No setting up training sets, no manually batching documents,
real-time intelligence based on reviewer coding, sounds too good to
be true right? We thought so too until we put Relativity’s
latest iteration of Technology Assisted Review (TAR) –
Continuous Active Learning (CAL) to the test.
What is CAL?
As the name suggests CAL is an application embedded in
Relativity designed to actively learn what documents are considered
relevant in large unstructured data sets, based on reviewers’
input. The key advantage of CAL is the real-time intelligence our
experts can derive from each coding decision made by a reviewer.
From this intelligence our experts can identify if reviewers are in
sync in terms of correctly identifying documents as Relevant or Not
Relevant or if certain reviewers are coding documents
inconsistently. Real time performance monitoring of the review
allows the experts to effectively address any issues with the
review which in turn causes a drastic
