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US Tax Court Approves Computer-assisted Review

Constantine Pappas

Last week, Judge Ronald L. Buch of the United States Tax Court allowed a party to use computer-assisted review to find relevant documents on two backup tapes which contained archived emails. The order granted permission over the objections of the IRS, who unsuccessfully alleged that predictive coding technology is unproven.

The case—Dynamo Holdings v. Commissioner, 143 T.C. No. 9—concerns a controversy relating to whether certain transactions between Beekman Vista, Inc. and Dynamo Holdings Limited Partnership were loans or disguised gifts. The IRS requested access to the complete tapes, indicating that Dynamo could simply “claw back” any privileged or non-relevant information after the fact.

Dynamo, concerned with its obligation to protect privileged data, felt that such a measure would be overly intrusive. Furthermore, Dynamo alleged that it would require at least $450,000 to review the two backup tapes via a traditional, manual workflow, and argued that the use of computer-assisted review could reduce the cost to around $85,000—roughly 19 percent of the traditional estimate.

The Court’s order indicates that computer-assisted review serves as a reasonable compromise, balancing the IRS’s need to obtain relevant documents against Dynamo’s obligation to protect its privileged information. Specifically, Judge Buch states:

We find a potential happy medium in petitioners’ proposed use of predictive coding. Predictive coding is an expedited and efficient form of computer-assisted review that allows parties in litigation to avoid the time and costs associated with the traditional, manual review of large volumes of documents. Through the coding of a relatively small sample of documents, computers can predict the relevance of documents to a discovery request and then identify which documents are and are not responsive.

In his order allowing Dynamo to use predictive coding, Judge Buch approached his decision by comparing computer-assisted review technology with other traditional review methods:

[A]lthough it is a proper role of the Court to supervise the discovery process and intervene when it is abused by the parties, the Court is not normally in the business of dictating to parties the process that they should use when responding to discovery. If our focus were on paper discovery, we would not (for example) be dictating to a party the manner in which it should review documents for responsiveness or privilege, such as whether that review should be done by a paralegal, a junior attorney, or a senior attorney.

This common-sense, even-measured approach is an important step forward, as it holds computer-assisted review to the same standard of reasonableness as other discovery methods. The legal debates of late have largely focused on whether predictive coding technology should be held to a higher standard, particularly where transparency of process is concerned.

Judge Buch further addressed the IRS’s concerns should Dynamo’s computer-assisted review-derived production be found lacking or incomplete:

If respondent believes that the ultimate discovery response is incomplete and can support that belief, he can file another motion to compel at that time.

It is surprising that this fact isn’t brought up more often. Nothing about using computer-assisted review will prevent a party from seeking additional information if a production is deemed incomplete. Many of those with reservations about the technology’s efficacy seem to overlook the fact that they are not without recourse should they find a production to be somehow lacking.

We’re excited to see increased adoption for computer-assisted review in the marketplace, as well as growing judicial acceptance. If you have any questions about the most defensible way to build your computer-assisted review workflows in Relativity, please don’t hesitate to contact us.


Constantine Pappas is a licensed attorney with more than 15 years of legal experience. He has served as in-house counsel and managed both paper and electronic discovery for large-scale lawsuits and government investigations. As a member of Relativity’s customer success team, Constantine helps Relativity users with workflows for text analytics and computer-assisted review.

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