Explanation of the Legal Profession’s Remarkably Slow Adoption of Predictive Coding
Well-known predictive coding expert attorney, Maura Grossman, and her husband, noted information scientist, Gordon Cormack, recently began on article in Practical Law magazine with the assertion:
Adoption of TAR has been remarkably slow, considering the amount of attention these offerings have received since the publication of the first federal opinion approving TAR use (see Da Silva Moore v. Publicis Groupe, 287 F.R.D. 182 (S.D.N.Y. 2012)).
Grossman & Cormack, Continuous Active Learning for TAR (Practical Law, April/May 2016).
TAR, which stands for Technology Assisted Review, is their favorite term for what the legal profession commonly calls predictive coding. I remember when our firm attained the landmark ruling in our Da Silva Moore case. I thought it would open a floodgate on new cases. It did not. But it did start a flood of judicial rulings approving predictive coding all around the country, and lately, around the world. See Eg. Pyrrho Investments v MWB Property, EWHC 256 (Ch) (2/26/16). Judge Andrew Peck’s more recent ruling on the topic contains a good summary of the law. Rio Tinto PLC v. Vale S.A., 306 F.R.D. 125 (S.D.N.Y. 2015). The bottom line is that at this point in time, late May 2016, the Bench is waiting for the Bar to catch up.
Although I am known for my exuberant endorsement of predictive coding, this enthusiasm for new technology to find electronic evidence is still rare in the legal profession. Losey, R., Why I Love Predictive Coding: Making document review fun with Mr. EDR and Predictive Coding 3.0. (2/14/16). So why do I love this technology so much, and most other lawyers, not so much? It may have to do with the fact that I have been using computers since 1978 and am very used to pushing the technology edge. But that just explains why I was one of the first to knock on the door of predictive coding, not why I like the room. I have been an early adopter of many technologies that proved disappointing. (Anybody want to buy a slightly used iWatch?) No, I like it because it really works.
This in turn raises the question why have not all attorneys had this same reaction. If it really works for me, it should really work for everyone, right? And so everyone should be loving predictive coding, right? No. It is not working for everyone. Many have had unpleasant experiences with predictive coding. They left the room bored and frustrated. I am reminded of the old commercial, Where’s the beef? They went back to their old familiar keyword searches. Pity.
It took me a while to figure this out, that others were having failures and not talking about it. (Who can blame them?) In retrospect I should have seen this earlier. Still, it took Grossman and Cormack until 2015 to figure this out too. Interestingly, we have come to the same conclusion on causation. Bad software is not the main reason, although varying software quality among vendors is part of the explanation. Some software on the market is not that good, or does not even have bona fide predictive coding features using active machine learning. But these software differences only explain some of the dissatisfaction. The real reason for the failures is that attorneys have not been using the predictive coding features properly. They have been doing it wrong. That is why it did not work well for them. That is why many attorneys tried it out and never returned.
Grossman and Cormack explain this and provide their best-practice methods in the new article, Continuous Active Learning for TAR, and many other articles they have written since 2015. I read and recommend them all. I have shared my own best practices in my lengthy personal blog, Predictive Coding 3.0 article. Part one describes the history and part two describes the method. Our best practices are not exactly the same, but they are close and compatible. I have written a total of 59 articles on the subject now that are currently all online and freely available. I call the method Hybrid Multimodal and its basic steps are shown in the figure below.