The Power of Predictive Coding in Managed Document Review

Predictive coding, also known as technology-assisted review (TAR) or computer-assisted review, is a powerful tool used in managed document review to expedite the review process and increase efficiency. It involves leveraging machine learning algorithms to predict the relevance or responsiveness of documents to a particular legal matter.

Here’s how predictive coding works in managed document review:

Training the system: Initially, a sample set of documents is manually reviewed and categorized by human reviewers based on their relevance to the legal matter. These categorized documents serve as the training set for the predictive coding system.

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Machine learning algorithms: The predictive coding system uses sophisticated machine learning algorithms to analyze the characteristics of the training set, such as keywords, concepts, metadata, and document structure. It learns the patterns and relationships between relevant and non-relevant documents.

Predictive ranking: Once the system is trained, it assigns a relevance score or ranking to the remaining unreviewed documents based on their similarity to the documents in the training set. The system identifies documents that are likely to be relevant to the legal matter, prioritizing them for human review.

Iterative process: The human reviewers then review the top-ranked documents and confirm or correct the predictions made by the system. This feedback is used to further refine the predictive coding model in subsequent iterations. As the system receives more feedback, it becomes increasingly accurate in predicting document relevance.

Benefits of predictive coding in managed document review:

Increased efficiency: Predictive coding significantly reduces the number of documents that need to be manually reviewed, saving time and costs associated with the review process. It allows reviewers to focus their efforts on the most relevant documents, rather than sifting through large volumes of data.

Consistency and accuracy: By leveraging machine learning algorithms, predictive coding offers consistency in document categorization and relevance determination. It minimizes human errors and biases that can occur during manual review, leading to more accurate and reliable results.

Cost savings: Traditional manual document review can be expensive, particularly when dealing with large volumes of data. Predictive coding helps reduce costs by streamlining the review process and eliminating the need for extensive human review of every document.

Scalability: Predictive coding is highly scalable and can handle large document collections efficiently. It can process thousands or even millions of documents, allowing organizations to tackle complex and time-sensitive legal matters with greater ease.

Defensibility: Predictive coding can provide defensibility in the review process. The use of machine learning algorithms and documented protocols can demonstrate a consistent and transparent approach to document review, which can be important in legal proceedings.

While predictive coding is a powerful tool, it’s important to note that it is not a replacement for human reviewers but rather a complement to their expertise. Human review and oversight remain crucial for quality control and ensuring the accuracy of predictions made by the system.

Overall, the power of predictive coding in managed document review lies in its ability to expedite the review process, increase efficiency, reduce costs, and improve the accuracy and consistency of document categorization.

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