Discovery, as all lawyers know, is the process of collecting and exchanging information about the court case to prepare for the trial. Traditionally, this was done by many lawyers over countless billable hours in which every page of potential evidence was examined for important information. Because of this, the more information existed in reference to...
Sharon D. Nelson, Esq. is president of the digital forensics, managed information technology and cybersecurity firm Sensei Enterprises. Ms....
John W. Simek is vice president of the digital forensics, managed information technology and cybersecurity firm Sensei Enterprises. He...
Discovery, as all lawyers know, is the process of collecting and exchanging information about the court case to prepare for the trial. Traditionally, this was done by many lawyers over countless billable hours in which every page of potential evidence was examined for important information. Because of this, the more information existed in reference to a case, the more expensive the case was. As technology developed, law firms began using computers to do keyword searches and conceptual searches. Unfortunately, there were problems including picking the right keywords or concepts, misspelled words, how to structure the items, and that these searches only yielded 20% of important data. Recently, technology has advanced to predictive coding, or teaching a computer program to think like a lawyer would. But how cost effective and practical is predictive coding, and how well does it actually work?
In this episode of The Digital Detectives, Sharon Nelson and John Simek discuss the evolution of technology and case discovery, how predictive coding works and is priced, and examples of cases that have involved predictive coding. Simek first explains the importance of culling, or filtering out unimportant data sets through DeNISTing, deduping, or filtering by dates. He then explains predictive coding in its simplicity: to feed a computer program information based on discovery attorneys have already done until the computer can accurately predict which information is important. Simek and Nelson then go on to examine the prices vendors charge for the predictive coding process and in which cases it might be profitable for the law firm or client. There is a steep, expensive learning curve involved; many mid-sized law firms probably will not profit and even very large cases only save an average of 15% using predictive coding. However, Nelson explains, predictive coding is the future of discovery, so it is important for lawyers to pay attention to when the benefits outweigh the costs.
Nelson concludes the podcast by giving examples of when predictive coding has already appeared in court cases. The landmark case was Da Silva Moore v. Publicis Groupe, in which Magistrate Judge Andrew Peck allowed predictive coding to be used as long as the defense and prosecution agree to its use, there are a large volume of documents, it is the superior technology, it is more cost effective, and it is transparent and defensible. Inevitably, the conclusion is that it is not for the judge to micromanage the discovery process.
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