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Technology Assisted Review (TAR) is a crucial tool within eDiscovery. It addresses the escalating volumes of data generated and the disproportionate time and costs associated with electronic document reviews.

TAR is software built using mathematical algorithms and statistical sampling to code documents automatically. The software is trained, using a seed set of documents, coded by an expert, to determine what is a ‘relevant’ document and what is not.

Through leveraging advanced machine learning algorithms, TAR significantly enhances efficiency and accuracy. It involves a cooperative effort between human reviewers and the technology. Where the initial sample of documents reviewed by humans informs the algorithm’s understanding of relevance.

As the algorithm learns from reviewer decisions, it becomes increasingly adept at predicting which documents are relevant, reducing the time and effort traditionally required for manual review. TAR not only expedites eDiscovery processes but also improves consistency in decision-making, making it a pivotal advancement in the legal tech landscape.

Explore the importance of TAR here.