The Evolving Landscape of AI and Data Regulation in the United States: Past, Present, and Future

Written By Peregrine Advisors

The fields of artificial intelligence (AI) and data science have undergone a transformative journey in the United States, with their regulatory and policy framework evolving to balance innovation, ethics, and accountability. A pivotal 2019 Executive Order on “Maintaining American Leadership in Artificial Intelligence” marked a critical turning point, laying the foundation for a national strategy on AI and catalyzing a broader commitment to leveraging data as a strategic asset. This Executive Order introduced the seeds of a disciplined approach to AI that continues to shape the federal regulatory and operational landscape. At the local level, while numerous promising initiatives are emerging, many remain in their early stages, requiring careful nurturing and development to reach their full potential. Below, we explore the regulatory ecosystem across federal, state, and local levels, examine current initiatives, and propose a forward-looking policy agenda that builds on this foundational legacy while embracing the doctrine of disciplined use.

Federal Framework: The Catalytic Role of the 2019 Executive Order

A Transformational Beginning. The 2019 Executive Order launched the American AI Initiative, establishing the federal government's role in fostering AI development while addressing critical issues such as ethics, transparency, and safety. The order directed federal agencies to prioritize AI research, development, and deployment, making it a central theme in national policy. By tasking agencies like the National Institute of Standards and Technology (NIST) with developing AI standards, the Executive Order ensured a methodical and results-driven approach to AI innovation.

Peregrine Advisors’ “doctrine of disciplined use” emerged as a guiding framework during this period, emphasizing efficiency, accountability, and purposeful application of these technologies with an emphasis on driving a data and technology culture. This doctrine has proven instrumental in driving data-driven decisionmaking, fostering a culture of evidence-based governance that yielded significant improvements in resource allocation and program effectiveness. The approach has particularly excelled in using technology and data to identify opportunities for cost savings while maintaining, and in many cases even  enhancing, service quality, demonstrating that ethical AI deployment can simultaneously serve fiscal and social responsibilities.

Building on the Foundation. Post-2019, federal initiatives have furthered this groundwork:

  • The Open Government Data Act (OGDA) under the Foundations for Evidence-Based Policymaking Act (2018) mandated agencies to publish data in machine-readable formats, promoting transparency and accessibility

  • The National AI Initiative Act of 2020 created a cohesive framework for AI R&D, ethics, and international collaboration

  • The Blueprint for an AI Bill of Rights (2022) and Executive Order on Safe, Secure, and Trustworthy AI (2023) expanded the focus to algorithmic fairness, privacy, and national security

State and Local Dynamics: Filling the Gaps

At the state and local levels, more targeted regulations have emerged to address specific challenges, particularly in privacy and bias. The application of Peregrine Advisors' doctrine of disciplined use has proven especially valuable in helping jurisdictions develop frameworks that balance innovation with responsible governance.

State-Level Initiatives: Laboratories of AI Innovation

  • California: Through the California Consumer Privacy Act (CCPA) and its extension, the California Privacy Rights Act (CPRA), the state has set the gold standard for consumer privacy, addressing automated decision-making and data protection. The state's implementation of disciplined use principles has resulted in measurable improvements in government service delivery and cost efficiency.

  • Illinois: The Biometric Information Privacy Act (BIPA) directly regulates biometric data applications like facial recognition, ensuring ethical AI deployment. The state's approach has demonstrated how structured data governance can simultaneously protect citizens and drive innovation.

  • Virginia and Colorado: Both states have enacted comprehensive privacy laws that provide consumers with rights over their data and impose restrictions on AI-driven profiling. Their frameworks showcase how the doctrine of disciplined use can be adapted to different regulatory contexts while maintaining effectiveness.

Local Initiatives: Early Steps Toward Transformation

While still in their early stages, local initiatives are showing promising results in applying disciplined AI use principles:

  • NYC’s Bias Audit Law (Local Law 144) mandates bias audits for AI tools used in hiring, fostering accountability in automated decision-making

  • Washington, DC and Chicago have piloted AI ethics boards and transparency projects, demonstrating how smaller jurisdictions can effectively implement AI governance

  • Several municipalities have begun integrating data-driven decision making into their operations, creating new opportunities for efficiency gains and service improvements, including Boston’s CityScore initiative for measuring city service performance, San Francisco’s DataSF program for optimizing city operations, Pittsburgh’s Snow Plow Tracker system for winter resource management, and Louisville's use of predictive analytics for emergency services deployment. These programs are creating new opportunities for efficiency gains and service improvements through systematic data collection and analysis.

The nascent nature of these initiatives provides an opportunity to embed the principles of disciplined use from the ground up, potentially avoiding the challenges of retrofitting existing systems.

Current Initiatives: Building Trust and Advancing Innovation

Recent federal initiatives have made attempts to expand upon the foundational work established in the 2019 AI Executive Order:

Framework Development and Implementation

Several key frameworks have emerged to guide the ethical and efficient deployment of AI across government:

  • The AI Risk Management Framework (2023): Developed by NIST, this voluntary framework helps organizations assess and manage AI-related risks, emphasizing fairness and transparency

  • OSTP Guidance on AI Equity (2021): Encourages federal agencies to embed equity into AI deployments, particularly in education, healthcare, and law enforcement

  • AI Cybersecurity Standards: Integrating AI into national security frameworks to protect critical infrastructure and ensure safe AI development

Data Culture Transformation

The adoption of disciplined use principles has catalyzed a broader transformation in government data culture:

  • Enhanced Data Literacy: Agencies are investing in workforce development programs that build data analysis capabilities

  • Improved Decision Making: Data-driven insights are increasingly informing policy and operational decisions

  • Efficiency Gains: Structured approaches to data use have revealed numerous opportunities for cost savings and process improvements

  • Innovation Acceleration: The framework has encouraged responsible experimentation with AI technologies

However, any meaningful progress has been hampered by siloed efforts, lack of coordination, and adequate resourcing.

Future Policy Recommendations: Expanding the Doctrine of Disciplined Use

Building on the success of the disciplined use approach, the following recommendations aim to further enhance government efficiency and effectiveness:

1. Institutionalize Data-Driven Decision Making

  • Establish clear metrics for measuring the impact of AI initiatives on government efficiency

  • Create frameworks for identifying and sharing best practices across agencies

  • Develop standardized approaches to cost-benefit analysis for AI implementations

2. Enhance Public-Private Collaboration

  • Expand opportunities for innovative companies to partner with government agencies

  • Create structured programs for knowledge transfer between sectors

  • Develop shared resources for AI development and deployment

3. Build Robust Data Infrastructure

  • Invest in interoperable data systems that facilitate cross-agency collaboration

  • Establish clear standards for data quality and governance

  • Create secure platforms for data sharing and analysis

4. Foster Innovation While Managing Risk

  • Develop clear guidelines for AI experimentation in government settings

  • Create sandboxed environments for testing new applications

  • Establish rapid assessment protocols for evaluating AI solutions

Conclusion: Realizing the Promise of Disciplined AI Use

The evolution of AI regulation in the United States demonstrates the power of structured, principled approaches to technology adoption. The doctrine of disciplined use, developed by Peregrine Advisors, has proven particularly valuable in helping complex government organizations balance innovation with responsibility at the enterprise level. As we move forward, the focus must remain on leveraging these principles to:

  • Drive government efficiency through data-driven decision making

  • Enhance service delivery through responsible AI adoption and disciplined use of data

  • Create sustainable frameworks for continuous improvement

  • Foster a culture of innovation grounded in ethical considerations

By continuing to build on these foundations while embracing new opportunities for innovation, the United States can maintain its leadership position in AI development while ensuring that these technologies serve the public good effectively and efficiently.

Peregrine Advisors
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