Predictive coding uses a supervised, machine learning (a type of artificial intelligence) to assist an attorney in the review and classification of electronically stored information. Predictive coding type software analyzes whole documents in a dataset, not just keywords, and uses advanced mathematics, including near-infinite-dimensional vector space probability analysis, to order, compare, and rank them.
In predictive coding driven CARs attorneys train a computer to find documents identified by the attorney as a target, typically as relevant to a particular lawsuit, or some other classification, such as privileged.
Below is the diagram of the latest Predictive Coding 3.0 workflow for use in a typical CAR project.
For a full description of the eight steps see Predictive Coding 3.0 part two.
Please remember that before you begin to actually carry out a predictive coding project as described, you need to plan for it. This is critical to the success of the project. We suggest you consult this detailed outline of a Form Plan for a Predictive Coding Project for a complete checklist.
The use of multimodal judgmental sampling in steps three and seven to locate relevant documents follows the consensus view of information scientists specializing in information retrieval, but is not followed by several prominent predictive coding software vendors in e-discovery. They instead rely entirely on machine selected documents for training, or even worse, rely entirely on random selected documents to train the software. See Part One of Predictive Coding 3.0 where some of the errors in Predictive Coding 1.0 and 2.0, are described.