Technology-Assisted Review (TAR), also known as predictive coding or machine learning-assisted review, is a powerful tool in the field of e-discovery that utilizes advanced algorithms to streamline and improve the efficiency and accuracy of the document review process. Here’s how TAR works and its benefits in e-discovery:
- Training and Learning: TAR involves training a machine learning model to analyze and categorize documents based on relevance to the case. Initially, a subset of documents is manually reviewed and coded by human reviewers to establish a baseline for the model’s understanding of relevance. The model then learns from this training set, identifying patterns and characteristics that determine relevance.
- Predictive Coding: Once the model is trained, it can predict the relevance of documents that have not been manually reviewed. The model applies the knowledge it has gained from the training set to assign relevance scores to the remaining documents. These scores help prioritize the review process, allowing reviewers to focus on the most likely relevant documents.
- Iterative Process: TAR is an iterative process that combines human expertise with machine learning. As reviewers code additional documents, their decisions are incorporated into the model, refining its understanding of relevance. The model continuously adapts and improves its predictions, making the review process increasingly accurate over time.
- Efficiency Gains: TAR significantly reduces the time and effort required for document review. By using predictive coding to prioritize documents, reviewers can concentrate on those with higher relevance scores, expediting the review process. This leads to significant time and cost savings, especially in cases involving large volumes of electronically stored information (ESI).
- Consistency and Quality Control: TAR promotes consistency in the review process. Once the model is trained and established, it applies consistent criteria for relevance across all documents. This helps mitigate inconsistencies that may arise from human reviewers’ subjective interpretations. TAR also allows for ongoing quality control by monitoring and validating the model’s performance through statistical sampling and accuracy measurements.
- Scalability and Cost Savings: TAR is highly scalable and can handle large-scale document reviews efficiently. Its ability to prioritize relevant documents and streamline the review process reduces the number of documents that require manual review, resulting in significant cost savings for e-discovery projects.
- Defensibility: TAR is defensible in legal proceedings, provided the process is properly validated and documented. Courts have recognized TAR as a reliable and efficient method for document review, as long as parties can demonstrate the defensibility and reliability of their TAR protocols, including the training and validation processes.
- Flexibility and Customization: TAR can be customized to suit the specific needs of a case or project. Different types of TAR methodologies, such as continuous active learning or simple passive learning, can be employed based on the characteristics of the data and the goals of the review. TAR can also be combined with other e-discovery techniques, such as concept clustering or keyword search, to further enhance efficiency and accuracy.
TAR has revolutionized the e-discovery process by leveraging machine learning to improve efficiency and accuracy in document review. It empowers legal teams to handle large volumes of data more effectively, reducing costs and time while maintaining high-quality results. As technology advances, TAR continues to evolve, offering even greater potential for enhancing e-discovery practices.
