Proposition Audit — AI Output Trust Verification

Proposition Audit — AI Output Trust Verification

Profile

• Jurisdiction: England and Wales (the maintained domain-routing profile; other jurisdictions illustrated under Step 2).
• Practice area: Clinical negligence and healthcare law (the maintained example domain; the methodology generalises to any field where AI-drafted factual content needs structured verification).
• Intended user: A practitioner verifying AI-generated factual or interpretive content before professional reliance — publication, court use, formal external use, or internal reliance.

Purpose

AI-generated research, statistics, citations, and factual claims require structured verification before professional use. This skill provides a systematic post-hoc audit — classifying each claim by type and salience, searching domain-appropriate sources, scoring trustworthiness transparently, and flagging what needs attention.

This complements rather than replaces rigour during generation. It is an independent verification layer applied to completed output.

The Verification Process

Step 1: Agree the Threshold

Before beginning verification, ask what minimum standard applies to this use case. Offer these defaults if the user has no preference:

• Publication or formal external use (skeleton arguments, blog posts, published advice, court documents, regulatory submissions, external correspondence on file): 90%+ (empirical), Accurate (legal), Fair (rhetorical). Remove or independently verify anything below threshold.
• Internal working documents (drafts circulated for review, internal research notes, working files): 70%+. Flag claims below threshold but they may be retained with caveats.
• Exploratory research (early-stage research to identify avenues for further investigation): 50%+. The purpose is to identify which claims merit further investigation, not to produce a final verified output.

Step 2: Extract and Classify Claims

Extract each distinct factual or interpretive claim from the output. Break composite sentences into their constituent sub-claims — each gets verified independently, because a sentence can be half-right and half-wrong. Where a single sentence contains claims of different types (a citation plus an empirical figure, for example), split it; each sub-claim is classified and scored independently.

Classify each claim by type:

• Empirical fact — a verifiable statement about the world (a statistic, a date, a named event). Verify by source corroboration.
• Citation or reference — a claimed source, case, study, or authority. Verify by locating the primary source and confirming it says what is attributed to it.
• Legal or regulatory proposition — a statement about the law, regulation, or procedural rules. Verify by locating the primary legislative or judicial source and assessing whether the proposition accurately reflects it. Source-counting alone is insufficient here because legal accuracy depends on interpretive precision, not volume of agreement.
• Interpretive or analytical claim — a conclusion drawn from evidence ("this suggests X", "the trend indicates Y"). These cannot be verified by corroboration. Instead, assess whether the underlying evidence exists, whether the reasoning is sound, whether credible counter-interpretations exist, and whether the opposing position is presented fairly (see rhetorical fairness in Step 4). Flag rather than score.

Then assign each claim a salience level:

• Load-bearing — the surrounding argument turns on this claim. The argument fails or substantially weakens if the claim is wrong.
• Supporting — strengthens the argument but is not strictly necessary to it.
• Illustrative — used by way of example, colour, or background. The argument is unaffected by removal.