Embracing E-Discovery In Antitrust Matters: Slow But Steady Progress Toward Convergence Between The U.S. And The UK?

Lawyers are sometimes risk adverse and slow to change. Although this tends to lead to a more cautious approach to embracing new technologies, including the use of artificial intelligence, the increasing burden of e-discovery has forced the issue. Lawyers on both sides of matters increasingly are embracing the rise of a technology known as "predictive coding" to identify responsive and nonresponsive documents in private litigation and government investigations. While the United States is on the leading edge of this trend, other jurisdictions, including the United Kingdom, have been slower to follow suit, particularly in antitrust matters.

This is a timely and important issue. Recent research shows that nearly half of the cases requiring UK electronic corporate data to be processed were either in preparation for, or in response to, UK or foreign antitrust and regulatory matters. This dynamic has led to predictions that lawyers in the UK (and elsewhere) are expected to make greater use of artificial intelligence in the near future.

The U.S. experience is illustrative. The two federal antitrust agencies—the U.S. Department of Justice ("DOJ") and Federal Trade Commission ("FTC")—have agreed with parties that predictive coding is useful to cull large volumes of electronically stored information in antitrust investigations. By contrast, there has not been any clear statement on this subject from the UK's Competition and Markets Authority ("CMA"), the UK sector regulators, or the courts. That changed in February 2016, when the High Court of England and Wales for the first time endorsed the use of predictive coding in the UK, relying in large part on judicial acceptance of the technology in the U.S. (Case No: HC-2014-000038, Pyrrho Investments Limited and another v MWB Property Limited, [2016] EWHC 256 (Ch)).

This Commentary discusses the latest trends in the use of predictive coding in U.S. and UK antitrust matters, and how Pyrrho is likely to spur slow but steady progress toward greater acceptance in the UK.

What Is Predictive Coding?

Use of e-discovery tools to alleviate the burdens associated with document-intensive matters is not new. Since the mid-1980s, private litigants have agreed to use keyword searches, "concept-based" searches, and most recently predictive coding as alternatives to manual document-by-document ("linear") review. Generally speaking, predictive coding is a form of artificial intelligence that uses human reviewers' examination of a subset of documents (so-called "seed documents") to "train" computer algorithms to review and "predict" what other documents are responsive. Nowadays, the term "predictive coding" is used interchangeably with "technology-assisted review" ("TAR"), "computer-assisted review," or simply "assisted review."

How Does It Work in Practice?

There are a number of different software platforms capable of performing the necessary analytics for predictive coding. Lawyers work with e-discovery vendors to understand the capabilities of the predictive coding software to ensure that the document population is handled appropriately. For example, some predictive coding models cannot categorize certain file types, which would need to undergo a linear review. However, other predictive coding software platforms do not have the same limitations. In terms of training protocols, there are two broad categories of how predictive coding models can be trained. In "passive learning" protocols, the model is trained by evaluating multiple sets of random samples of documents coded by attorney reviewers. In "active learning" protocols, the computer helps select certain "borderline" documents for attorneys to review to further refine the model more efficiently than entirely random document sets would.

While the various software platforms may employ assorted processes and have varied limitations, a key objective across all of them is the ultimate...