Legislators cannot rely on understanding every line of code or weight in a neural network to govern. Instead, they must shift their focus from how a model works internally to what the model actually does. This means regulating outcomes rather than architecture. If a system causes real-world harm, such as generating dangerous biological instructions or spreading systemic bias, the technical 'black box' defense fails to hold up in a court of law.
Governments should mandate rigorous third-party audits and red-teaming protocols. By requiring companies to submit their models to independent testers—similar to how the FDA tests new drugs—the state bypasses the need to master high-level mathematics. We can also implement strict liability frameworks. If a company profits from an autonomous agent, they must remain legally responsible for its actions. This creates a massive financial incentive to prioritize safety over speed.
Finally, we need standardized testing suites. Instead of debating the nuances of stochastic parrots, lawmakers can demand evidence of performance against specific, predefined safety metrics. When developers prove their models meet these benchmarks through reproducible tests, they gain social license. If they refuse, the lack of transparency becomes a legal liability, not a shield.
