Setting the Right Expectations: Algorithmic Recourse Over Time

TL;DR

This paper introduces an agent-based simulation framework to study how changing environments affect the reliability of algorithmic recourse over time, emphasizing the importance of considering dynamic factors like model drift and agent competition.

Contribution

It presents a novel simulation approach to analyze the temporal stability of algorithmic recourse, highlighting the need for methods that account for environmental changes and agent interactions.

Findings

Recourse reliability is sensitive to environmental parameters.

Only specific conditions ensure long-term recourse reliability.

Additional research is needed for robust, effort-rewarding recourse methods.

Abstract

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.