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

EAGER：AI-DCL：关于在无家可归和虐待儿童问题上使用人工智能的探索性研究

基本信息

批准号：
2127754
负责人：
Sanmay Das
金额：
$ 30万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127754&HistoricalAwards=false
关键词：
EAGER AI DCL Exploratory research

项目摘要

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.

无家可归和住房不安全感提出了社会服务提供的基本挑战。缺乏稳定的住宿会增加所有家庭成员（包括儿童）经历这种不安全感的不良结果的风险。该项目是一项探索性研究，用于使用人工智能（AI）的技术来改善早期筛查，并向面临未来无家可归和儿童虐待风险的家庭提供有针对性的援助。考虑到总体效率和公平性的考虑，该团队将寻求开发新的方法，以分配稀缺住房支持资源为高危家庭。这项工作将需要新颖的问题制定和算法发展，并创建新的道德方法，以决定如何有效地提供社会服务，以考虑到人类行为的严重复杂性。此外，降低无家可归和儿童虐待的风险是至关重要的社会目标，具有实质性的改善我们许多最脆弱的公民的生活。该项目将探索使用新颖的算法技术的可行性，以将社会决策告知稀缺资源的社会决策，以涉及稀缺资源的分配，并以既改善无家可归的人的无家可归者又有无家可归的目标。该团队的重点将放在无家可归的预防干预措施上，这些干预措施提供及时，非重建的资源，以稳定有遇到住房危机的风险的家庭；此类资源的示例包括房东调解，一次性租金或公用事业付款以及搬家费用。他们将利用研究中有关儿童福利和无家可归的独特数据集，并使用这些数据集为机器学习方法的设计提供了预测结果的设计（特别是，无家可归的重复发作和未来与儿童保护服务的互动），以及优化技术，这些技术利用这些预测来确定哪些家庭来决定预防干预措施。预测和优化的相互作用在整体分配必须改善社会福利（沿多个维度衡量），并满足稀缺资源分配中的公平，公平和地方正义的概念，这是AI的一个挑战性领域，是AI的一个挑战性领域。这是NSF的法定任务，反映了NSF通过评估的概念而受到评估的范围，并反映出了该奖项的范围。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Trade-offs between Group Fairness Metrics in Societal Resource Allocation

DOI：
10.1145/3531146.3533171
发表时间：
2022-01-01
期刊：
FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency
影响因子：
0
作者：
Mashiat, Tasfia;Gitiaux, Xavier;Das, Sanmay
通讯作者：
Das, Sanmay

Scarce Societal Resource Allocation and the Price of (Local) Justice

稀缺的社会资源配置和（地方）正义的代价