Endogenous Macrodynamics in Algorithic Recourse [VIDEO]

P. Altmeyer, G.J.A. Angela, A.J. Buszydlik, K.T. Dobiczek, A. van Deursen, C.C.S. Liem

Research output: Contribution to conferenceOther


Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely been limited to the static setting and focused on single individuals: 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 at this point. 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 by systematizing and extending existing knowledge. We first show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework fails to 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 situations that involve large groups of individuals. Fortunately, we find various potential mitigation strategies that can be used in combination with existing approaches. Our simulation framework for studying recourse dynamics is fast and open-sourced.
Original languageEnglish
Publication statusPublished - 2023
Event1st IEEE Conference on Secure and Trustworthy Machine Learning - Raleigh, United States
Duration: 8 Feb 202310 Feb 2023
Conference number: 1


Conference1st IEEE Conference on Secure and Trustworthy Machine Learning
Abbreviated titleSATML 2023
Country/TerritoryUnited States
Internet address


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  • Endogenous Macrodynamics in Algorithmic Recourse

    Altmeyer, P., Giovan, A., Buszydlik, A., Dobiczek, K., van Deursen, A. & Liem, C. C. S., 2023, Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). Ceballos, C. (ed.). Piscataway: IEEE , p. 418-431 14 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    Open Access
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