Local Justice and the Algorithmic Allocation of Societal Resources
This work aims to improve AI decision-making in resource allocation for societal applications like housing and healthcare, but it is incremental as it builds on existing proposals without presenting new empirical results.
The paper addresses the problem of designing AI systems for allocating scarce societal resources by proposing closer engagement with political philosophy literature on local justice to frame objectives, and discusses integrating data and algorithms to leverage predictive accuracy.
AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations. Recently, there have been several proposals for how to design objectives for these systems that attempt to achieve some combination of fairness, efficiency, incentive compatibility, and satisfactory aggregation of stakeholder preferences. This paper lays out possible roles and opportunities for AI in this domain, arguing for a closer engagement with the political philosophy literature on local justice, which provides a framework for thinking about how societies have over time framed objectives for such allocation problems. It also discusses how we may be able to integrate into this framework the opportunities and risks opened up by the ubiquity of data and the availability of algorithms that can use them to make accurate predictions about the future.