Resources
AI evidence and expert witness analysis
Educational resources for attorneys and legal teams handling AI-related litigation. Practical analysis of the technical and legal issues that matter.
Attorney tools
Organize the technical questions before expert selection
Two browser-local preparation tools for counsel. Neither tool requests case facts, uploads documents, or transmits selections.
Checklist
AI evidence readiness checklist
Assess evidence integrity, methodology, and litigation readiness before technical review or expert engagement.
Open checklistPlanning tool
AI expert scope planner
Generate a nonbinding preparation summary of technical disciplines, potential records, and questions for counsel.
Open plannerCurrent authority
Dated developments with practical litigation relevance
Three focused resources on developments that affect expert workflow, technical records, and AI intellectual-property evidence. Each distinguishes current authority from proposals and unresolved issues.
When an expert uses generative AI: verification, prompt records, and discovery risk
A dated review of AI-assisted expert workflows, the Kohls verification lesson, and developing prompt-record discovery questions.
Read current resourceAutomated-decision records are becoming litigation evidence
Why documentation, notices, explanations, and review records matter in disputes involving consequential automated decisions.
Read current resourceAI training data, output evidence, and copyright claims after 2025
A current review of technical evidence in training-data, output, market-effect, and AI copyright disputes.
Read current resourceArticles
Analysis and insights
AI evidence in court: what attorneys need to know in 2026
A practical overview of the evidentiary issues that arise when AI-generated or AI-processed evidence is introduced in civil and criminal proceedings, including authentication requirements, chain of custody considerations, and the deepfake defense.
Read articleDeepfakes and litigation: authentication, detection, and the deepfake defense
An analysis of the technical and legal issues that arise when synthetic media is at issue in litigation, including the current state of deepfake detection technology, the limits of available tools, and how courts are approaching authenticity challenges.
Read articleFederal Rule 702 and AI expert testimony: the 2023 amendment
An analysis of the 2023 amendment to Federal Rule of Evidence 702 and its implications for AI expert testimony, including the heightened gatekeeping standard and the requirement that the proponent demonstrate admissibility by a preponderance of the evidence.
Read articleAlgorithmic discrimination: technical analysis in employment and lending cases
A technical overview of how algorithmic bias analysis works in litigation, including disparate impact analysis, validation methodology critique, and the statistical methods used to identify discriminatory patterns in AI decision systems.
Read articleGenerative AI and copyright: the training data question
An analysis of the technical and legal issues in generative AI copyright litigation, including the relationship between training data and model outputs, memorization and extraction analysis, and the current state of the law in the Northern District of California.
Read articleAI-assisted digital forensics: reliability and chain of custody
An analysis of the reliability issues that arise when AI-powered forensic tools are used in evidence collection and analysis, including chain of custody requirements, tool validation standards, and how to challenge or defend AI-assisted forensic evidence.
Read articleFinding the right AI expert witness: a guide for attorneys
A practical guide for attorneys on how to identify, evaluate, and retain AI expert witnesses, including what credentials to look for, how to evaluate methodology, and the questions to ask before retaining an expert.
Read articleAI litigation in California: key cases, courts, and trends
A guide to the California-specific legal standards and regulatory framework applicable to AI litigation, including the Kelly-Frye standard, California Evidence Code requirements, and state AI governance laws.
Read articleAI system failure and product liability: technical analysis for attorneys
An analysis of how AI system failure analysis works in product liability and negligence litigation, including root cause analysis methodology, foreseeability assessment, and the technical standards applicable to AI system design.
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Start with the technical question
Begin a non-confidential matter triage to identify the technical issue, litigation posture, and expert profile that may fit the matter.