AI-DCL: EAGER: Fairness-aware Informatics System for Enhancing Disaster Resilience

AI-DCL：EAGER：增强抗灾能力的公平意识信息系统

基本信息

批准号：
1927513
负责人：
Mingxuan Sun
金额：
$ 30万
依托单位：
Louisiana State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927513&HistoricalAwards=false
关键词：
AI DCL EAGER Fairness aware

项目摘要

This award supports a research project to develop a smart, fairness-aware, emergency informatics system. The system would automatically collect disaster-related data for real-time event monitoring and prediction making to better coordinate search and rescue operations. The system could, for example, automatically collect real-time victim event data from social media such as Twitter, utilize predictive algorithms to capture the spatiotemporal dynamics associated with those events, forecast future events, and direct rescue teams in response. Such systems would be useful to state and local government agencies for resource allocation and planning. For the public to support their implementation, steps are needed to ensure that they operate fairly; it is well known that decisions made by algorithms generated by machine learning techniques often exhibit bias due to a number of factors including data bias and the design of algorithm models. A rescue system based only on Twitter data, for example, may exhibit socioeconomic bias since higher disaster-related Twitter-use communities tend to be communities of higher socioeconomic status. To address fairness concerns, a prototype will be tested and verified using Twitter data as well as data collected from other sources in response to Hurricane Harvey. The approach could be applied to various types of emergency situations including earthquakes and fires. The project is interdisciplinary; the research team includes an expert in computer science and artificial intelligence, and another in geography and spatial sciences. Two graduate research assistants will also be involved in the project, which will deepen their understanding of machine learning, data analytics, and environmental social science; as a result, the project will contribute to capacity building for interdisciplinary research. Results of this project will also be incorporated into course materials and classroom activities.The central goal of this research project is to develop a fairness-aware AI system for emergency management. The project involves formulating and testing reliable principles and methods to adjust the AI algorithms for fairness, a very domain specific challenge. This is especially true in emergency management, where the system has to be able to predict rescue events in real time from large, noisy, and biased data, such as Twitter data. In light of this, the research team will develop a novel point process model for event prediction from streaming data, and it will investigate statistical learning problems when event data are noisy and incomplete. To adjust for the fairness of the prediction algorithm, the team will integrate heterogeneous social and geographical data with varying degrees of granularity and different levels to build a classic event prediction model and to examine correlations between the two approaches. Through comparing the approaches (with and without fairness adjustment) using an empirical example (Hurricane Harvey), the project will reveal the patterns of disparities, if any, and add new knowledge on community resilience and emergency management. Theory, models, and software all together form a framework that leads to scientific advances to further development in disaster resilience. This interdisciplinary research will serve to advance our understanding of machine learning, data science, and socioeconomic fairness in the management of environmental hazards. New methods will be developed to tackle incomplete and biased data and to integrate them with other components of emergency informatics systems. The approach will be applicable to many other AI system developments efforts.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.

该奖项支持一个研究项目，以开发智能，公平感知的紧急信息系统。该系统将自动收集与灾害相关的数据进行实时事件监控和预测制作，以更好地协调搜索和救援操作。例如，该系统可以自动从Twitter等社交媒体（例如Twitter）收集实时受害者事件数据，它利用预测算法来捕获与这些事件相关的时空动态，预测未来事件以及直接救援团队响应。这样的系统对于国家和地方政府机构的资源分配和计划将很有用。为了使公众支持其实施，需要采取步骤以确保他们公平运作；众所周知，机器学习技术产生的算法做出的决策通常由于许多因素，包括数据偏见和算法模型的设计，通常会出现偏见。例如，仅基于Twitter数据的救援系统可能会表现出社会经济偏见，因为与灾难有关的较高的Twitter使用社区往往是具有较高社会经济地位的社区。为了解决公平的问题，将使用Twitter数据以及从其他来源收集的数据来测试和验证原型。该方法可以应用于各种紧急情况，包括地震和大火。该项目是跨学科的；该研究团队包括计算机科学和人工智能的专家，以及另一个地理和空间科学专家。该项目还将参与两名研究生研究助理，这将加深他们对机器学习，数据分析和环境社会科学的理解。结果，该项目将有助于跨学科研究的能力建设。该项目的结果还将纳入课程材料和课堂活动中。该研究项目的核心目标是开发一种公平意识的AI系统，用于应急管理。该项目涉及制定和测试可靠的原理和方法，以调整AI算法以确保公平性，这是一个非常特定的域名挑战。在紧急管理中尤其如此，该系统必须能够从大型，嘈杂和有偏见的数据（例如Twitter数据）实时预测救援事件。鉴于此，研究团队将开发出从流数据中的事件预测的新颖点过程模型，当事件数据嘈杂且不完整时，它将研究统计学习问题。为了调整预测算法的公平性，该团队将以不同程度的粒度和不同级别的不同程度整合异质的社会和地理数据，以构建经典的事件预测模型并检查两种方法之间的相关性。通过使用经验例子（Harvey Harvey）比较方法（有和没有公平调整），该项目将揭示差异模式（如果有），并增加有关社区韧性和紧急管理的新知识。理论，模型和软件共同构成了一个框架，从而导致科学进步，以进一步发展灾难弹性。这项跨学科的研究将有助于我们在环境危害管理中对机器学习，数据科学和社会经济公平性的理解。将开发新的方法来解决不完整和偏见的数据，并将其与紧急信息系统的其他组件集成在一起。该方法将适用于许多其他AI系统的开发工作。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Game-Theoretic Approach to Achieving Bilateral Privacy-Utility Tradeoff in Spectrum Sharing

DOI：
10.1109/globecom42002.2020.9322123
发表时间：
2020-12
期刊：
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
影响因子：
0
作者：
Mengmeng Liu;Xiangwei Zhou;Mingxuan Sun
通讯作者：
Mengmeng Liu;Xiangwei Zhou;Mingxuan Sun

Human Action Image Generation with Differential Privacy

具有差分隐私的人类动作图像生成

DOI：
10.1109/icme46284.2020.9102767
发表时间：
2020
期刊：
IEEE International Conference on Multimedia and Expo (ICME
影响因子：
0
作者：
Sun, Mingxuan;Wang, Qing;Liu, Zicheng
通讯作者：
Liu, Zicheng

A Machine Learning Approach for Detecting Rescue Requests from Social Media

DOI：
10.3390/ijgi11110570
发表时间：
2022-11
期刊：
ISPRS Int. J. Geo Inf.
影响因子：
0
作者：
Zheye Wang;N. Lam;Mingxuan Sun;Xiao Huang;Jin Shang;Lei Zou;Yue Wu;V. Mihunov
通讯作者：
Zheye Wang;N. Lam;Mingxuan Sun;Xiao Huang;Jin Shang;Lei Zou;Yue Wu;V. Mihunov

Bilateral Privacy-Utility Tradeoff in Spectrum Sharing Systems: A Game-Theoretic Approach