Lilith Bat-Leah, CEDS, Ronald J. Hedges, Author at ACEDS

Ronald J. Hedges

Ronald J. Hedges is the Principal of Ronald J. Hedges LLC. He served as a United States Magistrate Judge in the District of New Jersey for over 20 years. He speaks and writes on a variety of topics, many of which are related to electronic information, including procedural and substantive criminal law, information governance, litigation management, and the integration of new technologies such as artificial intelligence into existing information governance policies and procedures. He was a member of the artificial intelligence (“AI”) task forces of the New Jersey and New York state bar associations and is now a member of the permanent AI committees of both Bars. Ron is also a member of the Founders Circle of the Georgetown Law Advanced eDiscovery Institute. Ron is the lead author of a guide for federal judges on electronically stored information, https://www.fjc.gov/content/323370/managing-discovery-electronic-information-third-edition-2017. Ron is also the co-senior editor of The Sedona Conference Cooperation Proclamation, Resources for the Judiciary, Third Edition (June 2020) and the 2022 supplement thereto, https://thesedonaconference.org/sites/default/files/Judicial%20Resources%20publication%20announcement.pdf. He is also the editor of various compendiums on electronic information in criminal investigations and proceedings hosted by the Massachusetts Attorney General’s Office, https://www.mass.gov/service-details/understanding-electronic-information-in-criminal-investigations-and-actions. He can be reached at [email protected].

Defensibility Considerations Around AI in Best-Case and Worst-Case Scenarios

ACEDS Blog, Artificial Intelligence / Ronald J. Hedges / July 28, 2025

Introduction The integration of AI, including agentic AI, into eDiscovery workflows represents a significant evolution in how legal teams manage the review and production of electronically stored information (ESI). As […]

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Ronald J. Hedges

Defensibility Considerations Around AI in Best-Case and Worst-Case Scenarios

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