AI Safety

Software-style gradual rollouts could transform government policy making

What if new laws were A/B tested like code updates before national rollout?

Deep Dive

Yair Halberstadt argues that governments currently enact sweeping policy changes without the rigorous testing that even minor software updates require. While developers use dev/staging environments, unit tests, gradual rollouts, and rollback plans, lawmakers often YOLO major reforms like school system overhauls across the entire country. The proposed solution: treat legislation like a software deployment.

Halberstadt outlines a process where legislators draft a bill specifying granularity (e.g., individual schools, local councils). An oversight committee randomly selects a subset of entities, defines testing duration and success criteria, monitors data, and decides to rollback, extend, or rollout nationwide. This randomization isolates causal impact from confounders. Testing this against 10 UK acts of parliament from 2026, Halberstadt found 3 would work perfectly, 5 could be shoehorned with complexity, 1 infeasible, and 1 irrelevant. Examples include 'Children's Wellbeing and Schools' (high feasibility at Local Education Authorities level) and 'Crime and Policing' (high at Territorial Police Forces).

Key Points
  • Proposes using random selection of entities (school districts, police forces) to test policies before national rollout
  • Oversight committee sets testing period, success criteria, and decides on rollback, extension, or full rollout
  • Analysis of 10 UK acts: 3 perfectly feasible, 5 feasible with complexity, only 1 infeasible

Why It Matters

Applies tech's rigorous testing culture to governance, potentially reducing costly policy failures and enabling data-driven legislation.

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