Transforming Responsive and Privilege Reviews with AI

Advancements in the last decade have brought artificial intelligence out of theory and into operation. In the legal arena, the implications of this change may be particularly profound in the areas of privilege and responsive reviews, where challenges are rapidly evolving. That has meant exploring predictive coding and technology assisted review; there are two significant challenges, however, that make it difficult to employ them.

Predictive coding requires a subject-matter expert, typically a senior attorney who is unlikely to have the time to devote to this effort, to hand label documents in order to create a “seed set.” Second, predictive coding is constrained to looking within the four corners of the document, which means that it doesn’t take into account any of the broader ecosystem of documents where valuable context lies.

The author designed a test with the AI company Text IQ. He found that AI (unsupervised machine learning, where a system takes on huge scales of unstructured data and makes meaningful deductions that no team of humans could make, given the scale and velocity of the data) could successfully automate privilege review. He then tested its ability to implement substantive responsiveness, or “first-pass review.” AI quickly knocked out 50 percent of the population of documents that were non-responsive, drastically reducing time and money spent on paying attorneys to manually review documents. AI is not only transforming the results of his discovery, it is transforming the way he manages the discovery process itself.