Towards Fairness-Aware Disaster Informatics: an Interdisciplinary Perspective
Towards Fairness-Aware Disaster Informatics: an Interdisciplinary Perspective
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DOI：
10.1109/access.2020.3035714
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Yang Yang-Yang;Cheng Zhang;Chao Fan;A. Mostafavi;Xia Hu
中科院分区：
文献类型：
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作者：
Yang Yang-Yang;Cheng Zhang;Chao Fan;A. Mostafavi;Xia Hu
在灾害期间从众包和传统传感技术中收集信息，为利用这一新的数据源加强态势感知、救灾和救援协调以及影响评估提供了机会。灾害/危机信息学的发展提供了处理多模式数据和实施分析以支持灾害管理任务的能力。然而，人们对灾害信息学的公平性以及这一问题在多大程度上影响救灾工作知之甚少。经常被忽视的是，现有的数据分析方法是否反映了平等社区的影响，特别是服务不足的社区（即，少数民族、老年人和穷人）。我们认为，灾害信息学还没有系统地确定公平问题，这种差距可能会导致问题的决策和协调的灾害响应和救济。此外，公平、机器学习和灾害信息学领域的孤立性阻碍了这些追求之间的交流。本文基于现有的灾害信息学方法和公平性评估标准，通过评估灾害信息学任务中潜在的公平性问题来弥合知识鸿沟。具体来说，我们确定潜在的公平性问题，在灾害事件检测和影响评估任务。我们回顾了现有的方法，通过修改数据，分析和输出来解决潜在的公平问题。最后，本文提出了一个总体的公平意识的灾害信息框架，以结构减轻公平问题的工作流程。本文不仅揭示了灾害信息学方法中公平性问题被忽视的重要方面，而且还弥合了阻碍灾害信息学研究人员和机器学习研究人员之间理解公平性的孤岛。
Collection of information from crowdsourced and traditional sensing techniques during a disaster offers opportunities to exploit this new data source to enhance situational awareness, relief, and rescue coordination, and impact assessment. The evolution of disaster/crisis informatics affords the capability to process multi-modal data and to implement analytics in support of disaster management tasks. Little is known, however, about fairness in disaster informatics and the extent to which this issue affects disaster response. Often ignored is whether existing data analytics approaches reflect the impact of communities with equality, especially the underserved communities (i.e., minorities, the elderly, and the poor). We argue that disaster informatics has not systematically identified fairness issues, and such gaps may cause issues in decision making for and coordination of disaster response and relief. Furthermore, the isolating siloed nature of the domains of fairness, machine learning, and disaster informatics prevents interchange between these pursuits. This paper bridges the knowledge gap by evaluating potential fairness issues in disaster informatics tasks based on existing disaster informatics approaches and fairness assessment criteria. Specifically, we identify potential fairness issues in disaster event detection and impact assessment tasks. We review existing approaches that address potential fairness issues by modifying the data, analytics, and outputs. Finally, this paper proposes an overarching fairness-aware disaster informatics framework to structure the workflow of mitigating fairness issues. This paper not only unveils both the ignored and essential aspects of fairness issues in disaster informatics approaches but also bridges the silos which prevent the understanding of fairness between disaster informatics researchers and machine-learning researchers.