Building a Defensible eDiscovery Review Protocol with AI and Human QC

Defensibility Is Process Evidence

A defensible eDiscovery review is not defined by software alone. It is defined by the ability to explain how documents were collected, filtered, reviewed, coded, escalated, sampled, and produced. Courts and opposing counsel care about whether the process was reasonable and repeatable.

Calibrate Reviewers Early

Reviewer calibration should happen before scale review begins. Sample documents, coding examples, privilege scenarios, and issue definitions help reviewers align on relevance and risk. Calibration reduces inconsistency and prevents rework later in the project.

Use AI to Prioritize, Not Abdicate

Technology Assisted Review and predictive coding can prioritize likely responsive documents, detect patterns, and improve review efficiency. But the review protocol should define how AI suggestions are tested, how false negatives are sampled, and how human reviewers validate critical categories.

Track Review Decisions

Coding decisions, privilege calls, redactions, and escalations should be recorded in a way that supports later explanation. Review logs and audit trails help clients understand the basis for production decisions and support defensibility if challenged.

Validate the Production Set

Before delivery, the production set should be checked for privilege leakage, redaction accuracy, metadata issues, duplicate handling, confidentiality designations, and format compliance. Final QC is where review discipline becomes client protection.