CAREER: Understanding and Addressing Geographic Inequalities in Location-Aware Technologies

该研究项目将探索当前位置感知技术的局限性，寻求理解工作中的因果动态，并提出计算和社会技术改进。 本地搜索系统、对等经济平台、智能路由技术和其他地理算法和系统越来越多地介导我们对物理世界的了解和与物理世界的互动。然而，已经开始出现的证据表明，这些技术在某些类型的地区（如城市）比在其他地区（如农村）效果更好。如果成功，这项研究将为目前位置感知技术服务不足的地区带来各种各样的好处，从在服务不足地区更准确的算法到涉及执行与位置感知技术相关的地面任务的新就业机会。这项工作还将为技术设计人员提供新的工具和技术，例如新的地理数据结构和处理地理数据稀疏性的策略，这将有助于他们确保未来的位置感知技术在任何地方都能最大限度地为每个人发挥作用。这项研究将集中在两类位置感知技术上：自愿提供基于地理信息的技术和了解地点的同行经济技术。这些项目包括以下工作：（1）建模和解决地理参考用户生成内容的覆盖范围差距，（2）增加我们对算法如何无意中采用偏见的理解（例如以城市为中心的）世界观，并批判性地找出纠正这些偏见的方法，以及（3）揭示拼车系统和点对点等重要对等经济技术中价格和服务质量的地理差异同行借贷和租赁平台，并为服务不足的地区开发解决方案。 该项目整合了研究和教学活动，并将为现有的大规模开放式在线课程开发新的材料，该课程覆盖了大量不同的受众。

This research project will explore the current limitations of location-aware technologies, seek to understand the causal dynamics at work, and propose computational and sociotechnical improvements. Local search systems, peer economy platforms, intelligent routing techniques and other geographic algorithms and systems increasingly mediate our knowledge of and interactions with the physical world. However, evidence has begun to emerge that these technologies work better in some types of areas (e.g. urban) than in others (e.g. rural). If successful, this research will lead to a wide variety of benefits for areas currently under-served by location-aware technologies, benefits ranging from algorithms that are more accurate in under-served areas to new employment opportunities that involve performing on-the-ground tasks associated with location-aware technologies. The work will also provide technology designers with new tools and techniques - such as new geographic data structures and strategies for handling geographic data sparsity - that will help them ensure that the location-aware technologies of the future are maximally effective everywhere and for everyone.This research will focus on two families of location aware-technologies: volunteered geographic information-based technologies and location-aware peer economy technologies. The projects include work that will (1) model and address coverage gaps in geographically-referenced user-generated content, (2) increase our understanding of how algorithms can unintentionally adopt biased (e.g. urban-focused) worldviews and, critically, identify ways to correct for these biases, and (3) shed light on the geographic variation of price and service quality in important peer economy technologies like ride-sharing systems and peer-to-peer borrowing and rental platforms, and develop solutions for under-served areas. This project integrates research and teaching activities, and will develop new material for an existing massive open online course, which reaches a large and diverse audience.