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给社会服务的提供带来了根本挑战。缺乏稳定的住处会增加经历这种不安全感的所有家庭成员（包括儿童）遭受不良后果的风险。该项目是一项探索性研究，旨在利用人工智能 (AI) 技术来改善早期筛查，并向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。该团队将寻求开发新方法，将稀缺的住房支持资源分配给高风险家庭，同时考虑整体效率和公平性。这项工作将需要在人工智能中提出新的问题和算法开发，并创建新的道德方法来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，有可能实质性改善许多最弱势公民的生活。该项目将探索使用新颖的算法技术为社会分配决策提供信息的可行性稀缺资源，其具体目标是改善无家可归者和儿童福利的服务系统成果。该团队的重点将是无家可归预防干预措施，提供及时、非重复性的资源，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有关儿童福利和无家可归的独特数据集，并利用这些数据来设计机器学习方法来预测结果（特别是无家可归的重复事件以及未来与儿童保护服务的互动），以及利用这些数据的优化技术这些预测是为了决定针对哪些家庭进行预防干预。在总体分配必须提高社会福利（从多个维度衡量）并满足稀缺资源分配的公平、公正和地方正义理念的背景下，预测和优化的相互作用对于人工智能来说是一个具有挑战性的领域。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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）

Community- and data-driven homelessness prevention and service delivery: optimizing for equity

社区和数据驱动的无家可归预防和服务提供：优化公平

DOI：
10.1093/jamia/ocad052
发表时间：
2023-04
期刊：
Journal of the American Medical Informatics Association
影响因子：
6.4
作者：
Kube, Amanda R.;Das, Sanmay;Fowler, Patrick J.
通讯作者：
Fowler, Patrick J.

Incentivizing Truthfulness Through Audits in Strategic Classification

通过战略分类审计激励诚实

DOI：
发表时间：
2021-01
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Estornell, Andrew;Das, Sanmay;Vorobeychik, Yevgeniy
通讯作者：
Vorobeychik, Yevgeniy

Local Justice and the Algorithmic Allocation of Scarce Societal Resources

地方正义与稀缺社会资源的算法分配

DOI：
发表时间：
2022-01
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Sanmay Das
通讯作者：
Sanmay Das

Trade-offs between Group Fairness Metrics in Societal Resource Allocation

社会资源配置中群体公平指标之间的权衡

DOI：
10.1145/3531146.3533171
发表时间：
2022-01
期刊：
and Transparency
影响因子：
0
作者：
Mashiat, Tasfia;Gitiaux, Xavier;Rangwala, Huzefa;Fowler, Patrick J.;and Das, Sanmay
通讯作者：
and 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