EAGER: AI-DCL: Exploratory research on the use of AI at the intersection of homelessness and child maltreatment

无家可归和住房不安全是提供社会服务的根本挑战。缺乏稳定的住所增加了包括儿童在内的所有家庭成员遭受这种不安全感的不良后果的风险。该项目是一项探索性研究，旨在利用人工智能（AI）技术改善早期筛查，并为未来面临无家可归和儿童虐待风险的家庭提供有针对性的援助。该小组将在考虑到总体效率和公平的情况下，寻求开发新的方法，将稀缺的住房支助资源分配给处境危险的家庭。这项工作将需要人工智能中的新问题制定和算法开发，以及创建新的伦理方法，以决定如何有效地提供社会服务，同时考虑到人类行为的巨大复杂性。此外，减少无家可归和虐待儿童的风险是重要的社会目标，有可能大大改善我们许多最弱势公民的生活。该项目将探索使用新颖算法技术为稀缺资源分配的社会决策提供信息的可行性，其具体目标是改善无家可归者和儿童福利的服务系统结果。该小组的重点将放在预防无家可归的干预措施上，提供及时、不重复的资源，以稳定面临住房危机风险的家庭；此类资源的例子包括房东调解、一次性租金或水电费以及搬家费用。他们将在研究中利用关于儿童福利和无家可归者的独特数据集，并利用这些数据为机器学习方法的设计提供信息，以预测结果（具体而言，重复的无家可归事件和未来与儿童保护服务的互动），并优化利用这些预测的技术，以决定针对哪些家庭进行预防干预。在整体分配必须既提高社会福利（沿着多个维度衡量）又满足稀缺资源分配中的公平、公平和地方正义概念的背景下，预测和优化的相互作用对人工智能来说是一个具有挑战性的领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Homelessness and housing insecurity present fundamental challenges for the delivery of social services. The absence of stable accommodations increases the risks of poor outcomes for all members of the household, including children, experiencing this insecurity. This project is an exploratory study in the use of techniques from artificial intelligence (AI) to improve early screening and the delivery of targeted assistance to households that are at risk of future homelessness and child maltreatment. The team will seek to develop novel methods for allocation of scarce housing-support resources to at-risk households, taking into account considerations of both overall efficiency and fairness. This work will necessitate novel problem formulation and algorithm development in AI as well as creating new ethical methods for deciding on how to effectively deliver social services taking into account the vast complexity of human behavior. Moreover, reducing the risks of homelessness and child maltreatment are critical societal goals with the potential to substantively improve the lives of many of our most vulnerable citizens.This project will explore the feasibility of using novel algorithmic techniques to inform societal decision-making on the allocation of scarce resources, with the specific goal of improving service system outcomes for both homelessness and child welfare. The team's focus will be on homelessness prevention interventions that offer timely, non-reoccurring resources to stabilize families at risk of experiencing housing crises; examples of such resources include landlord mediation, one-time rent or utility payments, and moving expenses. They will leverage unique datasets on child welfare and homelessness in the research, and use these to inform the design of machine learning approaches to prediction of outcomes (specifically, repeat episodes of homelessness and future interactions with child protective services), and optimization techniques that leverage these predictions in order to decide on which households to target for prevention interventions. The interplay of prediction and optimization, in a context where the overall allocation must both improve social welfare (measured along multiple dimensions) and satisfy notions of fairness, equity, and local justice in the allocation of scarce resources, is a challenging domain for AI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.