Why More Case Teams Should Use Active Learning for All Their Review Projects

Over the past few blog posts, we’ve been covering the importance of Relativity Active Learning Review and what users need to know to help decide whether to use it in their next eDiscovery project. The first blog post covered what users can expect throughout the five phases of a project. Then we covered how active learning offers various options that can be customized to suit the specific needs of a case team. Today we share the benefits of using Relativity Active Learning Review.

  1. Cost Reduction: Relativity Active Learning Review can significantly reduce the costs associated with document review. By leveraging machine learning algorithms, the review process becomes more efficient, and reviewers can focus on the most relevant documents, eliminating the need to review irrelevant documents. This reduces the overall review time and expenses, making it a cost-effective solution for document review projects.
  2. Increased Efficiency: Active Learning Review automates the document review process so that only relevant documents need to be seen by the human reviewers. This eliminates manual and repetitive tasks, such as reviewing nonrelevant documents, which can be time-consuming and are prone to human error. The optimized review process allows reviewers to focus on the most important documents, leading to increased efficiency and productivity.
  3. Improved Accuracy: Relativity Active Learning Review leverages machine learning algorithms to continuously improve from reviewer feedback and refine the active learning index. This results in improved accuracy in identifying relevant documents, reducing the risk of missing relevant documents or producing false positives. The iterative process of active learning ensures that the review becomes more accurate over time, leading to more reliable results.
  4. Flexibility and Customization: Relativity Active Learning Review offers customization options, allowing case teams to tailor the review process to their specific needs. Customized review models, real-time monitoring and refinement options enable case teams to adapt the review process based on the unique requirements of each case, ensuring the flexibility to optimize review for the specific goals and objectives of the project.

We recently released a case study that covers a case where Gulfstream ran linear review and active learning at the same time. The case study demonstrates in specific terms the benefits mentioned above. Download it now.

Relativity Active Learning Review is a powerful tool that can significantly enhance the document review process, reducing costs, improving efficiency, and increasing accuracy. With its customizable options, real-time monitoring and refinement capabilities, Relativity Active Learning Review offers a streamlined and optimized approach to document review. Case teams should consider incorporating active learning into all their review projects to unlock the benefits of this innovative technology and achieve more efficient and accurate review results. Embracing Relativity Active Learning Review can revolutionize the way document review is conducted, leading to more effective and successful eDiscovery outcomes.

Download our new white paper on Relativity Active Learning Review now.