AI Institute for Societal Decision Making (AI-SDM)
人工智能社会决策研究所 (AI-SDM)
基本信息
- 批准号:2229881
- 负责人:
- 金额:$ 1987.97万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Cooperative Agreement
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-06-01 至 2028-05-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Decision making in domains such as a public health crisis or disaster response has a significant societal and economic impact. These domains present critical challenges for decision-making as they require complex, potentially life-saving, decisions to be made under dynamic, uncertain and resource-constrained scenarios, while accounting for factors that are key to acceptance of the decisions, such as stakeholders' biases and perception of risk, trust, and equity. AI advancements and data availability can complement human limitations in navigating this complex decision space, however, current systems fail to account for the stakeholders' mental states and behavior. The AI institute for Societal Decision Making (AI-SDM) will target this opportunity at the confluence of social decision sciences and AI by developing human-centric AI for decision-making and inter-disciplinary training, to enable transformative solutions to societal decision challenges. By bringing AI and social science researchers, AI-SDM will enable emergency managers, public health officials, first responders, community workers, and the public to make quick, data-driven, and resource-efficient decisions, while also improving outcomes by accounting for human factors governing acceptance. The vision of AI-SDM will be realized via development of novel AI theory and methods, translational research, training, and outreach, enabled by partnerships among diverse universities, government organizations, corporate partners, community colleges, public libraries, and high schools.The institute will establish the role of AI in advancing and bridging human and autonomous decision-making, under the use-inspired challenges of working in environments that are dynamic, uncertain, resource constrained, and require societal acceptance arising in public health crisis and disaster response. Specifically, the foundational research will develop (1) computational representations of human decision processes, (2) robust aggregation methods for collective decision-making, (3) multi-objective autonomous decision support tools, and corresponding innovations in (4) causal and counterfactual reasoning. These foundational foci are inspired by, and will be applied to, equitable resource allocation to improve public health and disaster outcomes, timely targeted interventions informed by human decision-making to encourage adherence to policy recommendations, and adoption of AI decision support by understanding how adoption can be modulated by different use patterns. The research will be guided by theoretical advances in computational cognitive science, social-choice theory, distribution-free statistics, game theory, casual and counterfactual reasoning, and interactive and autonomous machine learning. In addition to impacting use-case domains via a wide network of partners, AI-SDM will develop the next generation of workforce trained on human-centric AI and an AI-aware public via broader impact efforts including professional development workshops for high school educators, enrichment and leadership activities for under-represented students, inter-disciplinary degrees and courses, curriculum co-design with community college and educational partners, workforce training, and public engagement activities.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.
公共卫生危机或灾害应对等领域的决策具有重大的社会和经济影响。这些领域对决策提出了重大挑战,因为它们需要在动态、不确定和资源有限的情况下作出复杂的、可能挽救生命的决定,同时考虑到对接受决定至关重要的因素,如利益攸关方的偏见和对风险、信任和公平的看法。人工智能的进步和数据可用性可以弥补人类在导航这个复杂决策空间方面的局限性,然而,目前的系统无法考虑利益相关者的心理状态和行为。社会决策人工智能研究所(AI-SDM)将通过开发以人为本的决策和跨学科培训人工智能,在社会决策科学和人工智能的融合中抓住这一机会,为社会决策挑战提供变革性的解决方案。通过引入人工智能和社会科学研究人员,AI-SDM将使应急管理人员、公共卫生官员、急救人员、社区工作者和公众能够做出快速、数据驱动和资源高效的决策,同时通过考虑管理接受度的人为因素来改善结果。AI-SDM的愿景将通过开发新的人工智能理论和方法,转化研究,培训和推广,通过不同大学,政府组织,企业合作伙伴,社区学院,公共图书馆和高中之间的合作来实现。该研究所将建立人工智能在推进和连接人类和自主决策方面的作用,在公共卫生危机和灾害应对中,在动态、不确定、资源有限、需要社会接受的环境中工作,面临着使用带来的挑战。具体而言,基础研究将开发(1)人类决策过程的计算表示,(2)集体决策的鲁棒聚合方法,(3)多目标自主决策支持工具,以及(4)因果和反事实推理的相应创新。这些基本焦点的灵感来自并将应用于公平的资源分配,以改善公共卫生和灾害结果,及时有针对性的干预措施,以鼓励遵守政策建议,并通过了解如何通过不同的使用模式来调整人工智能决策支持。该研究将以计算认知科学,社会选择理论,分布自由统计,博弈论,休闲和反事实推理以及交互式和自主机器学习的理论进展为指导。除了通过广泛的合作伙伴网络影响用例领域外,AI-SDM还将通过更广泛的影响力工作,包括高中教育工作者的专业发展研讨会,代表性不足的学生的丰富和领导活动,跨学科学位和课程,与社区学院和教育合作伙伴共同设计课程,该奖项反映了NSF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。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Strategyproof Voting under Correlated Beliefs
相关信念下的策略证明投票
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Daniel Halpern, Rachel Li
- 通讯作者:Daniel Halpern, Rachel Li
Optimal Engagement-Diversity Tradeoffs in Social Media
社交媒体中的最佳参与多样性权衡
- DOI:
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Fabian Baumann, Daniel Halpern
- 通讯作者:Fabian Baumann, Daniel Halpern
School Redistricting: Wiping Unfairness Off the Map
学校重新划分:消除地图上的不公平现象
- DOI:
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Ariel D. Procaccia, Isaac Robinson
- 通讯作者:Ariel D. Procaccia, Isaac Robinson
Manipulation-Robust Selection of Citizens’ Assemblies
公民集会的操纵-稳健选择
- DOI:
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Bailey Flanigan, Jennifer Liang
- 通讯作者:Bailey Flanigan, Jennifer Liang
The Distortion of Binomial Voting Defies Expectation
二项式投票的扭曲超出了预期
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Yannai Gonczarowski, Gregory Kehne
- 通讯作者:Yannai Gonczarowski, Gregory Kehne
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Aarti Singh其他文献
Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition
Tsybakov 噪声条件下基于噪声自适应裕度的主动学习和下界
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Yining Wang;Aarti Singh - 通讯作者:
Aarti Singh
A closer look at jobless recoveries
仔细观察失业复苏
- DOI:
- 发表时间:
2003 - 期刊:
- 影响因子:0
- 作者:
Stacey L. Schreft;Aarti Singh - 通讯作者:
Aarti Singh
Design of an Intelligent and Adaptive Mapping Mechanism for Multiagent Interface
一种智能自适应多智能体接口映射机制设计
- DOI:
10.1007/978-3-642-22577-2_51 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Aarti Singh;Dimple Juneja;A. Sharma - 通讯作者:
A. Sharma
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data
用于缺失数据的矩阵列子集选择的可证明正确的主动采样算法
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Yining Wang;Aarti Singh - 通讯作者:
Aarti Singh
An empirical comparison of sampling techniques for matrix column subset selection
矩阵列子集选择采样技术的实证比较
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Yining Wang;Aarti Singh - 通讯作者:
Aarti Singh
Aarti Singh的其他文献
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{{ truncateString('Aarti Singh', 18)}}的其他基金
Collaborative Research: New Perspectives on Deep Learning: Bridging Approximation, Statistical, and Algorithmic Theories
合作研究:深度学习的新视角:桥接近似、统计和算法理论
- 批准号:
2134133 - 财政年份:2021
- 资助金额:
$ 1987.97万 - 项目类别:
Standard Grant
QuBBD: Collaborative Research: Personalized Predictive Neuromarkers for Stress-Related Health Risks
QuBBD:合作研究:压力相关健康风险的个性化预测神经标志物
- 批准号:
1557572 - 财政年份:2015
- 资助金额:
$ 1987.97万 - 项目类别:
Standard Grant
15th IMS New Researchers Conference
第15届IMS新研究员大会
- 批准号:
1301845 - 财政年份:2013
- 资助金额:
$ 1987.97万 - 项目类别:
Standard Grant
CAREER: Distilling information structure from big and dirty data: Efficient learning of clusters and graphs in modern datasets
职业:从大数据和脏数据中提取信息结构:现代数据集中集群和图的高效学习
- 批准号:
1252412 - 财政年份:2013
- 资助金额:
$ 1987.97万 - 项目类别:
Continuing Grant
BIGDATA: Mid-Scale: DA: Distribution-based machine learning for high dimensional datasets
BIGDATA:中规模:DA:针对高维数据集的基于分布的机器学习
- 批准号:
1247658 - 财政年份:2013
- 资助金额:
$ 1987.97万 - 项目类别:
Continuing Grant
III: Small: Spectral Methods for Active Clustering and Bi-Clustering
III:小:主动聚类和双聚类的谱方法
- 批准号:
1116458 - 财政年份:2011
- 资助金额:
$ 1987.97万 - 项目类别:
Standard Grant
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