This paper compares TAR 1.0, TAR 2.0, and Continuous Active Learning approaches for eDiscovery review. Page 1 highlights rising data volumes and the need for defensible AI-driven review. It explains workflow differences—batch training vs. continuous model learning—and how objectives such as speed, recall, cost reduction, and transparency influence protocol choice. Legal teams receive guidance on validation methods, sampling strategies, and reporting expectations. Case examples demonstrate when each TAR method is most effective. The paper emphasizes defensibility, measurable accuracy, and alignment with opposing-counsel requirements.

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