Allocation of scarce resources is a recurring challenge for the public sector: something that emerges in areas as diverse as healthcare, disaster recovery, and social welfare. The complexity of these policy domains and the need for meeting multiple and sometimes conflicting criteria has led to increased focus on the use of algorithms in this type of decision. However, little engagement between researchers across these domains has happened, meaning a lack of understanding of common problems and techniques for approaching them. Here, we performed a cross disciplinary literature review to understand approaches taken for different areas of algorithmic allocation including healthcare, organ transplantation, homelessness, disaster relief, and welfare. We initially identified 1070 papers by searching the literature, then six researchers went through them in two phases of screening resulting in 176 and 75 relevant papers respectively. We then analyzed the 75 papers from the lenses of optimization goals, techniques, interpretability, flexibility, bias, ethical considerations, and performance. We categorized approaches into human-oriented versus resource-oriented perspective, and individual versus aggregate and identified that 76% of the papers approached the problem from a human perspective and 60% from an aggregate level using optimization techniques. We found considerable potential for performance gains, with optimization techniques often decreasing waiting times and increasing success rate by as much as 50%. However, there was a lack of attention to responsible innovation: only around one third of the papers considered ethical issues in choosing the optimization goals while just a very few of them paid attention to the bias issues. Our work can serve as a guide for policy makers and researchers wanting to use an algorithm for addressing a resource allocation problem.

翻译：稀缺资源的分配是公共部门反复面临的挑战：这一问题出现在医疗、灾害救济和社会福利等不同领域。这些政策领域的复杂性，以及满足多重且有时相互冲突标准的需求，使得算法在此类决策中的应用日益受到关注。然而，各领域研究者之间缺乏交流，导致对常见问题及应对方法认知不足。为此，我们通过跨学科文献综述，系统梳理了医疗、器官移植、无家可归者救助、灾害救济和福利等不同算法分配领域采用的方法。通过文献检索初步识别1070篇论文，随后六名研究者分两阶段筛选，分别获得176篇和75篇相关论文。我们从优化目标、技术方法、可解释性、灵活性、偏差、伦理考量及性能表现等维度分析了这75篇论文。我们将方法分为以人为本与以资源为导向两类，以及个体与整体两个层面，发现76%的论文从人的视角探讨问题，60%采用优化技术从整体层面分析。研究表明性能提升潜力显著：优化技术通常可将等待时间减少50%以上，成功率提高50%以上。然而，负责任创新方面存在明显不足：仅约三分之一的论文在优化目标选择时考虑伦理问题，极少数关注偏差问题。本工作可为政策制定者和希望使用算法解决资源分配问题的研究者提供指导。