How Active Learning Will Change Your eDiscovery Review

In e-discovery, active learning utilizes machine learning technologies such as technology-assisted review (TAR), helping legal teams dramatically speed document review and thereby reduce its cost. Active learns puts the most relevant documents first, typically eliminating the need to review from 50 to 90 percent of a collection. It does this through continually learning, in real time, your team’s coding decisions and using those decisions to deliver the data that matters most.

  • Optimize Your Searches – Active learning focuses on coding decisions in real time to refine its understanding of what’s responsive. As reviewers code more documents, the engine gets smarter, analyzing the coding decisions and constantly refining its understanding of what’s most important to your case.
  • Spend less time on setup and administration – Start a large document project from setup to review in under 10 minutes. No need for training sets, no manually batching documents. Reviewers simply log in, click a button, and start reviewing the most relevant data. And because the review queue of documents is continuous, administrators don’t have to worry about any next steps, and they can easily monitor the results.
  • Connectivity – Combine email threading, clustering, sample-based learning, and visualizations with active learning to create unique workflows that match the needs of your project—whether it’s investigating the merits of a claim, sorting your data into key issues, or preparing evidence for litigation.

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