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Using a search engine as a resource locate the Da Silva Moore v. Publicis Groupe, No. 11 CIV. 1279 ALC AJP, 2012 WL 607412 case. Using the FIRAC (facts, issues, rules and references, analysis, and conclusions) method, examine the case and that will involve highlight portions of the file. When reading or reviewing the case, use a word processor’s highlighting feature to highlight important facts in your case using the colors defined below in the FIRAC highlighting guide. Finding important items is a lot easier later using the FIRAC colored highlighting guide.

Refer to the attached FIRAC Analysis Example.doc. Remember the key to this assignment is to use the appropriate colors as define in the above FIRAC guide to highlight important facts in your case.

Upon analyzing the case, you should have been able to gather enough information 1) to apply the FRIAC (facts, issues, rules and references, analysis) method to the Da Silva Moore case and 2) fully answer questions 1 – 3 pertaining to the Da Silva Moore case as listed below.

Additionally, it is not necessary to locate and/or retrieve a final Westlaw and/or LexisNexis case law summary/report. There are many case summaries available via the internet of this historical ground breaking case which paved the way for the usage of Computer Assisted Review (machine learning) to be used in eDiscovery to comb thru the millions of pages of records/documents that is NOW being collected and produced thru eDiscovery.

1. Technology Assisted Review (“TAR”), also known as Computer Assisted Review (“CAR”), uses software to analyze, search, and categorize documents that are relevant for the purposes of discovery in those cases or investigations (“matters”) that require the review and production of materials in electronic or “e-discovery” form. TAR and CAR are also often used interchangeably with the term “Predictive Coding”—though, to be more precise, TAR and CAR encompass many applications, of which the most commonly discussed is predictive coding. TAR was introduced primarily as a means of reducing costs associated with discovery efforts, and predictive coding is a type of TAR in which an algorithm leverages decisions made by an attorney reviewer regarding a subset of documents to make predictions regarding the relevancy of each document in the data set. Scientific evidence suggests that certain TAR methods offer not only reduced effort and cost, but also improved accuracy, when compared to manual review. Manual review involves human review and coding of each and every document in the collection. Do you agree and/or disagree with the method/technique “TAR” used to review evidence in the Da Silva Moore case? Please explain your answer.
2. Based on Magistrate Judge Andrew J. Peck’s judicial opinion endorsing the use of TAR in “large-data-volume” case, do you believe that the plaintiffs’ objections/concerns on the use of TAR were appropriate? Please expound upon your answer in-depth.
3. Despite the pronouncement (as well as similar court opinions Judge Peck referenced in the Da Silva Moore case), most judges, litigators, and clients are still apprehensive in utilizing TAR even though TAR has been proven as an option that “should be seriously considered for use in large-data-volume cases where it may save the producing party (or both parties) significant amounts of legal fees in document review. Do you believe that this stigma is due to lack of education in the legal profession or the simple fact lawyers are more deeply immersed in a paper-based mind-set as oppose to embarking on technological change in the legal practice? Please expound upon your answer in-depth.

Upon review of the case, please answer the above questions: following naming convention to name your document: lastname, firstname – C2L3.doc.