Humans vs. the Machines: Reassessing the Superiority of Machines in Doc Review

Research comparing the quality of human review to predictive coding (also called technology-assisted review) has clouded the market’s understanding of the importance lawyers play in document review. This view that humans are inferior to machines, largely fueled by misunderstandings of past research, has increased the risk of inadvertent disclosures of sensitive and confidential information when responding to document requests. New research published by this webinar’s speakers in the Richmond Journal of Law & Technology challenges this conventional wisdom that machines are “better” than humans and provides legal practitioners with a citable source that may help de-risk their document reviews when using predictive coding. This EDRM session will: • Explain how the results of prior research on predictive coding has been misunderstood. • Quantify the risks associated with relying exclusively on predictive coding, without final human judgment calls. • Examine the results of experiments that use real world data to assess the quality of review when combining predictive coding with human judgment calls. Speaker Bios: Rishi P. Chhatwal is Assistant Vice President, Senior Legal Counsel at AT&T Services, Inc. Robert Keeling is a partner at Sidley Austin LLP whose practice includes a special focus on electronic discovery matters and he is co-chair of Sidley’s eDiscovery and Data Analytics Group. Peter Gronvall is a Senior Managing Director at Ankura where he leads our Global Data & Technology practice group, working with companies and their counsel on a range of service areas including data analytics, machine learning, discovery, forensics, cyber security and information risk advising. Nathaniel (Nate) Huber-Fliflet is a Senior Managing Director at Ankura, He consults with law firms and corporations on advanced data analytics solutions and legal technology services.