Transparent Predictive Coding enables review teams to achieve highly accurate results with minimal input, significantly reducing review time and cost. As a form of technology-assisted review, it works by leveraging algorithms that learn tagging criteria from a manually-reviewed training set, enabling users to generate accurate tagging predictions for the remaining documents.
Reviewers benefit from complete visibility into how predictions are generated and the accuracy of predictions across the case. As part of the Clearwell eDiscovery Platform's Review & Production Module, Transparent Predictive Coding is fully integrated with the rest of the eDiscovery process and can be used in conjunction with linear review. The net result is an accurate and defensible review process at significantly reduced time and cost.
Symantec's Transparent Predictive Coding is:
- Accurate: Produces highly accurate results with minimal human input, further reducing cost and risk.
- Transparent: Provides visibility into prediction process, so users can make informed decisions with confidence in results.
- Defensible: Delivers robust, built-in sampling tools and auditing to document results and demonstrate process integrity.
Key Features of Transparent Predictive Coding
Training Source Analytics — Offers sophisticated analytics by criteria such as custodian, discussion, concept, and participant to ensure the selection of highly relevant initial training sets.
Directed Training — Leverages patent-pending active learning technology to automatically suggest each subsequent training set after the initial training iteration for optimal system learning.
Prediction Insight — Automatically provides a prediction score for the document under review, displaying content and metadata relevant to the prediction.
Review Quality Control — Provides a built-in quality control workflow enabling users to assess and improve review accuracy of technology-assisted review.
Advanced Sampling — Provides built-in statistical sampling tools that allow users to select the correct random sample based on the accuracy requirements of the case.
Prediction Templates — Enable users to import and export prediction models to leverage predictive intelligence across cases.