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，试图了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调解我们对物理世界的认识和与物理世界的互动。然而，已有证据表明，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，从在服务不足的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——例如处理地理数据稀疏性的新地理数据结构和策略——这将帮助他们确保未来的位置感知技术在任何地方、对每个人都最有效。本研究将聚焦于两个位置感知技术家族：基于地理信息的志愿技术和位置感知对等经济技术。这些项目包括以下工作：(1)模拟和解决地理参考用户生成内容的覆盖差距；(2)增加我们对算法如何无意中采用偏见（例如以城市为中心）世界观的理解，并关键地确定纠正这些偏见的方法；(3)阐明重要的对等经济技术（如拼车系统和点对点借贷和租赁平台）中价格和服务质量的地理差异。并为服务不足的地区制定解决方案。该项目整合了研究和教学活动，并将为现有的大规模开放在线课程开发新材料，使其能够接触到大量不同的受众。

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.