Modernizing the Code of Federal Regulations with AI: A Path to Simplicity and Clarity
- Andrew Loposser
- 5 days ago
- 4 min read
White Paper
“Man and Machine” Approach: Human oversight and legal expertise must be the core foundation of any solution. Given the high stakes of federal regulations, AI would serve as an advisory aid, not an autonomous lawmaker. AI can triage and suggest, but experts must interpret AI findings, make judgments on legal/policy merits, and implement changes via the normal rulemaking process. This ensures that any modernization of the CFR upholds democratic accountability, transparency, and substantive due care.
OVERVIEW
The CFR’s Complexity Problem: The U.S. Code of Federal Regulations (CFR) has grown into a vast and convoluted document – over 180,000 pages as of 2025 (up from just 10,000 pages in 1950). Decades of accumulating rules have led to duplication, contradictions, and regulatory bloat with no routine “clean-up” process. This structural complexity makes the CFR difficult to navigate and understand.
Why It Matters: An overly complex CFR undermines government effectiveness and legal clarity and imposes heavy compliance burdens on the public and businesses. When rules overlap or conflict, agencies waste resources, regulated parties struggle to comply, and the economy bears the cost of inefficient regulation. Studies link regulatory accumulation to real costs, from slow economic growth (an estimated $4 trillion GDP loss in 2012 alone due to post-1980 regulatory buildup) to reduced safety as overwhelmed workers inadvertently ignore overly numerous rules.
Real-World Consequences: Three case studies illustrate how CFR complexity causes tangible problems:
Fragmented Food Safety Oversight – e.g. overlapping regulations for pizza and catfish mean multiple agencies inspecting the same food with inconsistent frequency, wasting effort and taxpayer money.
Conflicting Health Privacy Rules – misaligned federal regulations (HIPAA vs. 42 CFR Part 2) for patient records created confusion and hindered care until reforms were enacted.
Environmental Cleanup Delays – contradictory environmental standards from different regulators (federal, state, local) have led to legal battles and cleanup delays, as seen in the Willits, CA case.
AI as a Solution Tool: Artificial Intelligence can help tame the CFR’s complexity. AI-powered text analysis and machine learning can rapidly review the entire CFR corpus to identify redundant provisions, flag potential contradictions, and map overlapping rules across agencies. This can greatly assist in harmonizing regulations and eliminating obsolete rules. For example, AI systems can highlight themes and relationships in hundreds of thousands of pages, pinpointing where multiple regulations address the same issue or where regulatory language conflicts. Early experiments at the state level have been promising – Ohio used an AI tool to find outdated or redundant laws, leading to the removal of 5 million unnecessary words from its code.
Managing the Pitfalls: Using AI for regulatory reform carries risks that need to be managed:
Algorithmic Misinterpretation: AI might misread legal context or nuance, mistakenly marking regulations as contradictory or duplicative when a human expert knows they serve distinct purposes.
Data Bias: If the underlying data or training of an AI tool is biased or incomplete, it could recommend changes that skew policy or overlook the impact on certain groups.
Lack of Explainability: Advanced AI (like deep learning models) can be a “black box,” making it hard to explain why a rule was flagged. Regulators need clear justifications for any changes – a mysterious AI output won’t suffice in legal reasoning.
Accountability: There must be clarity that humans are accountable for decisions. Agencies cannot defer blame to an algorithm; they must only use AI insights that they can validate and defend.
Conclusion: Modernizing the CFR is essential to improve governance, clarity, and economic efficiency. AI offers a powerful new means to cut through the thicket of federal regulations, but it must be deployed carefully. By combining intelligent automation with the judgment of legal and policy experts, Congress and federal agencies can streamline the regulatory code while preserving the safeguards and intents behind the rules. The result would be a leaner, more coherent CFR that better serves both the public interest and those who must abide by the rules.
About the Author
Sid Ghatak is a seasoned AI and analytics executive with three decades of experience and
advanced studies at the University of Michigan and the University of Chicago. As Director of the Data & Analytics Center of Excellence at the U.S. General Services Administration, he used data science to streamline government operations. He later founded Increase Alpha, where his deep-learning neural network has outperformed U.S. equity benchmarks over the last 4 years.
Sid shaped national AI policy by contributing to the White House Executive Order on AI Safety and Trustworthiness and co-authoring the federal AI Maturity Model. Earlier, his analytics-driven strategy at Time Warner generated an 80 percent revenue surge, proving his knack for turning AI insight into business growth. A prolific thought leader and educator, he writes widely on AI and data monetization and created Villanova University’s Master’s Certificate in Business Intelligence to train the next generation of AI professionals. He serves as a Senior Advisor of the National Artificial Intelligence Association (NAIA) – www.theNAIA.org