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The Delayed Countdown Method for Validating Review-Oriented AI Workflows in eDiscovery
David D. Lewis, Tara Emory, Lenora Gray, Mark Noel, Jeremy Pickens, Eugene Yang

The Delayed Countdown Method for Validating Review-Oriented AI Workflows in eDiscovery

Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

Review-oriented workflows have become the dominant approach to AI-assisted document review in electronic discovery (eDiscovery). How to validate the effectiveness of review-oriented workflows has, however, remained a confusing and controversial topic in technology-assisted review (TAR) in the law. We examine the sources of this controversy, and propose a simple and practical evaluation approach for review-oriented workflows. Our Delayed Countdown method is easily incorporated into existing review-oriented workflows and software, is compatible with arbitrary stopping rules, and produces valid frequentist confidence intervals on effectiveness. We explain the conceptual foundations of the method, provide example computations, discuss the handling of logically impossible recall values, and suggest future work on decreasing review costs. (This paper was submitted October 1, 2026 to The Tenth Annual Workshop on Applications of Artificial Intelligence in the Legal Industry.)

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title The Delayed Countdown Method for Validating Review-Oriented AI Workflows in eDiscovery
Author / Organization David D. Lewis, Tara Emory, Lenora Gray, Mark Noel, Jeremy Pickens, Eugene Yang
Subject & Discipline cs.IR
Categories Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-264193
Original Source Link Visit Source Page on arxiv.org »