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GENERATIVEAIINDISCLOSURE · A GUIDE FOR UK LAWYERS Using Generative AI Responsibly in Electronic Disclosure Prompted Review, Human Verification and Defensibility Under PD 57AD COMPUTER FORENSICS LAB
§ ABOUT THE AUTHOR PREPARED BY COMPUTER FORENSICS LAB E-DISCOVERY TEAM ESTABLISHED 2007 · LONDON ISO 17025-ALIGNED PROCEDURES GENAI-ASSISTED REVIEW WORKFLOW S
§ CONTENTS In this guide 01 Executive summary 02 The problem in plain English: a reader that never tires and some time s invents 03 The guidance landscape: ILTA, the Law Society, the judiciary and Ayinde 04 Where GenAI fits: relevance, privilege, summarisation and redaction support 05 Prompt engineering as methodology: drafting, testing and version in g 06 Verification: sampling AI outputs against human judgement 07 Transparency, data protection and the protocol 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: GENAIREADSANDCHARACTERISESATSCALEUNDER INSTRUCTIONSYOUWRITE: THEINSTRUCTIONSARETESTEDLIKESEARCHTERMS, THE OUTPUTSVERIFIEDLIKEAJUNIOR ' SWORK, ANDTHELAWYERREMAINSACCOUNTABLE FOR ALL OF IT
§ 02 · FIRST PRINCIPLES The problem in plain English: a reader that never tires and some time s invents
§ 03 · THERULESOFTHEROAD The guidance landscape: ILTA, the Law Society, the judiciary and Ayinde
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§ 04 · THE USE CASES Where GenAI fits: relevance, privilege, summarisation and redaction support
§ 05 · INSTRUCTION S AS METHOD Prompt engineering as methodology: drafting, testing and version in g
§ 06 · CHECKINGTHEREADER Verification: sampling AI outputs against human judgement
§ 07 · THE PAPER WORK AND THE DATA Transparency, data protection and the protocol
§ 08 · THEWIDERMAP Source architecture: where else the evidence lives QUESTION DECISION QUALIT Y PROMPT VERIFICATION PROCESSING SCOPE FILE RECORDS INVENTORY LAYERS HUMAN TEXT- LOG LAYER
§ 09 · IN THE WILD Worked examples EXAMPLE1 · THEPROMPTEDFIRSTPASSTHATSURVIVEDITSAUDIT EXAMPLE2 · THESUMMARYTHATINVENTEDACONCESSION EXAMPLE3 · THEPRIVILEGEFLAGSTHATACCELERATED, ANDNEVERDECIDED
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§ 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 · GENAI PA R AG RAPHFORTHEDRD / PROTOCOL
§ 12 · QUICK CONTROL Checklist and red flags · When to involve a digital forensic expert The GenAI checklist Red flags When to involve a digital forensic expert
§ 13 · COMMON QUESTIONS Frequently asked questions Is it actually permissible to use generative AI in court-ordered disclosure? How is this different from the TAR we already use? What is "hallucination" and how do we protect against it in disclosure work? Do we have to tell the other side we used AI? Can AI make privilege calls? Our client's data is highly sensitive. Does that rule GenAI out?
§ 14 · REFERENCE Glossary 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
Cite as: Joseph Naghdi, Using Generative AI Responsibly In Electronic Disclosure, Computer Forensics Lab, https://e-discovery.uk/library/using-generative-ai-responsibly-in-electronic-disclosure/pdf.