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.

该论文介绍了 AI 技术在资源分配方面的应用，讨论了如何在政治哲学的指导下设计这些系统的目标，以实现公平、效率、激励兼容性和利益相关者偏好的满意聚合，并探讨了数据万物互连时代下，算法可预测未来为我们带来的机遇和风险。