Relativity's assisted review tool, Active Learning, helps you categorize your documents and automate the review process while minimizing the time your review team would otherwise spend coding irrelevant documents in your document set.
Sample Active Learning scenario:
You’re a litigation support specialist at a Relativity service provider, and the legal department of a large financial services company reaches out to you because the federal government is demanding that documents belonging to three key custodians be turned over quickly as part of an ongoing investigation.
This company is in a serious time crunch because the government agency’s attorneys then unexpectedly request documents from a fourth custodian, whom they believe is crucial to the case. This doubles the size of the data they’re required to review and produce, so they turn to you and you turn to Active Learning.
You create a project that uses an Analytics index that includes the data of all four custodians. The project uses documents that were previously coded to expedite the training of the system. Relativity categorizes the document universe for prevalence, and Reviewers begin reviewing more documents to assist the system in deciding relevance.
In an Active Learning project, reviewers are continuously provided documents of a certain rank. At the end of this project, you learn that less than 15% of the total documents in the document universe needed review to produce accurate results in a limited time frame. The financial services company you’re assisting can now easily comply with the federal government and give them what they need.
Note: Sample-based learning has been removed from Assisted Review and replaced with Active Learning. To view reports for old sample-based learning projects in your workspace, see Report sample-based statistics.
