Machine-Interpretable Rule Systems

Unsnarling the Red Tape:

Machine-Interpretable Rule Systems for Efficient Regulatory Compliance

March 17 2026, 3-4PM Eastern Time (Over Zoom)

Regulatory complexity is increasingly recognized as a drag on innovation, public trust, and economic growth, with regulatory sprawl estimated to reduce U.S. GDP growth by nearly a full percentage point annually. At the same time, governments at the federal, state, and city levels are actively seeking ways to modernize permitting, environmental review, financial regulation, and technical standards—areas already identified by the White House as targets for increased efficiency. This panel will examine how advances in computing research can enable a systematic transition from natural-language regulations to reliable, machine-interpretable rule systems.  Many modern applications (especially financial systems) operate on a real-time basis, where automated application of rules and regulations, along with proofs of compliance, have immense value.  

The discussion will focus on two tightly coupled challenges. First, what would it take to identify and prioritize the classes of rules most amenable to computational encoding—such as engineering and design standards, permitting workflows, environmental impact assessment, and financial regulations—where prescriptive structure already exists but adoption lags behind technological availability? Second, what new research is required to make large-scale regulatory encoding feasible, including AI-assisted text analysis, program synthesis, formal verification, formally provable public verification, human-in-the-loop validation, and governance mechanisms that preserve accountability and legal intent?

Panelists will highlight how recent breakthroughs in AI dramatically lower the manual burden that has historically made such efforts impractical, enabling analysis and validation of complex regulatory corpora at unprecedented scale. The panel will articulate a concrete research and policy agenda for the computing community—identifying opportunities for federal investment, cross-sector collaboration, and shared infrastructure—to ensure that AI-enabled regulation advances efficiency, transparency, and economic competitiveness while remaining aligned with democratic values.

Panel Chairs

Vinay K. Chaudhri, Knowledge Systems Research LLC

Hank Korth, Lehigh University

Panelists

Patrick MacLaughlin, Hoover Institute Stanford

Leora Morgenstern, SRI International

Wee Kee Toh, JPMorgan Chase

Jaromir Savelka, Carnegie Mellon University

Registration Information

The panel is being hosted by the Computing Research Association Industry Group. Please visit the registration page to register for the panel and get the zoom details.