In the latest episode of the New York Second District’s most tantalizing e-discovery soap opera, U.S. District Court Magistrate Judge Andrew Peck has issued a 31-page “Report and Recommendation” to his colleague, District Judge Andrew Carter, urging Carter to deny plaintiffs’ motion to file a third amended complaint, in the ongoing gender employment discrimination dispute, Monique Da Silva Moore, et al. v. Publicis Groupe SA and MSLGroup, 11 Civ. 1279.
Ranking is a key feature of AI enhanced document review, perhaps the key feature. Ranking orders all documents in a collection according to likely relevance as you define it. You have to train the software on a few exemplar documents, but then as the training kicks in, the software ranks all of the documents in the collection in accordance with your instructions.
On May 6, 2013, the U.S. Department of Justice’s (“DOJ”) Antitrust Division approved Constellation Brands Inc.’s (“Constellation”) and Crown Imports LLC’s (“Crown”) request to use predictive coding to determine which documents were most relevant and responsive to the DOJ’s requests. Constellation is a potential buyer of assets from the huge AB InBev-Grupo Modelo merger, and Crown is a joint venture between Grupo Modelo and Constellation. Reportedly, Constellation and Crown identified in excess of one million documents that would require manual review before being handed over to the Justice Department for scrutiny. After several seed sets were run using the automated data review software and compared manually, DOJ was satisfied that the predictive coding software would identify the most relevant documents and approved its use.
Ralph Losey has once again turned over his blog to me to report on what transpired at the DESI V Workshop with the latest iteration of this workshop series held in Rome on June 14, 2013, as part of the 14th International Conference on Artificial Intelligence and Law (ICAIL 2013). DESI V was focused on standards for using predictive coding, machine learning, and other advanced search and review methods in e-discovery, with a high degree of tolerance on the part of the organizers in accepting interesting e-discovery papers across a range of nominally related topics.
Kaleida Health isn’t taking a May decision by U.S. Magistrate Judge Leslie Foschio (W. District, N.Y.) lying down. Foscho refused to disqualify e-discovery vendor D4 Discovery . (Hat tip: Bob Ambrogi at Catalyst .) On Friday, Kaleida , the largest non-profit health care provider in Western New York, filed papers with the U.S. District Court in Buffalo reaffirming its stance that Foschio erred and D4 should have been disqualified. Kaleida had originally hired D4 in 2010 after Kaleida was sued by a group of employees in a wage-and-hour class action alleging that they were owed regular and overtime wages.
Attorneys representing a group of employees suing public relations company Publicis over gender discrimination claims took their e-discovery dispute with U.S. Magistrate Judge Andrew Peck (So. District, N.Y.) to the U.S. Supreme Court on Tuesday, in Monique Da Silva Moore, et al. v. Publicis Groupe SA and MSLGroup, 11 Civ. 1279. In a petition for certiorari filed with the court, attorneys representing lead plaintiff Monique Da Silva Moore and five other employees argued that Peck, who approved an e-discovery protocol agreed to by the parties that included predictive coding technology, should have recused himself given his previous public statements expressing strong support of predictive coding.
The legal community has been increasingly eager to identify opportunities to leverage review technology to better manage the scope and cost of discovery. McDermott Will & Emery recently took advantage of such an opportunity when members of its antitrust and discovery groups negotiated with the U.S. Department of Justice to develop a win-win predictive coding protocol that met the department’s and our client’s needs.
Despite this guidance, some attorneys may feel overwhelmed by the current state of predictive coding technology and they will be inclined to remain on the sidelines until the technology and case law evolve. If you find yourself in the “wait-and-see” camp, this article just might convince you that the time to try predictive coding has arrived. Here in Part 2, the top 3 eDiscovery & compliance predictive coding use cases for the “risk-averse” attorney are discussed. Each of the use cases summarizes different situations where users may be able to reap the rewards of predictive coding while minimizing the risk of something going wrong.
Of all the challenges facing e-discovery practitioners, none is more daunting than that which Stuhledreher (2012) calls searching for that needle-in-the-haystack in masses of electronically stored information in all its new and evolving forms, and identifying that comparatively small set of documents that are relevant to the matter at hand, and from among those, finding the rarer documents that really matter, that truly mean something. Practitioners are asked to do all this, and do it well – effectively and efficiently.
Based on a compilation of research from analyst firms and industry expert reports in the electronic discovery arena, the following “Top 100+Provider” list provides a short listing that may be useful in the consideration of electronic discovery providers. This listing is taken directly from eDiscovery provider mentions in selected key formal industry reports and surveys published between August 2011 and June 2013. Where appropriate, the list has been adjusted for industry mergers and acquisitions, with the primary company listed as the recognized eDiscovery provider.