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.

This collection contains a set of articles and position papers Our main goal in organizing the AAAI Spring Symposium on topics in artificial intelligence for development at Stanford on Artificial Intelligence for Development has (AID). Each paper explores one or more opportunities for been to bring together a critical mass of researchers who harnessing AI to promote the socioeconomic development share an interest in applying AI research to development and enhance the quality of life of disadvantaged populations, challenges. We hope that the meeting will catalyze new research including people living within developing countries. Insightful applications of machine learning, reasoning, We note that the use of machine intelligence has been pursued planning, and perception have the potential to bring great before in projects within the information and communication value to disadvantaged populations in a wide array of areas, technologies for development (ICT-D) community. We hope that can extend medical care to remote regions through this new collection of papers, and the presentations, panels, automated diagnosis and effective triaging of limited and discussions at the AID symposium, will help to further medical expertise and transportation resources.