This guide, 'Technology-Assisted Review for UK Lawyers', covers Predictive Coding, Continuous Active Learning, and defending the machine's judgement.
Guide · 17 pages · 22 min read · Published 2026-08-30
This guide, 'Technology-Assisted Review for UK Lawyers', covers Predictive Coding, Continuous Active Learning, and defending the machine's judgement. It details how TAR works, including models, training, and generations, alongside the human layer of seeds and subject-matter reviewers. Validation, recall, precision, and the stopping decision are explained. The guide addresses TAR in the protocol, disclosure, negotiation, and the courts, as well as its place in the tool chain with keywords, threading, and privilege. It includes common mistakes, technical limitations, and questions to ask. This resource is for UK litigators, in-house counsel, and investigators seeking to understand and apply Technology-Assisted Review in e-discovery.
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17 pages · 22 min read
This guide, 'Technology-Assisted Review for UK Lawyers', covers Predictive Coding, Continuous Active Learning, and defending the machine's judgement.
Published by Computer Forensics Lab on 2026-08-30. Original material of the practice, free to read, cite and download. The authority behind this subject is Practice Direction 57AD, Appendix 2, use of technology in disclosure, which you should read alongside this guide. See every guide's author and source.
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TECHNOLOGY - ASSISTED REVIEW · A GUIDE FOR UK LAWYERS Technology-Assisted Review for UK Lawyers Predictive Coding, Continuous Active Learning and Defending the Machine's Judgement COMPUTER FORENSICS LAB
§ ABOUT THE AUTHOR PREPARED BY COMPUTER FORENSICS LAB E-DISCOVERY TEAM VALIDATION STATISTICS CPR PART 35 EXPERT REPORT S FULL CHAIN-OF-CUSTODY DOCUMENTATION
§ CONTENTS In this guide 01 Executive summary 02 The problem in plain English: teaching a machine what relevant looks like 03 How TAR works: models, training and the two generations 04 The human layer: seeds, subject-matter reviewers and consistency 05 Validation: recall, precision and the stopping decision 06 TAR in the protocol: disclosure, negotiation and the courts 07 TAR in the tool chain: keywords, threading and privilege 08 Source architecture: where else the evidence lives 09 Worked examples 10 Common mistakes and technical limitations 11 Questions to ask · Suggested wording 12 Checklist and red flags · When to involve a digital forensic expert 13 Frequently asked questions 14 Glossary · References · Disclaimer · How a specialist laboratory can assist
§ 01 · ORIENTATION Executive summary THE HEADLINE POINT: TARISHUMANJUDGEMENTAMPLIFIEDBYRANKING: ITS DEFENSIBILITYISTHETRAININGRECORDPLUSTHEVALIDATIONSTATISTICS, ANDBOTH AREBUILT, NOTASSUMED
§ 02 · FIRST PRINCIPLES The problem in plain English: teaching a machine what relevant looks like
§ 03 · THEMACHINERY How TAR works: models, training and the two generations
§ 04 · THETEACHERS The human layer: seeds, subject-matter reviewers and consistency
§ 05 · THELICENCETOSTOP Validation: recall, precision and the stopping decision
§ 06 · THE PAPER WORK AND THE COURT S TAR in the protocol: disclosure, negotiation and the courts
§ 07 · THETOOLCHAINSEAT TAR in the tool chain: keywords, threading and privilege
§ 08 · THEWIDERMAP Source architecture: where else the evidence lives QUESTION QUALIT Y TRAINING VALIDATION PROTOCOL & PLATFORM N ON-TEXT RECORD FILE CORRESPONDENCE LOGS ROUTES TEXT- LAYER
§ 09 · IN THE WILD Worked examples EXAMPLE1 · THE1. 8MILLIONDOCUMENTSREVIEWEDBY140, EXAMPLE2 · THESTOPPINGDECISIONTHATWASABUDGETINCOSTUME EXAMPLE3 · THEELUSIONFINDTHATWASASCOPEHOLE
§ 10 · WHEREITGOESWRONG Common mistakes and technical limitations Common mistakes Technical limitations
§ 11 · INTERROGATORIES & DRAFTING AIDS Questions to ask · Suggested wording Ask your client Ask your opponent Ask your e Discovery / forensic provider SUGGESTED WORDING · TA R PA R AG RAPHFORTHEDRD / PROTOCOL
§ 12 · QUICK CONTROL Checklist and red flags · When to involve a digital forensic expert The TAR checklist Red flags When to involve a digital forensic expert
§ 13 · COMMON QUESTIONS Frequently asked questions Is predictive coding actually approved by the English courts? How big does a matter need to be before TAR makes sense? Can the model miss the smoking gun? Do we have to tell the other side we are using TAR? What recall target should we agree? Is TAR the same as using generative AI for review?
§ 14 · REFERENCE Glossary CAL Sources and authoritative references DISCLAIMER
§ HOW A SPECIALIST LABORATORY CAN ASSIST Working with Computer Forensics Lab Speak to a forensic examiner, not a salesperson. INSTRUCTTHELAB NEWENQUIRIESEMAILE - DISCOVERY
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