March 11, 2024
Author: Nasir Ali
Proxiio’s partnership with
Relativity provides access to many technology-assisted review tools that are incorporated
into the RelativityOne platform. One of the most widely used tools, Active
Learning, serves as a technology-aided solution designed to efficiently analyze
large datasets and predict the documents likely to be relevant for the review.
Using guidelines and requirements shared by the client counsel, Proxiio review managers identify the reviewers with highest level of experience and subject-matter expertise based on the facts of the case. They then design a workflow to derive the most value from Active Learning.
When case requirements demand relevant or key documents be identified quickly from large data sets to meet production timelines or prepare for trial, Proxiio utilizes the “Prioritized Review” queue to accelerate the learning model and ensure that the most relevant documents are identified first.
Active Learning enables us to significantly cut down on review expenses and streamline the time needed to finalize the review in accordance with counsel directives. We utilize our proficiency in crafting workflows that decrease the necessity for human document review by as much as 75%, all while instituting safeguards to maintain uncompromised quality.
When dealing with numerous custodians and data sources, creating a review workflow that efficiently handles the review and production of documents in several iterations can present complexities. Using the Active Learning “Coverage Review” queue allows us to design a workflow that ensures new documents are seamlessly added to review queues and processed for production.
We utilize multiple Active Learning models for a project to
ensure documents processed by our review team not only meet the relevance
criteria, but also are appropriately flagged for Privilege, Confidentiality,
and other parameters. We also ensure any documents scored highly by the model
for a particular parameter receive a QC check, minimizing the chances of human
error.
March 11, 2024
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