Artificial Intelligence in Government: The Federal and State Landscape

Summary

This 2024 report from the National Conference of State Legislatures provides a comprehensive examination of how AI is being deployed across U.S. government agencies at both federal and state levels. Unlike typical tech policy reports that focus on regulation, this resource dives deep into the operational realities of AI implementation in public sector environments. It maps the current landscape of government AI initiatives, analyzes emerging governance frameworks, and explores how different jurisdictions are collaborating to tackle shared challenges around ethics, procurement, and talent acquisition in AI projects.

The Federal-State AI Divide: What You Need to Know

The report reveals significant differences in how federal agencies and state governments approach AI adoption. Federal agencies tend to focus on large-scale, standardized implementations with extensive oversight mechanisms, while states are experimenting with more agile, localized solutions. This creates both opportunities for innovation and challenges for interoperability. The report documents specific examples from agencies like the Department of Veterans Affairs using AI for claims processing and states like Colorado implementing AI in unemployment benefits administration, showing how governance approaches vary dramatically based on scale and constituency needs.

Governance Frameworks in Action

Rather than theoretical policy discussions, this report examines actual governance structures being implemented across government entities. It covers emerging practices like AI review boards, algorithmic impact assessments, and cross-agency coordination mechanisms. The resource details how different jurisdictions are handling vendor relationships, data sharing agreements, and public transparency requirements. Particularly valuable are the case studies showing how states are adapting federal guidance to their specific legal and operational contexts.

Collaboration Models That Work

One of the report's unique strengths is its focus on intergovernmental collaboration in AI initiatives. It documents successful partnership models, from multi-state consortiums sharing AI development costs to federal-state data sharing agreements that enable more effective AI training. The resource includes specific examples of how jurisdictions are pooling resources for AI talent acquisition and creating shared standards for AI procurement that smaller governments can leverage.

Who This Resource Is For

• State and local government officials developing AI strategies and need to understand what peer jurisdictions are implementing
• Federal program managers seeking to understand how their AI initiatives impact state and local operations
• Government contractors and vendors who need to navigate the complex landscape of public sector AI procurement and compliance requirements
• Policy researchers and advocates tracking the evolution of AI governance in government settings
• Legal counsel advising government entities on AI implementation, procurement, and compliance issues
• Academic researchers studying public sector innovation and intergovernmental relations in emerging technologies

Implementation Roadmap

The report provides practical guidance for government entities at different stages of AI adoption. It outlines a progression from initial pilot programs through scaled implementation, with specific attention to the unique constraints of government operations like budget cycles, procurement rules, and public accountability requirements. The resource includes templates and checklists that government officials can adapt for their own AI governance processes, making it immediately actionable rather than purely informational.

Watch Out For

This report reflects the rapidly evolving nature of government AI adoption, meaning some specific initiatives and policies referenced may have changed since publication. The focus is primarily on operational and administrative AI applications rather than more controversial uses like predictive policing or automated decision-making in benefits determination. Additionally, while the report covers collaboration models, it doesn't deeply address the legal and jurisdictional complexities that can complicate multi-state AI initiatives.