AR can be normatively expected or not [77], many systems can genuinely benefit from recourse mechanisms, especially when the interests of the system owner and the end users are aligned [72], such as in the healthcare system to improve the well-being of patients [76, 96, 155], or on the online platforms that attempt to improve the experience of their users [89, 134]. Nonetheless, the values and norms underlying recourse - trust, agency, fairness, safety, and so on - are emergent properties of systems where recourse mechanisms would be introduced. Such norms can only be understood and evaluated when accounting for the technical, social, and institutional components of the system [32], but the latter two remain largely unexplored in the recourse literature.

Recourse is not inherently safe or unsafe, but its (incorrect) implementation may lead to the emergence of unsafe dynamics, such as the unexpected costs to stakeholders as discussed by [9] or the reinforcement of social segregation addressed in [42]. While it may be too challenging to provide accurate system-level evaluations at this stage of research, authors can still expand the boundaries of their analyses to account for global effects or look at the position of recourse mechanisms in the broader context of a complete socio-technical AI system [33]. As AR is a 'reality-centric AI' problem [140] by its nature, working towards its integration into existing systems will require a design-oriented approach, potentially with specific systems in mind. The 'Abstraction Traps' discussed by [119] in the context of research on fair machine learning apply here: that technical solutions designed for one social context cannot be directly repurposed for another application, that values to which they are expected to adhere to cannot be captured with mathematical formulas, that their insertion into an existing process will impact its behavior, or that the best solutions may not necessarily be technical.

It is perhaps most telling that only 12% of surveyed publications attempt to apply recourse in realistic settings. We will discuss two of these settings to highlight the stark differences in system properties. Most of the applications included in our review focus on the provision of actionable individual recommendations to students [3, 4, 24, 109, 126, 135, 160]. In this relatively low-stakes domain almost any recourse will be actionable in that following a personalized set of learning activities does not require any resources other than time. Even then, the system involves multiple actors - students, teachers, parents - whose interactions will impact the process, for example, because students may fail to benefit from certain learning activities without additional support. Conversely, we find several publications where authors attempt to provide recourse in the high-stakes medical domain [76, 96, 155]. Here, recommendations must be tailored to the preferences, resources, or lifestyles of patients in order to have a chance of being actionable. Moreover, certain aspects of their implementation fully rely on other actors, such as a clinician prescribing the medications. Finally, it may happen that recourse does not exist at all when the outcomes of a patient cannot be improved.

5.1 Recommendations for future research

We distill our findings into five key recommendations. First, in Sections 4.2, 4.3 we observed that operational definitions for recourse are still unavailable. Second, Sections 4.4 and 4.8 underlined little consideration for people involved in recourse processes. Third, Sections 4.5, 4.6 highlighted the overwhelmingly technical approaches to recourse. Fourth, Section 4.7 stressed the lack of group-level analyses. Fifth, from Sections 4.8, and 4.9 we learned about the missing consideration of practitioners.

1. Broadening the scope of research. AR is generally seen as a service for affected individuals, but this formalization may be unnecessarily limiting. In fact, in many systems, these individuals may be unable to directly act on recommendations [see also 142]. Instead, we propose to operationalize the aim of AR as the provision of recommendations aligned with the preferences of non-expert users in an attempt to help them improve outcomes in an ADM setting , which emphasizes that providing easy to understand and individually actionable recommendations remains the key research problem.

2. Engaging end users, affected individuals, and communities. AR solutions are rarely evaluated with humans. Instead, they attempt to satisfy a variety of desiderata formulated by authors and assessed in an automated manner. Sparsity, proximity, or mutability of features are far from perfect proxies for individual actionability. For AR to be truly useful, it must be able to satisfy the preferences of its end users. Research is also necessary to learn about the needs of the affected individuals concerning recourse, and to validate its potential contributions and inherent limitations. Authors may also benefit from the rich literature on human-computer interaction [e.g., 11, 23] or psychology.

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3. Accepting a socio-technical perspective. A pervasive assumption in the literature is that all challenges of AR require purely technical solutions. For instance, many authors emphasize the importance of causal modeling to guarantee recourse, but the models that aim to be explained are themselves not causal. Similarly, to improve the performance of CE generators many authors turn to deep generative models [35, 42, 61, 67, 81, 90, 99]. Not only do they explain the data rather than the model [10], but more importantly they shift the problem from improving the trust in non-interpretable models, to attempting to trust non-interpretable explainers. Although a socio-technical perspective on AR brings its own challenges, such as accounting for the roles of stakeholders involved in the provision of recourse, it creates important opportunities. For example, developing 'recourse contracts' [34, 39] or designing feedback processes to account for imperfect robustness.

4. Accounting for emergent effects. Decision-making systems involve multiple individuals who may be interested in receiving recourse and may have competing interests. Research on AR should, from the onset, explore group-level effects such as external costs or fairness. While this may require expanding the boundaries of analysis, it is necessary to anticipate the emergent outcomes of recourse. These may even occur due to the multi-system dynamics of AR: recommendations implemented by an individual to improve their outcomes in one system will affect them in other contexts [see also 13].

5. Attending to other operational aspects. Finally, the artifacts of AR research should be practitioner-friendly. On the one hand, this requires being explicit about the position of the proposed methods in a broader system, for example, in the form of end-to-end case studies that allow practitioners to better understand the benefits of the proposed solutions. On the other hand, this suggests that authors should attempt to move away from merely providing scripts for experiments, and focus on developing well-documented frameworks that can be adapted to different ADM systems.

5.2 Limitations of our work

Our review is not without shortcomings. Most importantly, for each paper the extraction and coding of data was performed by a single author, which means that the quantitative results may be imperfect. We account for this by focusing the analysis on the overarching themes represented in existing publications, thus, even if another researcher would have carried out the coding in a somewhat different manner, they should arrive at similar results and our analysis remains valid. Additionally, as our review ultimately looks at the authors' perception of recourse, we do not want to misconstrue their views. Thus, we do not infer any considerations unless they are provided explicitly. Our reading may be more strict than intended by the authors and the numbers reported in our results may be underestimated. At the same time, we believe that if certain considerations are deemed important by the researchers, they would choose to be explicit about them. Finally, although we followed a systematic process, we cannot claim that we collected AR literature in an exhaustive manner due to the specificities of computer science publishing. Thus, we acknowledge that there may exist some insightful publications addressing recourse that have not been covered in this literature review.

6 Conclusions

Algorithmic recourse concerns the provision of recommendations aligned with the preferences of non-expert users of algorithmic decision-making systems to help them achieve more desirable outcomes in the future. Existing research on the topic is predominantly theoretical, even though recourse, in expectation, is a real-world problem with strong practical implications. To that end, we conducted a systematized literature review of 127 publications that focus on algorithmic recourse, and more generally on actionable counterfactual explanations. We evaluated the practical considerations provided by the authors. Our findings indicate that, indeed, AR tends to be perceived as a (predominantly) technical problem. Although we think highly of fundamental research, we note that for algorithmic recourse to leave computer science labs, it must be more strongly grounded and validated in the real world, and consider the requirements for systems that include not only technical but also social and institutional components. To help bridge this gap, we synthesize a list of five recommendations for other authors that aim to reinforce recourse as a practical problem. We believe that AR should not be seen as only a simple ad-hoc solution to improve the acceptance of black-box models in consequential domains, but rather as a full-fledged socio-technical mechanism that can benefit many systems and improve the agency of affected individuals and decision-makers across a variety of settings.

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Sahil Verma, Ashudeep Singh, Varich Boonsanong, John P. Dickerson, and Chirag Shah. RecRec: Algorithmic Recourse for Recommender Systems. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , CIKM '23, pages 4325-4329, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400701245. doi: 10.1145/3583780.3615181.

Kilian Vieth-Ditlmann. The algorithmic administration: automated decision-making in the public sector, May 2024. URL https://algorithmwatch.org/en/algorithmic-admin istration-explained/ . Accessed 22.05.2024.

Marco Virgolin and Saverio Fracaros. On the Robustness of Sparse Counterfactual Explana964 tions to Adverse Perturbations. Artificial Intelligence , 316(C), March 2023. ISSN 0004-3702. 965 doi: 10.1016/j.artint.2022.103840. 966

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Vy Vo, Trung Le, Van Nguyen, He Zhao, Edwin V. Bonilla, Gholamreza Haffari, and Dinh Phung. Feature-Based Learning for Diverse and Privacy-Preserving Counterfactual Explanations. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , KDD '23, pages 2211-2222, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400701030. doi: 10.1145/3580305.3599343.

Julius Von Kugelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Scholkopf. On the Fairness of Causal Algorithmic Recourse. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 , volume 36, pages 9584-9594. Association for the Advancement of Artificial Intelligence, 2022.

Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, and Bernhard Schölkopf. Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. arXiv , 2021.

Sandra Wachter, Brent Mittelstadt, and Chris Russell. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology , 31:841, 2017.

Paul Y. Wang, Sainyam Galhotra, Romila Pradhan, and Babak Salimi. Demonstration of Generating Explanations for Black-Box Algorithms Using Lewis. Proc. VLDB Endow. , 14 (12):2787-2790, July 2021. ISSN 2150-8097. doi: 10.14778/3476311.3476345.

Yongjie Wang, Qinxu Ding, Ke Wang, Yue Liu, Xingyu Wu, Jinglong Wang, Yong Liu, and Chunyan Miao. The Skyline of Counterfactual Explanations for Machine Learning Decision Models. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management , CIKM '21, pages 2030-2039, New York, NY, USA, 2021. Association for Computing Machinery. ISBN 978-1-4503-8446-9. doi: 10.1145/3459637.3482397.

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Zhendong Wang, Isak Samsten, Vasiliki Kougia, and Panagiotis Papapetrou. Style-Transfer Counterfactual Explanations: An Application to Mortality Prevention of ICU Patients. Artif. Intell. Med. , 135(C), January 2023. ISSN 0933-3657. doi: 10.1016/j.artmed.2022.102457.

Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, and Duen Horng Chau. GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , CHI '23, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 978-1-4503-9421-5. doi: 10.1145/3544548.3580816.

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Anjana Wijekoon, Nirmalie Wiratunga, Ikechukwu Nkisi-Orji, Kyle Martin, Chamath Palihawadana, and David Corsar. Counterfactual Explanations for Student Outcome Prediction with Moodle Footprints. In CEUR Workshop Proceedings , volume 2894, pages 1-8. CEURWS, 2021.

Nirmalie Wiratunga, Anjana Wijekoon, Ikechukwu Nkisi-Orji, Kyle Martin, Chamath Pal1019 ihawadana, and David Corsar. DisCERN: Discovering Counterfactual Explanations using 1020 Relevance Features from Neighbourhoods. In 2021 IEEE 33rd International Conference on 1021 Tools with Artificial Intelligence (ICTAI) , pages 1466-1473, November 2021. ISBN 2375-0197. 1022 doi: 10.1109/ICTAI52525.2021.00233. 1023

Claes Wohlin. Guidelines for Snowballing in Systematic Literature Studies and a Replication 1024 in Software Engineering. EASE '14: Proceedings of the 18th International Conference on 1025 Evaluation and Assessment in Software Engineering , 2014. doi: 10.1145/2601248.2601268. 1026 URL https://doi.org/10.1145/2601248.2601268 . 1027

Jingquan Yan and Hao Wang. Self-Interpretable Time Series Prediction with Counterfactual 1028 Explanations. In Proceedings of the 40th International Conference on Machine Learning , 1029 ICML'23. JMLR.org, 2023. 1030

Jayanth Yetukuri, Ian Hardy, and Yang Liu. Towards User Guided Actionable Recourse. 1031 In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society , AIES '23, 1032 pages 742-751, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 1033 9798400702310. doi: 10.1145/3600211.3604708. 1034

Songming Zhang, Xiaofeng Chen, Shiping Wen, and Zhongshan Li. Density-Based Reliable 1035 and Robust Explainer for Counterfactual Explanation. Expert Syst. Appl. , 226(C), September 1036 2023. ISSN 0957-4174. doi: 10.1016/j.eswa.2023.120214. 1037

A Extended discussion of the search process 1038

For SCOPUS: 1048

The search is carried out on January 12 th 2024 in titles, abstracts, and keywords, with 1267 results 1049 from ACM Digital Library (The ACM Guide to Computing Literature), 513 results from IEEE Xplore, 1050 and 2139 results from SCOPUS. This leads to a total of 3919 results, which are imported to the 1051 Zotero reference management software for de-duplication. After removing the duplicates, we are left 1052 with 3136 results, 44 of which are the meta-data of conference proceedings that we also remove. 1053

To facilitate the screening process, we employ the open-source ASReview tool, which makes use of 1054 an active learning approach to re-order the set of publications, such that the most relevant ones are 1055 always 'at the top of the stack' [139]. We run ASReview on the default settings, i.e.: 1056

The researchers behind the tool suggest employing a stopping rule measured in the number of 1057 consecutive irrelevant records, which we set to 30, or 1% of the entire dataset. We accept all papers 1058 that focus on algorithmic recourse and counterfactual explanations, completing the screening after 1059 evaluating 1040 abstracts (33.67% of the dataset), leading to 504 (16.30%) records among which we 1060 identify further 4 duplicates to remove. This results in the reported number of 499 relevant records. 1061

We observe that some important publications may be missing from our results. For instance, [151] 1062 was published in the Harvard Journal of Law & Technology that is not indexed by computer science 1063 search engines. Thus, we decide to augment the set of records by applying snowballing, which has 1064 been shown as a good alternative to databases in systematic reviews in software engineering [162]. 1065

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We decide to make use of citation counts as a proxy for impact. Due to the lack of a suitable tool that would provide unbiased citation counts for all papers in our dataset, we collect them from Google Scholar. Unfortunately, citation counts on Google Scholar tend to be inflated, but as we make use of snowballing purely to enrich the dataset, these does not impact the validity of our study. We manually collect Google Scholar citation counts for all 499 results from the first screening on January 27 th and 28 th , order them descendingly, and collect references for the top 50 (10%) 'most impactful' publications. Snowballing results in a total of 1519 new records. Indeed, we observe that [151] (mentioned above) is referenced by 39 of the 50 publications used for snowballing.

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While this strategy introduces several pre-prints into our result set [52, 61, 91, 113, 143, 150], we decide not to exclude them. Our review remains primarily concerned with peer-reviewed work. Here, we also note that [114], which we collected as a pre-print has been published between the search and appraisal. As such we decided to evaluate its published version and refer to it in this paper.

After adding the snowballed references into our dataset, we are left with 2018 records for the second 1078 screening with ASReview, again on the default settings. This time, we look for publications that 1079 specifically refer to the problem of AR, 'actionable' CEs, or modifying outcomes of automated 1080 decision-making systems. We employ a stricter stopping rule to minimize the risk of false neg1081 atives, completing the screening after 60 consecutive irrelevant records. We evaluate 538 results 1082 (26.71% of the dataset), with 203 (10.06%) relevant results that are considered for full-text appraisal. 1083

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2024

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NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Our main claim is that existing research on recourse is disconnected from the practical requirements of systems where it would be applied (see Section 4 and Section 5.1). Our claim is supported by a systematized literature review which is the contribution of this work (Section 3). These are reflected in the abstract and the introduction.

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Answer: [NA]

Justification: Our work, as a literature review, does not rely on theoretical results or proofs. Nonetheless, we are explicit about the 'assumptions' in that we discuss our approach to the collection and analysis of results in depth in Section 3.

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The answer NA means that the paper does not include theoretical results.

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5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

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The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: Although this is not covered in a separate section, positive and negative societal impacts of our work (and algorithmic recourse in general) are a key consideration throughout

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The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: Our work poses no such risks, so this question is not applicable. We do not introduce any data or models.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [NA]

Justification: Our work does not use existing assets (other than the referenced papers), so this question is not applicable. All papers covered in the review are referenced in sufficient detail, so that the readers can access them.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

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The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: Our work does not release any new assets, so this question is not applicable. We release the paper with the most permissible license available for NeurIPS submissions. Finally, we will release the review data upon acceptance.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: Our paper does not involve crowdsourcing or research with human subjects, so this question is not applicable. The work was in its entirety carried out by the authors.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: Our work does not involve crowdsourcing or research with human subjects, so this question is not applicable. We did not require an IRB approval or equivalent to carry

out this work.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper provides a review of previous works that study \"algorithmic recourse\", i.e. conceptual and practical approaches for giving people actionable recommendations to change how they are impacted by algorithmic systems. This literature is deeply connected with counterfactual explanations and understanding models through small changes to test data, answering questions such as \"how would the model M produce a different output if changed attribute x about myself\". The authors review 127 archival publications and answer 9 questions about how these works frame and study algorithmic recourse.\n\nSTRENGTHS:\nIn terms of originality, quality, and clarity:\n- While the primary novel contributions of this draft are to highlight themes in previous work, the overall level of novelty is reasonable. Some concerns here, see below.\n- Quality: the \"Systematized review\" methods are described such that they are replicable and seem justified. I don't expect readers to have major issues with inclusion criteria of papers, or any of the analyses presented.\n- Clarity: Writing is clear throughout.\n\nIn terms of significance, the paper could have impact on future work studying algorithmic recourse, and might motivate NeurIPS community members (including those in companies or working with governments) to support recourse methods. This would be a large positive impact.  \n\n This kind of review can certainly be useful to researchers trying to incorporate ideas or findings from recourse-related research. The calls to engage with HCI and systems-level thinking are reasonable (though, some of the broader discussion/motivation in the paper is more convincing on this front than any of the empirical results from the 127 recourse-related papers). If a version of this paper were able to unify definitions in the recourse space, this could be powerful (though further expansion of Section 2.2 might be necessary: the paper does note that reference [70] is highly similar -- the current draft was a bit vague in comparing these and clarifying the added contribution here.).\n\nA few other notes: There are 9 overall sub-research questions answered. Overall, these results seem likely to be useful to researchers entering the algorithmic recourse field (though, see below, some of these felt very general and not domain-specific in the current draft). The paper does fit into the \"Social and economic aspects of machine learning\" category listed in the CFP this year.\n\nWEAKNESSES:\nOverall, I do think the current draft may not achieve the full impact that a future revision could provide.\n\nThe current discussion section feels like it largely echoes other calls in the community to apply systems thinking to ethical/responsible/pro-social AI/ML initiatives, and while each of the suggestions has some connection to one of the analyses, the current draft is not totally clear about the extent to which these recommendations stem from the findings vs. are motivated by first principles. The paper is overall very critical of the AR field, i.e. \"Why hasn't this field engagement with any real world deployments\". However, it's also not entirely clear in the current draft how any of the general recommendations would be applied in specific AR domains.\n\nOne aspect of the paper that I think would have been most directly helpful to the NeurIPS audience in particular would be to take a stance on how recourse should be defined -- is the definition on line 62 \"endorsed\" by the paper? Is the \"imagine a counterfactual input x*\" an advisable approach to take for future work. Does this review support the definition, highlight core definitional or epistemic issues, etc.\n\nUltimately, given the intended goals of this draft, it seems success and impact (on top of the core empirical contribution provided by writing a systematized review) here are dependent on the ability for the provided recommendations to shape future research positively. While the five recommendations here could have a some positive impact, taking a stronger stance on the core definitions and framing of recourse \"tasks\" could have an even larger impact.\n\nQUESTIONS:\nThe primary questions that I would pose to the authors would be:\n- Is it possible to use the current \"data\" (i.e. selected papers) to provide more actionable domain-specific recommendations and/or pragmatic guidance about which contexts are likely to see real-world engagement with AR?\n- What is needed to get organizations that develop and/or operate algorithmic systems to engage with recourse? Are there circumstances that the current literature treats as \"futile\"?\n\nI think using the data that's already been collected and analyzed, focusing on domain-specificity of recommendations could go very far in strengthening the draft.\n\nLIMITATIONS:\n- No major concerns regarding unmentioned social impacts.\n- Regarding the limitations of systematized literature review, the current draft discusses these reasonably.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper provides a comprehensive review of the algorithmic recourse research literature, concentrating on understanding the recourse research \"in the wild\", by focusing on the practical application of these techniques in real-world scenarios. The authors then provide some suggestions to practitioners to push future research to better practical applications.\n\nSTRENGTHS:\nThe paper is well-written and well-structured. Considerable effort has been put into this work to provide a comprehensive review of the area, highlighting the need for a more down-to-earth approach when considering recourse. The data collection and analysis are well-motivated and described sufficiently (Section 3 and Section 4). The recommendations in Section 5.1 are on point and all true, and they highlight issues that everyone in the community is aware of but that are largely ignored.\n\nWEAKNESSES:\nI feel NeurIPS is not the right venue for this kind of contribution, since this paper does not provide the level of technical novelty required by the conference. Being a review, I think it does not fit the requirement of \"new and original research\" given by the Call of Papers. I suggest the authors not be discouraged, since I think the contribution is still valuable for the community. Potential other venues I believe are more in line with the scope of this work could be the following (the order is random):\n- IJCAI Survey Track (https://ijcai24.org/call-for-papers-survey-track/)\n- ACM FAccT (https://facctconference.org/)\n- AAAI/ACM AIES (https://www.aies-conference.com/2024/)\n- ICML Position Papers Track (https://icml.cc/Conferences/2024/CallForPositionPapers)\n- ACM Computing Surveys (https://dl.acm.org/journal/csur) \n- TMLR (https://jmlr.org/tmlr/)\n\nLastly, I would like to point out some potential additional papers on algorithmic recourse which could complement some remarks made by the authors:\n- Line 182 \"We did not identify any applications evaluated with humans in the loop\": there has been some development in providing human-in-the-loop algorithms to identify better recourse options:\n  - [1] De Toni, Giovanni, et al. \"Personalized Algorithmic Recourse with Preference Elicitation.\" Transactions on Machine Learning Research, https://openreview.net/forum?id=8sg2I9zXgO\n- Recommendation 4, \"Accounting for emergent effects\": there has been some research regarding providing recourse to multiple individuals, where they are competing for a limited pool of resources, looking also at the fairness of these systems:\n  - [2] Fonseca, João, et al. \"Setting the right expectations: Algorithmic recourse over time.\" Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. https://dl.acm.org/doi/pdf/10.1145/3617694.3623251\n  - [3] Bell, Andrew, et al. \"Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity.\" arXiv preprint arXiv:2401.16088, https://arxiv.org/pdf/2401.16088\n\nI also point the authors to some new papers considering human-in-the-loop interfaces for recourse (Recommendation 1, Section 5.1):\n- [4] Esfahani, 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. https://dl.acm.org/doi/pdf/10.1145/3627043.3659556\n- [5] Koh, Seunghun, Byung Hyung Kim, and Sungho Jo. \"Understanding the User Perception and Experience of Interactive Algorithmic Recourse Customization.\" ACM Transactions on Computer-Human Interaction. https://dl.acm.org/doi/pdf/10.1145/3674503\n\nQUESTIONS:\nI do not have any questions for the authors.\n\nLIMITATIONS:\nThe authors have highlighted the limitations of their work in Section 5.2.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper is a review of algorithmic recourse (AR) literature. The authors deploy a systematic framework to investigate research trends in algorithmic recourse and evaluate their incorporation of practical concerns like societal and institutional considerations of AR, or lack thereof. The review finds that current research is  focused on methods and technical considerations. The authors encourage researchers in AR to consider real-world implications of their work and conduct user studies.\n\nSTRENGTHS:\n- Paper is well-organized and easy to follow\n- Section 2 provides solid background information on algorithmic recourse\n- The questions in Section 4 are pertinent\n\nWEAKNESSES:\nWhile I agree with the points being made and appreciate the findings in the paper, I question their novelty. As mentioned in Section 4.6, there are papers (albeit in smaller numbers than we would want) that already provide real-world examples and attempt to discuss ethics within recourse. Previous work by [Doshi-Velez and Kim](https://arxiv.org/pdf/1702.08608), [Vaughan and Wallach](https://www.jennwv.com/papers/intel-chapter.pdf) have called for more user studies in interpretable ML, which resulted in studies like [Sixt et al.](https://openreview.net/pdf?id=v6s3HVjPerv). Considering that many researchers working on recourse is also in the field of ML interpretability, I am not sure if the paper's results and call for more user studies are very substantive.\n\nSpending more time differentiating this work from other related works (especially other literature reviews like [70]) rather than listing their contributions in Section 2.2 may be helpful in making your case.\n\nA more thorough discussion and evaluation of results (attempted in Section 5) may resolve some of these questions. As it stands, there is a disconnect between Section 4 and 5. The message of the first part of Section 5 (lines 318-350) is not clear. The second paragraph of the section does not seem to be a discussion of survey results but rather an argument the authors are trying to make (without using the results). The paper would benefit from expanding on the contents in lines 352 to 356, pointing to results in Section 4 and bridging them to the suggestions in Section 5.1.\n\nThe paper reads more like a position paper, trying to convince researchers in algorithmic recourse to not only focus on technical methods (which, again, I agree with). But I am not sure if a literature review or a position paper suitable for NeurIPS, considering its call for papers, does not seem to suggest so.\n\nQUESTIONS:\n- What was the motivation for this literature review?\n- Why do you say that recourse should be aligned with the *preferences* of the end-users?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present a survey regarding algorithmic recourse scientific literature. In their work, the authors analyze what types of contributions do the authors choose to make to the AR research, what are the criteria covered in the authors’ definitions of AR, what are the criteria covered in the authors’ definitions of actionability, the roles of end users, what types of real-world considerations motivate existing research, what types of real-world considerations are seen as challenges for future work, what types of group-level dynamics are addressed in the existing research, what are the approaches to the realistic evaluation of proposed methods, and what are the open source and documentation practices in AR research. They conclude their paper by providing recommendations on how to make future algorithmic recourse solutions better suited for real-world needs.\n\nSTRENGTHS:\n- the authors invested much effort into explaining the procedure followed to ensure a high-quality survey\n- the authors very synthetically review scientific literature related to algorithmic recourse and provide a great insight into the field within a few pages\n- the authors reviewed a vast amount of literature (165 references!)\n\nWEAKNESSES:\nWe did not identify important weaknesses. While an extensive survey could be created following this one, providing in-depth details for each of the sections, we understand this cannot be done within the constraints established for this venue.\n\nQUESTIONS:\nWe consider the work to be interesting and relevant. We consider the review to be concise yet relevant, and that could be extended later, providing more fine-grained insights on the topic of algorithmic recourse. Nevertheless, we would like to point to the following improvement opportunities:\n\n(1) - the authors in their abstract mention multiple times the actionable component of algorithmic recourse, which is present in some of the subsections, but in others, the link to this aspect is less clear and could be enhanced. \n\n(2) - did the authors find any works considering actionability in a wider sense, e.g., that some action could be taken even by machines? What are the implications and concerns in such cases?\n\nLIMITATIONS:\nThe authors have adequately acknowledged the limitations of their work.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}