@inproceedings{5023154a53c644ca9d091670ba0ded31,
title = "Endogenous Macrodynamics in Algorithmic Recourse",
abstract = "Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and model drift remains a largely unexplored research challenge. There has also been surprisingly little work on the related question of how the actual implementation of recourse by one individual may affect other individuals. Through this work, we aim to close that gap. We first show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level. Through simulation experiments involving various state-of-the-art counterfactual generators and several benchmark datasets, we generate large numbers of counterfactuals and study the resulting domain and model shifts. We find that the induced shifts are substantial enough to likely impede the applicability of Algorithmic Recourse in some situations. Fortunately, we find various strategies to mitigate these concerns. Our simulation framework for studying recourse dynamics is fast and open-sourced.",
keywords = "Algorithmic Recourse, Counterfactual Explanations, Explainable AI, Dynamic Systems",
author = "Patrick Altmeyer and Angela Giovan and Aleksander Buszydlik and Karol Dobiczek and {van Deursen}, Arie and Liem, {Cynthia C. S.}",
note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 1st IEEE Conference on Secure and Trustworthy Machine Learning, SATML 2023 ; Conference date: 08-02-2023 Through 10-02-2023",
year = "2023",
doi = "10.1109/SaTML54575.2023.00036",
language = "English",
isbn = "978-1-6654-6300-3",
pages = "418--431",
editor = "Ceballos, {Cristina }",
booktitle = "Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)",
publisher = "IEEE",
address = "United States",
url = "https://satml.org/",
}