The need for explicit user transparency is most critical for nondeterministic systems like LLMs, where even creators don't always know why an output was generated. Unlike a simple rules engine with predictable outcomes, AI's "black box" nature requires giving users more context to build trust.

Instead of trying to anticipate every potential harm, AI regulation should mandate open, internationally consistent audit trails, similar to financial transaction logs. This shifts the focus from pre-approval to post-hoc accountability, allowing regulators and the public to address harms as they emerge.

To provide a true early warning system, AI labs should be required to report their highest internal benchmark scores every quarter. Tying disclosures only to public product releases is insufficient, as a lab could develop dangerously powerful systems for internal use long before releasing a public-facing model, creating a significant and hidden risk.

When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.

The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.

Policymakers confront an 'evidence dilemma': act early on potential AI harms with incomplete data, risking ineffective policy, or wait for conclusive evidence, leaving society vulnerable. This tension highlights the difficulty of governing rapidly advancing technology where impacts lag behind capabilities.

Technical research is vital for governance because it provides concrete artifacts for policymakers. Demonstrations and evaluations showing dangerous AI behaviors make abstract risks tangible, giving policymakers a clear target for regulation, aligning with advice from figures like Jake Sullivan.

The most immediate danger from AI is not a hypothetical superintelligence but the growing delta between AI's capabilities and the public's understanding of how it works. This knowledge gap allows for subtle, widespread behavioral manipulation, a more insidious threat than a single rogue AGI.

As powerful AI capabilities become widely available, they pose significant risks. This creates a difficult choice: risk societal instability or implement a degree of surveillance to monitor for misuse. The challenge is to build these systems with embedded civil liberties protections, avoiding a purely authoritarian model.