In partnership with the University of Florida’s E-Discovery Conference, the Journal of Technology Law & Policy has published a special E-Discovery issue!

The special E-Discovery issue (available here) includes six terrific articles from judges, lawyers, law professors and other thought leaders in the industry on timely and important eDiscovery trends. The articles are:

The Coming Use and Misuse of Artificial Intelligence in the Courtroom: A Judicial Perspective and Proposal: Florida Chief U.S. Magistrate Judge William Matthewman examines how AI is rapidly transforming litigation and courtroom practice, creating both significant opportunities and serious risks. He contends that while AI can legitimately assist courts through evidence enhancement, demonstrative exhibits, data analysis, and improved access to justice, it also enables deepfakes, fabricated evidence, and other forms of deceptive content that threaten the integrity of the judicial process.

Judge Matthewman also contends that existing Federal Rules of Evidence and procedural rules are inadequate to address these emerging challenges and proposes reforms requiring early disclosure of AI-generated or AI-enhanced evidence, expanded discovery into AI provenance and methodology, clearer authentication standards, and revised evidentiary rules governing admissibility. He also emphasizes that lawyers must become technologically competent in detecting and challenging AI-generated misinformation, while judges must serve as vigilant gatekeepers to ensure that only reliable, properly authenticated AI evidence reaches juries.

Who Has “Possession, Custody, or Control” of the Employee’s Personal Mobile Device? Time for Amendments to the Federal Rules: Texas District Judge Xavier Rodriguez examines one of the most challenging issues in modern eDiscovery: whether a company has “possession, custody, or control” over data stored on employees’ personal mobile devices. He argues that the widespread use of Bring Your Own Device (BYOD) policies has blurred the line between personal and business communications, creating uncertainty regarding preservation, collection, and production obligations under the Federal Rules of Civil Procedure. The article analyzes the competing judicial standards used to determine control – including the “legal right” test and the “practical ability” test – and concludes that both are flawed because they either allow parties to evade discovery or create inconsistent, overly broad obligations.

Judge Rodriguez also explores the risks of spoliation, employee privacy concerns, and the growing tension between discovery obligations and privacy laws such as GDPR. To address these challenges, he proposes an agency-based framework under which ESI on the personal devices of officers, directors, and employees with significant managerial authority would be presumed to be within a company’s control, while data on the devices of most other employees would generally be obtainable only through Rule 45 subpoenas. The article ultimately calls for amendments to the Federal Rules to provide a clearer, more uniform national standard for handling personal mobile device data in discovery.

Force Multiplier? Artificial Intelligence, Uneven Competence, and the Integrity of the Adversarial System: Here, the authors (Judge Ralph Artigliere (ret.), David Horrigan of Relativity, & Rose Hunter Jones of Hilgers PLLC) argue that the greatest risk posed by generative AI to the legal profession is not isolated errors such as hallucinated citations, but the widening “competence divide” between legal professionals who understand and responsibly supervise AI tools and those who do not. Drawing parallels to earlier technological shifts such as eDiscovery and Technology-Assisted Review (TAR), the authors contend that generative AI differs because it directly participates in synthesis, analysis, and reasoning rather than simply organizing information. As a result, AI acts as a force multiplier that can either enhance professional judgment or magnify errors, creating disparities in litigation strategy, evidentiary analysis, access to justice, and courtroom advocacy.

While ethical rules and sanctions can address individual misuse, the authors argue that they cannot solve the deeper structural problem of uneven AI competence. The authors conclude that AI’s impact on fairness, accuracy, and public confidence in the justice system will depend on whether courts, law schools, law firms, and professional organizations adopt coordinated reforms focused on education, oversight, incentives, and standards to ensure that AI narrows rather than deepens existing inequalities within the legal system.

The Recovery of Meaning in a Digital Age: E-Discovery and the Renewal of Judgment: Professor William Hamilton of the University of Florida Levin College of Law argues that e-discovery should be understood not merely as a technical process for collecting and reviewing ESI, but as a foundational litigation practice that reconstructs the factual reality upon which legal judgment depends. The article traces the evolution of discovery from paper-based processes to today’s complex digital environment, where emails, metadata, collaborative communications, and other digital artifacts provide a rich but fragmented record of human activity.

Professor Hamilton contends that modern e-discovery, particularly when enhanced by generative AI, can help lawyers recover context, relationships, timelines, and competing perspectives from vast collections of data, enabling a more complete understanding of events. However, he emphasizes that while AI can illuminate patterns and organize information, it cannot replace human judgment, responsibility, or interpretation. Drawing on the philosophical work of Hannah Arendt, Professor Hamilton argues that the central challenge of the digital age is not simply managing information volume, but restoring meaning, context, and perspective so that courts and advocates can exercise sound judgment grounded in a shared factual world.

Artificial Intelligence in Document Review: Statistically Defending the Process and Results of E-Discovery Workflows: Here, the authors (Aron Ahmadia, Nathan Reff & Cristin Traylor of Relativity, Matthew Reiber, Associate Professor of Law at Jacksonville University College of Law and Chad Roberts of eDiscovery CoCounsel, pllc) examine how AI is transforming document review in e-discovery and provides a practical framework for validating AI-assisted review processes. The authors trace the evolution from manual review to predictive coding, technology-assisted review (TAR), and generative AI-driven workflows, emphasizing that while AI can dramatically reduce the cost, time, and burden of reviewing large volumes of electronically stored information, its use requires defensible validation to ensure completeness and accuracy.

The article explains key concepts such as recall, precision, richness, elusion, statistical sampling, confidence intervals, quality assurance, quality control, and workflow validation, arguing that courts and litigants need objective methods to evaluate whether AI-assisted review meets the requirements of proportionality and reasonableness under the Federal Rules of Civil Procedure. Ultimately, the authors conclude that no review process is perfect, but that carefully designed workflows, rigorous statistical validation, thorough documentation, and transparent reporting can provide courts and practitioners with a defensible basis for relying on AI-driven review technologies, including emerging GenAI tools.

Prompting Change with GenAI in Large-Scale Document Review: A Real-World Study: Here, the authors (Robert Keeling, Ray Mangum, Amy Hanke, and Alyssa Ogden of Redgrave LLP) examines the effectiveness of generative AI in eDiscovery through a real-world study comparing human document reviewers with Relativity’s aiR for Review on a sample of 1,600 documents from an actual regulatory matter. The authors found that genAI performed strongly in first-level responsiveness review of text-based custodial documents, achieving an 83.9% recall rate and an 84.7% precision rate – results that compare favorably to accepted Technology-Assisted Review (TAR) standards.

The study also found that GenAI can significantly improve efficiency and potentially reduce review costs, although its performance was less consistent for issue coding and weaker when evaluating documents that relied heavily on metadata, spreadsheets, templates, images, family relationships, or hyperlinked content. The authors conclude that genAI is a promising and defensible addition to document review workflows when combined with appropriate human oversight, validation, and quality control, while emphasizing that further empirical research is needed to establish best practices and address current limitations.

The six terrific articles in the special E-Discovery issue take an in-depth look at important issues impacting eDiscovery professionals. Over the next couple of weeks, I plan to cover several of them in greater depth, but hopefully, this brief summary of the articles gives you a head start on what the articles cover and what that might mean to you.

So, what do you think of the special E-Discovery issue from the University of Florida Levin College of Law? Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by my employer, my partners or my clients. eDiscovery Today is made available solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscovery Today should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.