Setting the Right Expectations: Algorithmic Recourse Over Time
Joao Fonseca Andrew Bell Carlo Abrate Francesco Bonchi Julia Stoyanovich
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.