Technology Assisted Review (TAR) refers to the use of advanced technologies, such as artificial intelligence (AI) and machine learning, to assist in the process of managed document review. Managed document review involves analyzing large volumes of documents for legal, regulatory, or investigative purposes. TAR enhances this process by automating and streamlining document review, making it more efficient and cost-effective.
Here’s a closer look at how TAR works in managed document review:
Training Phase: TAR begins with a training phase where human reviewers, typically attorneys, review a subset of documents and classify them as relevant or irrelevant. These documents serve as the training set for the TAR system.
Machine Learning: The TAR system then uses machine learning algorithms to analyze the characteristics and patterns in the training set. It learns from the decisions made by the human reviewers to identify relevant documents and develop a predictive model.
Predictive Coding: Once the model is trained, the TAR system applies predictive coding to the entire document collection. It automatically assigns relevance scores to each document based on the learned patterns. The higher the relevance score, the more likely the document is to be relevant.
Iterative Process: TAR typically operates in an iterative process, where the system presents batches of documents to human reviewers for review and feedback. The reviewers assess the accuracy of the system’s predictions and may adjust or refine the model accordingly.
Continuous Learning: As the reviewers provide feedback and make coding decisions on the reviewed documents, the TAR system continues to learn and adapt. It integrates the reviewer’s decisions into its model, improving its accuracy over time.
Benefits of Technology Assisted Review in Managed Document Review:
Increased Efficiency: TAR significantly reduces the time and effort required for document review by automating the process. It can analyze and prioritize documents based on relevance, allowing reviewers to focus on the most important ones.
Cost Savings: By speeding up the review process, TAR helps lower costs associated with document review. It reduces the number of billable hours spent by human reviewers, resulting in significant cost savings.
Consistency and Accuracy: TAR improves the consistency and accuracy of document review by applying machine learning algorithms consistently across the entire document collection. It minimizes human error and bias that can occur during manual review.
Scalability: TAR is highly scalable and can handle large volumes of documents efficiently. It is particularly useful when dealing with massive data sets where manual review alone would be time-consuming and impractical.
Quality Control: TAR provides a mechanism for ongoing quality control through the iterative feedback process. Human reviewers can continuously train and refine the system, ensuring higher accuracy and relevance in the document review process.
While TAR offers numerous benefits, it’s important to note that human oversight and expertise remain essential. Human reviewers play a crucial role in training the TAR system, providing feedback, and making final decisions on complex legal issues. TAR serves as a powerful tool to augment and streamline the managed document review process, but it does not replace human judgment and legal expertise.