The Local Innovation Unit: Achieving National Goals Through Local Experimentation
Summary
The Biden-Harris Administration should create the Local Innovation Unit (LIU) to catalyze and coordinate decentralized, city and county-based experiments focused on the most urgent and complex challenges facing the United States. Traditional “top-down” methods of policy design and problem solving are no longer effective in addressing our nation’s most pressing issues, such as pandemics, climate change, and decreasing economic mobility. The nature of these problems, coupled with an absence of tested solutions or “best practices” and ongoing partisan gridlock, demands a more agile and experimental “bottom-up” approach. Such an approach focuses on empowering coalitions of social innovators at the local level—including local governments, private-sector businesses, community-based organizations, philanthropists, and universities—to design and test solutions that work for their communities. Promising solutions can then be scaled horizontally (e.g., to other cities and counties) and vertically (e.g., to inform federal policy and action).
The LIU will be a place-based policy initiative consisting of two primary components: (1) multi-city and county experimentation cohorts organized around common problems, via which local coalitions design and test solutions within their communities, and (2) a digital platform, housed in the Department of Housing and Urban Development (HUD), that will help LIU participants connect, exchange materials and resources, help participants collect and visualize data, evaluate solutions, and publish lessons learned.
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.