  Algorithmic recourse -- providing recommendations to those affected
negatively by the outcome of an algorithmic system on how they can take action
and change that outcome -- has gained attention as a means of giving persons
agency in their interactions with artificial intelligence (AI) systems. Recent
work has shown that even if an AI decision-making classifier is ``fair''
(according to some reasonable criteria), recourse itself may be unfair due to
differences in the initial circumstances of individuals, compounding
disparities for marginalized populations and requiring them to exert more
effort than others. There is a need to define more methods and metrics for
evaluating fairness in recourse that span a range of normative views of the
world, and specifically those that take into account time. Time is a critical
element in recourse because the longer it takes an individual to act, the more
the setting may change due to model or data drift.
  This paper seeks to close this research gap by proposing two notions of
fairness in recourse that are in normative alignment with substantive equality
of opportunity, and that consider time. The first considers the (often
repeated) effort individuals exert per successful recourse event, and the
second considers time per successful recourse event. Building upon an
agent-based framework for simulating recourse, this paper demonstrates how much
effort is needed to overcome disparities in initial circumstances. We then
proposes an intervention to improve the fairness of recourse by rewarding
effort, and compare it to existing strategies.
