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 present them with the capacity to take actions that would guarantee more desirable outcomes in the future. Over the past few years, the literature has predominantly focused on the development of solutions 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 challenge: real-world decision-making systems are complex dynamic entities involving various actors – end users, domain experts, system owners, etc. – engaging in social and technical processes. Thus, research on algorithmic recourse should account for the characteristics of systems where such mechanisms could be implemented. This necessitates a rich understanding of the problem space of AR but, as we observe, it remains largely uncharted in the existing literature.

We focus on algorithmic recourse in real-world contexts, applying Design Science Research methods to bridge the gap between its technical affordances and the social constraints of real-world decision-making systems where it could be applied. First, we conduct a systematized literature review of 127 publications to learn about the authors' perception of the problem. Next, we consider a case study of a risk profiling model developed to support the authorities of a major Dutch city in the detection of welfare fraud. We employ a desk research approach to learn about the system, reinforce our understanding of the requirements for algorithms in public administration settings through interviews with experts, and make use of accident analysis methodologies to theorize about the value of AR interventions in this setting. We draw on these insights to propose a conceptual framework for the evaluation of AR in real-world contexts and provide its proof-of-concept instantiation as a simulation tool that facilitates the study of such mechanisms within decision-making processes. Finally, we design and prove an algorithm to generate actionable recommendations in expert systems. These are commonly used in public administration systems but overlooked in existing research.

On the example of our endeavor, we learn about the ways to strengthen the connections between the problem space and the solution space of algorithmic recourse. We argue that AR can be discussed on three levels of complexity: (1) as actionable recommendations, (2) as the process of improving outcomes, or (3) as the task of developing mechanisms to support end-users in this process. We advocate for computer science authors to focus on the final, broadest meaning of the challenge to improve the applicability of their solutions in real-world contexts. We also encourage researchers from other fields to contribute their perspectives and for practitioners to support further research by building upon our approach to reason about the place for AR solutions in their domains of expertise.

Algorithmic recourse aims to provide individuals affected by a negative classification outcome with actions which, if applied, would flip this outcome. Various approaches to the generation of recourse have been proposed in the literature; these are typically assessed on statistical measures such as the validity of generated explanations or their proximity to the training data. However, little to no attention has been paid to the underlying dynamics of recourse. If a group of individuals applies the suggested actions, they may over time induce a shift in the domain or model. We propose a framework for the measurement of such intrinsic shifts, and conduct an analysis of the dynamics of recourse implemented by the generators proposed by Mothilal et al. and Wachter et al.. Our results suggest that the application of recourse is likely to introduce statistically significant shifts in the system, and that the underlying dataset and model impact the behavior of the generators.