Map the steps that use automation
Ask the provider to distinguish collection, language processing, narrative grouping, identity matching, summarization and recommendation. Different steps can have different limitations and review requirements.
Compare narrative intelligence tools and analyst services by asking where automation ends, who reviews the interpretation and how a disputed finding is corrected.
Have the vendor identify what the reviewer can inspect at each step. A system that links an output to the source record offers a different review experience from one that supplies only a fluent summary.
Test contextual ambiguity
| Pilot example | Question to inspect |
|---|---|
| Quotation or rebuttal | Does the output preserve who said what? |
| Several meanings of a name | Are unrelated people or entities separated? |
| Translated material | Can a reviewer inspect the original meaning? |
| Incomplete source record | Does the output acknowledge what is missing? |
| Competing explanations | Are alternatives visible in the assessment? |
Review a defined reference set
Choose a bounded set of examples and agree the questions the output should answer. Have qualified reviewers assess the evidence and label disagreements. Include difficult cases instead of testing only obvious patterns.
Record missed observations, mistaken matches, inappropriate grouping and unsupported conclusions separately. A single accuracy percentage can hide that these errors have very different consequences for the brief.
Keep corrections visible
A reviewer should be able to challenge an account match, split a narrative cluster or amend a summary while retaining an explanation. Ask whether the correction affects later reports and how prior recipients are informed when necessary.
Preserve the distinction between source text, automated processing and the analyst’s conclusion. A later reviewer should not have to guess which part came from the original record.
Assign accountable analytical judgment
Define who signs off material findings and who authorizes action. The review depth should reflect the consequence of a mistake, the quality of the evidence and the uncertainty remaining.
NIST’s voluntary AI Risk Management Framework offers a broader reference for evaluating trustworthiness and managing AI-related risks. It is a governance resource, not a certification of the services in this guide. NIST AI RMF ↗
Questions for the brief
Can a generative AI summary be used as the evidence for a narrative attribution?
Treat it as an interpretation to verify. Inspect the underlying observations and reasoning, including alternative explanations. A summary’s fluency does not establish the identity or direction of an actor.
What should I ask a provider that advertises very high model accuracy?
Ask what task, dataset, languages and error definition the figure covers. Then test the relevant workflow on an agreed reference set rather than applying a general percentage to a different use case.
How should the team handle a finding that human reviewers disagree about?
Record the disagreement, inspect the source and definitions, and preserve the unresolved uncertainty where appropriate. Escalate material questions to the responsible specialist instead of forcing a confident label.