The 'trust us' model thrives when regulators lack the technical literacy to challenge corporate claims. To break this pattern, we must move from voluntary ethics to mandatory, verifiable auditing. This requires building independent agencies staffed by specialists who can actually read code and inspect datasets, not just read marketing brochures.
Effective oversight relies on three pillars: transparency, access, and enforcement. First, companies should be legally required to disclose high-level architecture and training data provenance. Second, independent researchers need sandboxed access to models to test for biases or safety failures without violating proprietary secrets. We cannot let intellectual property laws become a black box for societal harms.
Finally, oversight must have teeth. If an algorithm causes documented harm, there must be clear legal pathways for restitution. Fines should not be mere line items in a massive budget; they must be high enough to change behavior. When accountability is tied to the bottom line and supported by public scrutiny, the era of blind trust must end. We need systems that assume corporate negligence until proven otherwise.
