Unleashing the Power of Relativity Active Learning Review

Relativity Active Learning Review is a cutting-edge approach to document review that leverages machine learning algorithms to streamline and optimize the review process. With its ability to significantly reduce costs, improve efficiency and increase accuracy, Relativity Active Learning Review has emerged as an eDiscovery game-changer.

Document review is a critical and time-consuming process. Traditional methods of manual review can be costly, long and subject to human biases. However, with advancements in technology, machine learning algorithms have paved the way for more efficient and accurate review processes. One such breakthrough is Relativity Active Learning Review, a powerful tool that automates and optimizes the document review process.

The Five Phases of a Relativity Active Learning Project

The first phase in administering a Relativity Active Learning project is preparing the data set. This stage is critical as the quality of the data set will directly impact the accuracy and effectiveness of the machine learning model.

Once the data set is ready, the second phase is setting up the review in the Relativity platform. This phase involves configuring the necessary settings and parameters to train and deploy the machine learning model effectively.

Once the machine learning model is deployed, the review team can start reviewing the documents and providing feedback to the model. This third phase involves an iterative process of training and refining the model based on the review team’s feedback.

The review project is checked in phase four with validation and elusion testing. As the review nears completion, it’s crucial to validate the results; elusion testing ensures the accuracy and defensibility of the review.

Preparing for next steps constitutes a phase five: Possible actions include document production, analysis and reporting, privilege review, further action based on review findings and project closure. Compliance with matter protocols and legal requirements should be reviewed.

We recently released a case study that shows how legal teams can save significant time and money using active learning for review. Download it now.

In next week’s blog post, we’ll share the active learning options that can be customized to suit the specific needs of a case.