If You Can’t Explain It, You Can’t Defend It: A Paralegal AI Doc Review Protocol
A repeatable, defensible AI-assisted review workflow (with templates) — bounded scope, cite-backs, logs, and QA sampling.
On this page (jump)
If you can’t explain how an AI labeled a document, you can’t defend relying on it.
Want the templates? Download the kit.
TL;DR (quotable)
If you use AI in doc review, make it defensible: define scope (what AI can/can’t do), require structured outputs with cite-backs to the document text, keep batch and decision logs, and run QA sampling to catch systemic errors early. Use AI for triage, extraction, and draft notes—not as the final decision-maker on privilege or responsiveness unless your case team explicitly authorizes it. If you can’t explain the workflow in plain English, tighten the protocol before you scale it.
The minimum standard (what “defensible” looks like)
- Bounded scope: AI supports triage, extraction, chronology building, and draft notes—not final privilege calls unless authorized.
- Structured outputs: every output has fields + definitions + cite-backs (where possible).
- Audit trail: you can answer what ran, on what, when, with what settings, and what changed after QA.
- Quality control: sampling + escalation rules are written down before you start.
Protocol template (copy/paste)
Use the full template + logs here: Defensible AI Doc Review Protocol.
The one rule that prevents most pain
No cite-back = draft.
If an AI output can’t point back to the document text that supports it, treat it like a first draft—not a decision.
Call to action
Get the actual files (protocol DOCX + QA log + sampling plan): Download the kit.