What are the best practices for using AI in eDiscovery?

AI in eDiscovery fails in predictable ways, and the practices that prevent it are mostly procedural rather than technical.

Start with a written criterion. Most disappointing results come from a vague instruction, not a weak model. State the question the way you would state it to a contract reviewer: what makes a document responsive, what does not, and what the edge cases look like.

Validate before you rely. Run the criterion on a sample, have a human review the same sample independently, and compare. Disagreements tell you whether the problem is the instruction or the population.

Measure both directions. Track what the AI missed and what it over-called. A tool tuned to catch everything will bury reviewers in false positives; one tuned for precision will quietly drop documents.

Insist on reasons, not just labels. A coding decision you cannot explain is a coding decision you cannot defend. The reasoning behind each call should be recoverable months later.

Decide disclosure early. Whether and when to tell the other side how the review was run is a strategy question best settled at the discovery-plan stage, not after a challenge.

Keep a lawyer accountable. Competence guidance is consistent on this point: the tool assists, the lawyer remains responsible for the work product.

Protect the sensitive material. Confirm where data is processed, who can access it, and whether anything is retained for training.

Related reading: How AI fits into modern eDiscovery: a practical guide.

If you want to see what this looks like in a working review, book a Claira demo.