The trajectory of transformative AI poses global catastrophic risks that can only be mitigated through precise, technically-grounded policy. However, a significant "literacy gap" exists at the highest levels of governance. In my and my colleagues' interactions with high-ranking AI policymakers in the EU and US, we discovered that many decision-makers have never directly interfaced with AI models. Instead, they rely on anecdotal evidence and media narratives, lacking the basic scientific fundamentals required to effectively model the systems they intend to regulate.

This project operates on the premise that effective policy—even at the highest level of goal-setting—requires a conceptual "mental model" of the technology’s failure modes. By providing interactive Streamlit applications, we move stakeholders from passive consumers of information to active participants. When a policymaker personally trains a classifier and observes its total collapse under a simple adversarial attack, the abstract risk of "brittleness" or "alignment failure" becomes a tangible reality.

Our contribution to mitigating catastrophic risk is twofold:
- Precision in Governance: Reducing the "Goal, Technical Implementation, Agency level enforcement" disconnect when designing policies by ensuring the "Why" (high-level goals) is technically feasible, proportional and addresses actual architectural vulnerabilities.
- Resilience Against Hype: Empowering leaders to distinguish between superficial safety claims or "lobby lies" and robust technical benchmarks, thereby accelerating the implementation of safeguards against existential threats.