Technology Assisted Review is a crucial tool within eDiscovery due to the increasing amounts of data generated and the disproportionate amount of time and costs to carry out an electronic document review.
What exactly does Technology Assisted Review entail?
Technology Assisted Review (or TAR for short) uses mathematical algorithms and statistical sampling to automatically code documents. The software undergoes training using a seed set of documents, all coded by an expert. This aids in determining what constitutes a ‘relevant’ document and what does not.
How can CYFOR Legal help?
At CYFOR Legal, we use Relativity Assisted Review (RAR) for predictive coding as one of our tools. RAR uses a functionality called ‘Categorisation’ to arrange the documents into groups of ‘Relevant’ and ‘Not Relevant’ documents. Categorisation uses Relativity’s analytics engine to look at textual concepts within a document set. This is based on a type of textual analytics called Latent Semantic Indexing (LSI). The analytics engine will examine concepts within a document and identify other documents with similar textual content. We seize this opportunity to teach the system about the types of documents we are interested in. This allows the analytics engine to categorise them accordingly.
The best fit for you.
CYFOR Legal collaborates with clients in the early stages of a litigation case. This allows us to assess whether TAR is the most suitable approach for the project at hand. Additionally, once a client instructs our consultants to carry out an assisted review project, our eDiscovery team will then guide the reviewers through the necessary steps to achieve the desired outcome.
The Complete Process
Quality Control Round
Therefore, upon completing training rounds, we execute a quality control round on the categorised documents to test the accuracy of the system’s grouping. We batch out a sample of the documents already categorised as a result of the training rounds for manual review. Then, the system can compare how many documents it categorised correctly and how many it ‘overturned.’ An ‘overturn’ occurs when the system categorises a document, for example, as ‘Relevant,’ and a manual review codes the document as ‘Not Relevant.’ Additionally, we can measure and analyse the number of ‘overturns’ to identify and correct issues within the TAR project.
Training Rounds & Quality Control
In summary, the aim of the training rounds and QC rounds is to categorise as many documents as possible, as accurately as possible, in line with the F1 score (a measure of a test’s accuracy), agreed at the start of the project. The end result produces a set of coded documents that facilitate timely production. These documents can further prioritise review by pushing the ‘Relevant’ documents to the review team first or batching out all the ‘Relevant’ documents for review. Therefore, whatever the reason for your assisted review project, predictive coding will play an increasingly crucial role in the future as data sizes grow and manual document review costs soar. TAR has quickly become an essential tool in litigations, prioritising documents for review while reducing time and overall costs.
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