🧪 Artificial Intelligence Ai Governance Simulation

This simulation demonstrates the need for governance frameworks in the development and deployment of artificial intelligence by modelling one as a working control system: submitted AI systems flow through an intake queue, wait for a limited pool of auditors, and are approved into production only if they clear a risk-tolerance gate. A feedback controller watches a live systemic-risk score built from the average risk of deployed systems and the recent incident rate, and automatically throttles new submissions whenever that score crosses a policy threshold — the same reflex real regulators and internal AI governance boards use when incidents spike. Tune the submission rate, audit capacity, throttle threshold and approval tolerance to see queues build, risk climb, throttling kick in, and the system settle back into equilibrium.