In the digital age, vast amounts of information and data are generated and stored, creating a significant challenge for organizations when it comes to reviewing and analyzing documents for legal, regulatory, or investigative purposes. Traditional manual review methods often prove time-consuming, costly, and prone to human error. This is where Technology Assisted Review (TAR) comes into play, offering a more efficient and accurate approach to document review.

 

Technology Assisted Review, also known as Predictive Coding or Computer-Assisted Review, is a process that utilizes advanced machine learning algorithms and artificial intelligence (AI) to assist in the review and analysis of large volumes of electronic documents. TAR combines human expertise with the power of technology to streamline and enhance the document review process.

 

At its core, TAR employs sophisticated algorithms to categorize and prioritize documents based on their relevance to a particular case or investigation. Initially, human reviewers code a subset of documents, known as the "seed set," providing the system with examples of relevant and non-relevant documents. The TAR system then analyzes the characteristics of these documents, identifying patterns and learning to make predictions about the relevance of unseen documents. As the process continues, the system refines its predictions, and human reviewers validate and train the system by reviewing additional subsets of documents. This iterative feedback loop allows TAR to improve its accuracy over time.