Privilege Review Precision Achieved with AI
GC Avoids Providing Hundreds of Privileged Documents By Automating Review



The Dilemma

Leverage Automation For Efficiency Without Elevating Risk

As the general counsel of a Fortune 500 industrial company, the customer faced a challenge common to many legal departments: how to leverage automation to reduce the amount of routine work done manually by the team so they could better focus on questions requiring closer deliberation and expert legal advice.

However, the area of privilege review where the team had spent the most time on manual review, is also one with significant risk for the enterprise. Even a single document that is missed in the process of determining what constitutes privileged information from tens of thousands can later prove to be critical to the outcome of a matter.

In consultation with the e-discovery and litigation teams, the GC initiated a project to identify a tool which could reduce the relative level of manual effort for privileged review without elevating the risk of an already high stakes activity.

In his search for a solution, the GC evaluated the potential for Text IQ’s AI-driven technology to automate the first level review of potentially privileged documents, with the intent to reduce the overall number of documents requiring manual review, and, as a result, consume less of the team’s resources.

To address the concerns of key stakeholders and senior officers within the company that the automated first level review process would fail to classify documents as privileged, the GC went through a Proof of Concept (POC) process. He used the traditional method of relying on human reviewers for privilege review while running Text IQ’s AI in parallel. Conducting this POC, the GC could test Text IQ and have real metrics on its performance compared to the output of the company’s existing approach, providing a comparison to demonstrate the technology’s efficacy for automation and risk mitigation.

“We would have turned over, in that case, hundreds and hundreds of documents that were privileged.” 


Reduced Risk


Identified Missed Attorneys

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Fraction of the Cost


The Resolution


Through the use of unsupervised machine learning and proprietary Socio-linguistic Hypergraph, Text IQ’s AI platform understands the context behind the documents. It returned results in just 2.5 weeks. The GC was surprised to see 66 attorneys that were not on the known attorney list identified by the AI platform.

Text IQ’s AI delivered its privilege review in less time than human reviewers and with significant savings. With a Text IQ subscription, the cost of document review with Text IQ would be one-sixth the cost of human reviewers performing the same process.

Not only was Text IQ faster and more cost effective than the existing approach, Text IQ’s review identified more documents potentially considered privileged. Text IQ reduced risk by identifying more privileged documents than human reviewers would have otherwise missed.

On the basis of the POC results - and, in particular, the identification of additional attorneys not listed in the original known attorney list - the GC gained buy-in from formerly skeptical stakeholders.

The GC now has the opportunity to reduce the time spent on first level review, assign fewer lawyers to first level review, ensure a higher quality review by focusing on a smaller number of documents and better identify potentially privileged documents.

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