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Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take action against an undesirable outcome made by an algorithmic system, is receiving growing attention. The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context. Disregarding these effects on recourse is a significant oversight, since, in almost all cases, recourse consists of an individual making a first, unfavorable attempt, and then being given an opportunity to make one or several attempts at a later date — when the context might have changed. This can create false expectations, as initial recourse recommendations may become less reliable over time due to model drift and competition for access to the favorable outcome between individuals. In this work we propose an agent-based simulation framework for studying the effects of a continuously changing environment on algorithmic recourse. In particular, we identify two main effects that can alter the reliability of recourse for individuals represented by the agents: (1) competition with other agents acting upon recourse, and (2) competition with new agents entering the environment. Our findings highlight that only a small set of specific parameterizations result in algorithmic recourse that is reliable for agents over time. Consequently, we argue that substantial additional work is needed to understand recourse reliability over time, and to develop recourse methods that reward agents’ effort.
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The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness
Algorithmic recourse, or providing recommendations to individuals who receive an unfavorable outcome from an algorithmic system on how they can take action and change that outcome, is an important tool for giving individuals agency against algorithmic decision systems. Unfortunately, research on algorithmic recourse faces a fundamental challenge: there are no publicly available datasets on algorithmic recourse. In this work, we begin to explore a solution to this challenge by creating an agent-based simulation called The Game of Recourse (an homage to Conway's Game of Life) to synthesize realistic algorithmic recourse data. We designed The Game of Recourse with a focus on reliability and fairness, two areas of critical importance in socio-technical systems.
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- PAR ID:
- 10514479
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of Data
- ISBN:
- 9798400704222
- Page Range / eLocation ID:
- 464 to 467
- Subject(s) / Keyword(s):
- responsible AI algorithmic recourse fairness, reliability ranking data generation temporal data simulation
- Format(s):
- Medium: X
- Location:
- Santiago AA Chile
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626246.3654742
