At one time or another, most lawyers involved in eDiscovery have felt the unique pressure induced by a slow-moving document review. That pressure to speed a review is one of the reasons that eDiscovery is effectively ground zero for todays exploding use of AI in law. <\/p>\n
Many lawyers first encounter with machine-learning technology was through technology-assisted review (TAR) or predictive coding. Those technologies marked the first significant use of AI in any field of law, and that use is expanding. <\/p>\n
So How Does TAR Lighten the Load? <\/p>\n
Consider this scenario: Youre halfway through a document review and running out of time. Would it help to switch over to TAR? <\/p>\n
Answer: Depending on the size of the collection, yes. In fact, because youve already coded half the documents, you can use those coding decisions to jumpstart the TAR process. <\/p>\n
A law firm recently faced virtually the identical scenario. The firm had to review a relatively small collection of about 40,800 documents in a short time. As it waited for approval from its client to use TAR, the firm dove into the review, knowing time was tight. By the time the client gave the green light to use TAR, the firm had coded 18,200 of the 40,800 documents. <\/p>\n
The documents theyd coded provided a ready-made set of seeds to use to train the TAR algorithm. This is possible with a TAR engine that uses continuous active learning, a machine learning protocol that enables it to use any and all previously coded documents as judgmental seeds to start the process. <\/p>\n
The coded documents were fed into the system and then, based on that input, the entire population was analyzed and ranked. From that point, the TAR engine started feeding the reviewers batches of 50 documents each. Each batch contained the next-best documents that were most likely to be responsive. In each batch, the tool also included a few contextually diverse documents to make sure there are no topics or concepts left unexplored. <\/p>\n
As the reviewers completed their batches, the system continuously used their judgments to re-rank the entire population, improving its rankings with each new batch. The review proceeded along this track until the reviewers started seeing batches with few, if any, relevant documents, which told them that few relevant documents remained. Testing showed that the review had achieved high recall, meaning the reviewers had found the vast majority of the relevant documents. <\/p>\n
In the end, the law firm had reviewed another 6,800 documents beyond the 18,000 theyd reviewed before starting TAR. That meant there were another 15,800 documents they did not have to review. So, from the time they started using TAR, the process cut out 70% of the work that manual review would have required. The firm calculated that this saved its clients more than $70,000. <\/p>\n
This scenario of how TAR works focuses on just one of 20 questions answered in a new book, Ask Catalyst: A Users Guide to TAR, which is available for free download at this link. <\/p>\n
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How AI And Machine-Learning Tools Lighten The eDiscovery Load - Above the Law<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" At one time or another, most lawyers involved in eDiscovery have felt the unique pressure induced by a slow-moving document review. Continue reading