Use of Predictive Coding for e-Discovery in Litigation – UPDATE

by Tony Rushin

As discussed in the blog that I wrote on June 8th, it appears that predictive coding is gaining acceptance.

Since that time, Law Technology News has reported that Judge Shira Scheindlin, U.S. District Court for the Southern District of New York , issued another landmark e-discovery decision on July 13, addressing best practices in search — including specifically predictive coding.

“There are emerging best practices for dealing with these shortcomings and they are explained in detail elsewhere. There is a ‘need for careful thought, quality control, testing, and cooperation with opposing counsel in designing search terms or keywords to be used to produce emails or other electronically stored information.’ And beyond the use of keyword search, parties can (and frequently should) rely on latent semantic indexing, statistical probability models, and machine learning tools to find responsive documents.

“Through iterative learning, these methods (known as ‘computer-assisted’ or ‘predictive’ coding) allow humans to teach computers what documents are and are not responsive to a particular FOIA or discovery request and they can significantly increase the effectiveness and efficiency of searches. In short, a review of the literature makes it abundantly clear that a court cannot simply trust the defendant agencies’ unsupported assertions that their lay custodians have designed and conducted a reasonable search.”

Law Technology News has also shared its take on “Debunking the Myths about Predictive Coding”, a primer on predictive coding offered at ALM’s Virtual Corporate Counsel Forum on July 19. Perfect for those who need translation help to understand all the recent rulings in e-discovery cases.

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Tony Rushin: In his role as VP of sales & marketing, he is responsible for hiring, managing and coaching the sales team and always staying on top of (and meeting) the needs of clients. Under his leadership, the company has steadily grown each year.

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