Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one's risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.

翻译：无家可归是一个人道主义挑战，全球约有16亿人受到影响。面对发达国家中无家可归人口的增长及社会服务资源的紧张，政府机构越来越多地采用数据驱动模型来评估个人陷入无家可归状态的风险，并将稀缺资源分配给最需要的人。我们对57篇论文进行了系统性文献综述，以了解这些决策算法的演变过程。我们采用以人为本的视角，调查了用于开发模型的计算方法、预测变量和目标结果的趋势，发现只有9篇论文（15.7%）研究了模型的公平性与偏见。我们揭示了可解释性与生态效度之间的张力：预测风险模型（53.4%）侧重于简约的可解释性，而资源分配模型（25.9%）则依赖不切实际的假设和模拟数据，在实践中缺乏实用性。此外，我们讨论了在该领域开发以人为本算法的研究挑战与机遇。