North East law firm is using artificial intelligence in multi-million pound cases

Newcastle-based Muckle LLP has revealed how artificial intelligence technology has helped to speed up the disclosure process on a multi-million pound dispute claim.

With the application of AI becoming increasingly apparent in most industries, Muckle is one of the first law firms in the region to integrate the time-saving technology.

The firm’s Dispute Resolution team, headed by Partner Susan Howe, is using predictive coding or ‘technology assisted review’ (TAR), to analyse hundreds of thousands of disclosure documents linked to an ongoing multi-million pound claim.

The first case initially involved one million disclosure documents, which was cut to 660,000 after a standard keyword search, and then the use of this innovative technology enabled Muckle to reduce that number down by more than 90% to 35,000.

Traditionally, the documentation would have to be manually analysed and assessed for relevance by members of the team but TAR removes these first few relevancy stages, in turn saving time and the associated costs.

Susan said: “Using this type of technology is relatively new and is now being recognised by the court. Firms in London are investing very heavily in artificial intelligence systems but, regionally, it’s not something that is widely used yet.

“The Dispute Resolution team here at Muckle increasingly work on large claim cases where disclosure documents can be in the hundreds of thousands so having access to groundbreaking software such as TAR is game-changing.

“A process which would traditionally involve a team of trainees or paralegals turning the pages in a room for a year is now significantly condensed thanks to this cutting-edge computer programme.”

Muckle has enlisted the services of advanced discovery firm Millnet to access the predictive coding technology for this case.

Predictive coding is the use of keyword search, filtering and sampling to automate portions of an e-discovery document review. The aim is to reduce the number of irrelevant and non-responsive documents that need to be reviewed manually.

There is an initial level of investment required from the legal team to teach the program keywords and test the algorithm on a set of example documents.

The algorithm analyses the characteristics of the documents and learns from the lawyers’ decision-making to enable it to then identify similar documents and rank them in relevance.

Susan added: “There is a lot of work involved in training the computer but once this is done, it is incredible.

“And while the cost of using the technology is significant, in the context of substantial claims like our current one, it is worth the effort and the expense to be able to drop 75% or more of your documents in a single swoop.

“As a litigator, it’s understandable to feel uneasy having to rely on technology, but once you use the system and understand you’re the one training it, then it becomes very easy to trust.”

“The court is now encouraging the use of this type of technology so we will definitely use it again on future large claims.”

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