Ten Essential Best Practices in Predictive Coding

This article outlines emerging best practices in the application of predictive coding to e-discovery, which is now approved by multiple courts in the United States and abroad. Although the classification technologies that underlie predictive coding applications in e-discovery have been used in a broad range of industrial and scientific settings for decades, stringent defensibility requirements in e-discovery make best practice procedures necessary.

Predictive coding technology will encode either correct or incorrect guidance from the trainer. Thus, the first best practice is to give due consideration to choosing a trainer. This person needs to be a knowledgeable attorney, with the authority to make review decisions that are likely to have a significant impact on the outcome of the case.

Validate results. As emphasized in a recent court decision that the author references, quality assurance is a key component of predictive coding. The objective of quality assurance is to provide transparent validation of the results generated by the application. One key test is to verify culling decisions.

Predictive coding is a dynamic and rapidly developing arena, and best practices will continue to evolve. In documenting them as they have taken shape over the past year or so, the author’s intention is not to define a universal textbook template for the generic predictive coding project, but rather to provide a platform from which it will be possible to develop, refine and create new and better practices, as the e-discovery industry continues to assimilate the technology.

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