Collaborative Research: SaTC: CORE: Medium: An Incident-Response Approach for Empowering Fact-Checkers

协作研究：SaTC：核心：媒介：增强事实检查人员能力的事件响应方法

• 批准号: 2154119
• 负责人: Chinmay Hegde
• 金额: $ 39.6万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154119&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

Fact-checking can be effective in countering the growing threat of online misinformation because people across the political spectrum and demographics tend to trust credibility judgments of fact-checkers. However, a pipeline of manual and labor-intensive practices fragmented across disparate tools makes it difficult to scale fact-checking efforts. As a result, fact-checkers are inundated with information and lack effective dissemination mechanisms for countering misinformation early and effectively. To address these challenges, this project combines the complementary information processing strengths of humans and computation to transform the efficiency, effectiveness, and scale of fact-checking. The project can enable fact-checkers to spot misinformation early, prioritize effort, and unify the various tools and techniques used for fact-checking. The research outcomes can scale the work of human fact-checkers and boost information literacy in society, which can significantly reduce the number of people exposed to misinformation.The project draws upon the core components of security incident response (i.e., preparation, detection, containment, and post-incident activity) to transform the ad-hoc, time-consuming, and small-scale nature of current fact-checking practices with a security-analyst perspective and a unified user experience (UX). The research approach leverages the power of computation and personalization while retaining the synergistic advantages of the human fact-checker in the loop. The interdisciplinary sociotechnical approach involves empirical studies of fact-checker practices, collection of data and development of computational techniques to address their challenges and barriers, and design explorations of novel UI/UX techniques to connect humans and computation. The research incorporates a feedback loop to disseminate fact-checking outcomes, thus boosting their visibility and impact on end users exposed to misinformation. The researchers are developing early warning and detection techniques to reduce the time between misinformation generation and fact-check dissemination and are employing prioritization and personalization for more effective and efficient use of fact-checking resources. The researchers are engaging with professional fact-checkers to translate the research outcomes to the real world.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.

事实核查可以有效应对网络错误信息日益增长的威胁，因为不同政治派别和人口统计数据的人都倾向于信任事实核查人员的可信度判断。然而，手工和劳动密集型实践的管道分散在不同的工具中，这使得扩大事实检查工作变得困难。结果，事实核查人员被信息淹没，缺乏有效的传播机制，无法及早有效地打击错误信息。为了应对这些挑战，该项目结合了人类和计算的互补信息处理优势，以改变事实核查的效率、有效性和规模。该项目可以使事实检查人员尽早发现错误信息，确定工作的优先级，并统一用于事实检查的各种工具和技术。研究成果可以扩大人类事实核查员的工作范围，提高社会的信息素养，从而大大减少接触错误信息的人数。该项目利用安全事件响应的核心组件（即准备、检测、遏制和事件后活动），以安全分析师的观点和统一的用户体验（UX）转变当前事实检查实践的临时、耗时和小规模性质。该研究方法利用了计算和个性化的力量，同时保留了人类事实核查人员在循环中的协同优势。跨学科的社会技术方法包括对事实核查实践的实证研究，数据收集和计算技术的开发，以解决他们的挑战和障碍，以及设计探索新的UI/UX技术，以连接人类和计算。这项研究采用了一个反馈循环来传播事实核查结果，从而提高了它们的可见性，并对面临错误信息的最终用户产生了影响。研究人员正在开发早期预警和检测技术，以减少错误信息产生和事实核查传播之间的时间，并正在采用优先级和个性化，以更有效和高效地利用事实核查资源。研究人员正在与专业的事实核查人员合作，将研究成果转化为现实世界。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。

项目成果

Identity-Aware Facial Age Editing Using Latent Diffusion

使用潜在扩散进行身份感知面部年龄编辑

• DOI: 10.1109/tbiom.2024.3390570
• 发表时间: 2024
• 期刊: and Identity Science
• 作者: Banerjee, Sudipta;Mittal, Govind;Joshi, Ameya;Mullangi, Sai Pranaswi;Hegde, Chinmay;Memon, Nasir
• 通讯作者: Memon, Nasir

Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing

• DOI: 10.1109/satml59370.2024.00019
• 发表时间: 2023-11
• 期刊: 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
• 作者: Ashutosh Nirala;Ameya Joshi;Chinmay Hegde;Soumik Sarkar

True or False: Studying the Work Practices of Professional Fact-Checkers

是非：研究专业事实核查人员的工作实践

• DOI: 10.1145/3512974
• 发表时间: 2022
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Micallef, Nicholas;Armacost, Vivienne;Memon, Nasir;Patil, Sameer
• 通讯作者: Patil, Sameer