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

该奖项支持一项研究项目，该项目旨在开发一种智能、公平意识的紧急信息系统。该系统将自动收集与灾害有关的数据，以进行实时事件监测和预测，从而更好地协调搜救行动。例如，该系统可以自动从诸如Twitter的社交媒体收集实时受害者事件数据，利用预测算法来捕获与这些事件相关联的时空动态，预测未来事件，并指导救援队进行响应。这样的系统将有助于州和地方政府机构进行资源分配和规划。为使公众支持其实施，需要采取步骤确保其公平运作；众所周知，由于数据偏差和算法模型设计等多种因素，由机器学习技术生成的算法所作出的决策经常表现出偏差。例如，仅基于Twitter数据的救援系统可能会显示出社会经济偏见，因为与灾难相关的Twitter使用率较高的社区往往具有较高的社会经济地位。为了解决公平问题，将使用Twitter数据以及从其他来源收集的数据对原型进行测试和验证，以应对飓风哈维。这种方法可以应用于各种类型的紧急情况，包括地震和火灾。该项目是跨学科的；研究团队包括一名计算机科学和人工智能专家，以及另一名地理和空间科学专家。两名研究生研究助理也将参与该项目，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；因此，该项目将有助于跨学科研究的能力建设。该项目的成果还将被纳入课程教材和课堂活动。该研究项目的中心目标是开发一个用于应急管理的公平感知人工智能系统。该项目涉及制定和测试可靠的原则和方法，以调整人工智能算法的公平性，这是一个非常特定领域的挑战。这在应急管理中尤其如此，系统必须能够从大量、噪声和有偏见的数据(如Twitter数据)中实时预测救援事件。有鉴于此，研究小组将开发一种新颖的点过程模型，用于从流数据中进行事件预测，并将研究事件数据存在噪声和不完整时的统计学习问题。为了调整预测算法的公平性，该团队将整合不同粒度和不同级别的不同社会和地理数据，以构建一个经典的事件预测模型，并检查两种方法之间的相关性。通过使用一个经验例子(飓风哈维)比较这些方法(有和没有进行公平调整)，该项目将揭示差异的模式，如果有的话，并增加关于社区复原力和紧急情况管理的新知识。理论、模型和软件一起构成了一个框架，该框架导致了科学进步，从而进一步发展了灾难恢复能力。这项跨学科的研究将有助于促进我们对机器学习、数据科学和环境危害管理中的社会经济公平的理解。将开发新的方法来处理不完整和有偏见的数据，并将它们与紧急信息系统的其他组成部分相结合。该方法将适用于许多其他人工智能系统开发工作。这一奖项反映了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。

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.