A Hybrid Approach for Privilege Document Review: Rule-Based and Machine Learning

In the realm of U.S. legal practice, safeguarding privileged communications and data from unintended disclosure is of utmost importance. Traditionally, attorneys have relied on keyword searches and manual reviews to find privileged documents in legal cases. However, as data volumes grow, this method becomes increasingly costly and inefficient. Machine learning techniques have emerged as a viable solution for identifying privileged documents. This paper introduces a hybrid approach to identifying privileged documents by integrating rule-based modeling logistic regression, and active learning within a predictive analytics framework. This method streamlines privilege prediction in legal datasets by leveraging metadata, such as attorney and firm details, along with document text, to create a comprehensive end-to-end process for privilege identification. Evaluation through precision and recall metrics on the dataset from a real-world legal matter demonstrates the effectiveness of this approach in classifying privileged status in complex legal documents, providing a scalable and accurate solution for automated document review in legal contexts.