NSF Convergence Accelerator Track F: Actionable Sensemaking Tools for Curating and Authenticating Information in the Presence of Misinformation during Crises
NSF Convergence Accelerator Track F: Actionable Sensemaking Tools for Curating and Authenticating Information in the Presence of Misinformation during Crises
批准号:
2137806
负责人:
Srinivasan Parthasarathy
金额:
$75.0万
依托单位:
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
项目状态:
已结题
起止时间:
2021-10-01 至 2024-09-30
中文摘要
在危机期间,大量、快速变化、多样化的信息,其中往往包括错误信息,很容易让决策者不知所措。在危机期间和之后很长一段时间内作出的决定,都会影响负责任的决策者和公民(许多人来自弱势群体)之间的信任,而公民会受到这些决定的影响。该项目旨在帮助决策者管理信息,促进对真实知识生产过程的依赖,同时也减少故意虚假信息和无意虚假信息的影响。项目组将开发一套原型工具,将及时、高质量的综合内容用于决策和治理,作为业务的常规部分,特别是在危机期间。综合和经过认证的内容包括科学事实和技术信息,再加上公民和利益攸关方的观点,确保安全决策的准确性和救济工作的适当优先次序。项目团队将综合跨多个学科的融合专业知识;通过与政府和行业的合作伙伴关系参与并建立利益相关者社区,以指导工具开发;构建用于验证数据和管理错误信息的原型工具;并使用真实世界的危机场景验证该工具。项目团队将创建受使用启发的个性化人工智能驱动的感知原型工具,供决策者理解和验证动态、不确定且往往相互矛盾的信息,以促进危机期间的有效决策。这些工具将侧重于策划,同时考虑来源和可解释的内容的可信度。使用人种学方法获得的社区利益相关者的指导将确保所产生的工具是实用的、及时的,并与知情的决策相关。这些工具将利用信息环境的特点、人类的认知能力和局限性以及管理信息的算法方法。特别是,内容和网络分析可以揭示来源的星座,产生可信信息的可能性更高,而知识图谱可以帮助浮出水面并组织重要的共享材料,同时促进可解释性。项目组还将设计和开发一个微观世界环境,以检查和改善工具的稳健性,同时帮助培训现实世界环境中的决策者,如学区和公共卫生环境。该项目代表了计算机科学、社会科学、语言学、网络科学、公共卫生、认知科学、运筹学和传播学等学科的融合,这些都是实现其目标所必需的。社区、政府、行业和学术界之间的伙伴关系将确保交付成果满足利益相关者的需求。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
High volume, rapidly changing, diverse information, which often includes misinformation, can easily overwhelm decision makers during a crisis. Decisions made both during and long after a crisis, affect the trust between responsible decision makers and citizens (many from vulnerable populations), who are impacted by those decisions. This project seeks to help decision makers manage information, promoting reliance on authentic knowledge production processes while also reducing the impact of intentional disinformation and unintended misinformation. The project team will develop a suite of prototype tools that bring timely, high-quality integrated content to bear on decision making and governance, as a routine part of operations, and especially during a crisis. Integrated and authenticated content comprising scientific facts and technical information coupled with citizen and stakeholder viewpoints assure the accuracy of safety decisions and the appropriate prioritization of relief efforts. The project team will synthesize convergent expertise across multiple disciplines; engage and build stakeholder communities through partnerships with government and industry to guide tool development; build a prototype tool for authenticating data and managing misinformation; and validate the tool using real world crisis scenarios.The project team will create use-inspired personalized AI-driven sensemaking prototype tools for decision-makers to comprehend and authenticate dynamic, uncertain, and often contradictory information to facilitate effective decisions during crises. The tools will focus on curation while accounting for source and explainable content credibility. Guidance from community stakeholders obtained using ethnographic methods will ensure that the resulting tools are practical, timely, and relevant for informed decision making. These tools will capitalize on features of the information environment, human cognitive abilities and limitations, and algorithmic approaches to managing information. In particular, content and network analyses can reveal constellations of sources with a higher probability of producing credible information, while knowledge graphs can help surface and organize important materials being shared while facilitating explainability. The project team will also design and develop a microworld environment to examine and improve tool robustness while simultaneously helping to train decision makers in real-world settings such as school districts and public health settings. This project represents a convergence of disciplines spanning expertise in computer science, social sciences, linguistics, network science, public health, cognitive science, operations, and communication that are necessary to achieve its goals. Partnerships between communities, government industry, and academia will ensure the deliverables are responsive to stakeholder needs.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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Collaborative Research: PPoSS: Planning: A Cross-Layer Observable Approach to Extreme Scale Machine Learning and Analytics
-
批准号:2028944
-
项目类别:Standard Grant
-
资助金额:$20.45万
-
财政年份:2020
-
负责人:Srinivasan Parthasarathy
-
依托单位:
EAGER: Practical Graph Sparsification on GPUs
-
批准号:1550302
-
项目类别:Standard Grant
-
资助金额:$11.12万
-
财政年份:2015
-
负责人:Srinivasan Parthasarathy
-
依托单位:
Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response
-
批准号:1520870
-
项目类别:Standard Grant
-
资助金额:$197.5万
-
财政年份:2015
-
负责人:Srinivasan Parthasarathy
-
依托单位:
Sampling and Inference in Network Analysis
-
批准号:1418265
-
项目类别:Standard Grant
-
资助金额:$30.0万
-
财政年份:2014
-
负责人:Srinivasan Parthasarathy
-
依托单位:
SHF:Small:Collabroative Research: Elastic Fidelity: Trading off Computational Accuracy for Energy Efficiency
-
批准号:1217353
-
项目类别:Standard Grant
-
资助金额:$18.2万
-
财政年份:2012
-
负责人:Srinivasan Parthasarathy
-
依托单位:
CCF: EAGER: Collaborative Research: Scalable Graph Mining and Clustering on Desktop Supercomputers
-
批准号:1240651
-
项目类别:Standard Grant
-
资助金额:$7.5万
-
财政年份:2012
-
负责人:Srinivasan Parthasarathy
-
依托单位:
EAGER: Towards New Scalable Stochastic Flow Algorithms
-
批准号:1141828
-
项目类别:Standard Grant
-
资助金额:$15.0万
-
财政年份:2011
-
负责人:Srinivasan Parthasarathy
-
依托单位:
SoCS: Collaborative Research: Social Media Enhanced Organizational Sensemaking in Emergency Response
-
批准号:1111118
-
项目类别:Standard Grant
-
资助金额:$27.0万
-
财政年份:2011
-
负责人:Srinivasan Parthasarathy
-
依托单位:
Global Graphs: A Middleware for Data Intensive Computing
-
批准号:0917070
-
项目类别:Standard Grant
-
资助金额:$50.0万
-
财政年份:2009
-
负责人:Srinivasan Parthasarathy
-
依托单位:
Scalable Data Analysis: An Architecture Conscious Approach
-
批准号:0702587
-
项目类别:Continuing Grant
-
资助金额:$0.0万
-
财政年份:2007
-
负责人:Srinivasan Parthasarathy
-
依托单位:
SGER: An Event-Driven Approach for Analyzing Interaction Networks
-
批准号:0742999
-
项目类别:Standard Grant
-
资助金额:$0.0万
-
财政年份:2007
-
负责人:Srinivasan Parthasarathy
-
依托单位:
CAREER: A Scalable Framework for Mining Scientific and Biomedical Data
-
批准号:0347662
-
项目类别:Continuing Grant
-
资助金额:$49.78万
-
财政年份:2004
-
负责人:Srinivasan Parthasarathy
-
依托单位:
NGS: A Services-Oriented Framework for Next Generation Data Analysis Centers
-
批准号:0406386
-
项目类别:Continuing Grant
-
资助金额:$60.8万
-
财政年份:2004
-
负责人:Srinivasan Parthasarathy
-
依托单位:
海外基金