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A state is letting an algorithm read the rulebook—and then asking people to decide what to change. We head to Virginia to unpack the “agentic AI” pilot that scans statutes, regulations, and guidance to flag contradictions, redundancies, and unclear language, promising cleaner code for public life. The vision is compelling: fewer dead ends for citizens and small businesses, faster updates as laws evolve, and a maintainable regulatory corpus that doesn’t require a crisis to fix.
We walk through how the tool triages massive text, the kinds of suggestions it can generate, and where human judgment stays firmly in charge. Alongside the upside, we get specific about risk: explainability in a legal domain, bias that could over-target certain protections, and the danger of treating speed as a substitute for process. Accountability is the throughline—who signs off, who audits the outputs, and how courts and legislatures can see a transparent trail from machine suggestion to human decision.
Beyond the mechanics, we dig into the politics and the guardrails that make innovation legitimate: public logs, before–after drafts, independent audits, risk tiers for sensitive domains, and rollback plans when changes misfire. We also map the bigger picture: states adopting AI for internal governance, the potential for fragmentation if approaches diverge, and the likely federal response. Most importantly, we share how listeners can engage—request transparency from representatives, show up for comment windows, and support civil society groups that stress-test these systems. If AI is going to touch regulation, it must do so in the open, with people in the loop and trust as the benchmark.
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