Investing in “Privacy-at-the-Sensor” Civic Technologies to Advance Next-Gen American Infrastructure
Summary
The National Science Foundation (NSF) and the Department of Energy (DOE) should invest in a cohort of civic technologies that advance the next generation of American infrastructure while prioritizing individual privacy protections.
Our nation’s infrastructure is in urgent need of upkeep and replacement. The next generation of American infrastructure should be designed and built to be resilient, energy efficient, and integrate harmoniously with network communications, autonomous vehicles, and other “smart” systems. Emerging civic technologies — such as sensors, computers, and software that can support billing and payment, manage public resources, monitor integrity of structures, track traffic flows, and more — can improve the performance of future infrastructure and improve community livability. However, the public often believes that civic technologies invade individual privacy and enrich tech companies. Public distrust has disrupted multiple civic-technology projects around the world.
The federal government should invest in a suite of research and development (R&D) activities to develop new, sensor-based civic technologies that inherently preserve privacy in a manner verifiable by citizens. The federal government should also invest in complementary activities to promote adoption and acceptance of such “privacy-at-the-sensor” technologies. Such activities could include setting standards for the privacy properties of civic technologies, establishing technology test beds, funding public grants to encourage adoption of privacy-preserving sensing technologies, and creating partnerships with external stakeholders interested in civic technologies.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.