EAGER: SAI: Community-Informed Surveillance Infrastructure for Public Safety and Equity

加强美国基础设施（SAI）是NSF的一项计划，旨在促进以人为本的基础和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛改善生活质量奠定了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，促进经济增长，创造就业机会，提高公共部门服务提供的效率，加强社区建设，促进机会平等，保护自然环境，增强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策，治理以及社会和文化过程的知识如何使建设和维护有效的基础设施，改善生活和社会，并建立在技术和工程的进步。收集人工智能处理数据的安全摄像头正在被广泛采用，作为美国城市公共安全基础设施的关键要素。新的公私社区伙伴关系模式进一步推动了这一发展-警察局、市政府、私营企业主和社区官员的合作。该技术旨在保护建筑物并帮助警方识别罪犯，实现面部识别和人员识别。公民和学者对这些监控技术中的偏见表示担忧，这些偏见可能会使偏见永久化，并进一步使贫困社区处于不利地位。该项目正在调查监视基础设施如何影响社区权力关系，以及这种系统是否会对正在经历边缘化的社区造成意想不到的后果。该项目正在调查个人和社区如何看待监控基础设施并与之互动，以及个人和社区对公共安全的需求是否得到满足。该项目的最终目标是在美国建立和发展公平公正的公共安全基础设施。通过民族志和社区参与的方法，该项目审查了过去和正在进行的监测基础设施的转型，以设想在经历边缘化的社区的公共安全基础设施的未来。拟议的研究正在确定对安全的看法以及围绕数据驱动的监控和公共安全基础设施中的公私社区伙伴关系的任何紧张关系。该研究将揭示在开发和实施公共安全基础设施方面公私伙伴关系所产生的社会，技术和政治影响。它正在调查公众成员和那些经历边缘化的人的声音、历史和经历。它将提出公平的公共安全基础设施在未来可能是什么样子，并为未来公共安全基础设施的设计和开发过程提供信息。贡献包括监督研究和关键基础设施研究的经验和理论见解。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Security cameras that collect data processed with artificial intelligence are being widely adopted as a key element of urban public safety infrastructure in the United States. This development is enabled further by a new public-private-community partnership model--a collaboration of the police department, city government, private business owners, and community officials. The technology, meant to secure buildings and help police identify criminals, enable facial recognition and identification of people. Citizens and scholars have raised concerns about biases embedded in these surveillance technologies that can perpetuate prejudices and further disadvantage poor communities. This project is investigating how surveillance infrastructure affects community power relationships and whether such systems could lead to unintended consequences for communities experiencing marginalization. The project is investigating how individuals and communities perceive and interact with surveillance infrastructure, and whether individuals’ and communities’ needs for public safety are fulfilled. The project's ultimate aim is to create and develop fair and equitable public safety infrastructures in the United States. Through an ethnography and a community-based participatory approach, this project examines past and ongoing transformations of surveillance infrastructure in order to envision the future of public safety infrastructure in communities experiencing marginalization. The proposed research is identifying both perceptions of safety and any tensions around the public-private-community partnership in data-driven surveillance and public safety infrastructure. The research will uncover the social, technical, and political impact resulting from the public-private partnership in developing and implementing public safety infrastructure. It is investigating the voices, history, and experiences of members of the public and those experiencing marginalization. It will propose what equitable public safety infrastructure might look like in the future and inform the design and development process of future public safety infrastructure. Contributions include empirical and theoretical insights to surveillance studies and critical infrastructure studies. This investigation will also advance the fields of social computing concerning fairness, justice, and equity of socio-technical infrastructure and systems.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.