Leveraging machine learning for e-discovery

Leveraging machine learning for e-discovery

October 22, 2018

The legal profession is one among the many industries that is being disrupted by machine learning. Earlier, discovery involved attorneys leafing through paper documents. With the advent of digitization, only the format has changed — attorneys are still looking through documents albeit in the electronic format. This has given rise to the desire to limit the amount of time spent on discovery. Therefore, attorneys are adopting keyword search technologies. According to David Raths, a Philadelphia-based freelance writer, a process called technology – assisted review (TAR) has taken the process of simplifying e-discovery a step further.

How does TAR help in e-discovery? It applies machine learning to review electronically stored information. This has the potential to save time and money by avoiding the review of irrelevant documents. According to a draft guideline document produced at the Duke University Law School, “[a]A human reviewer reviews and codes documents as ‘relevant’ or ‘nonrelevant’ and feeds this information to the software, which takes that human input and uses it to draw inferences about unreviewed documents. The software categorizes each document in the collection as relevant or nonrelevant or ranks them in order of likely relevance.”

Most of the largest law firms and many U.S. government agencies including the Department of Justice are deploying TAR or have recognized its value. Yet, many attorneys and judges are still unfamiliar with TAR and are unsure of how to apply it. For one, the costs and complexities of TAR and the potential for disputes with opposing parties could neutralize the benefits that the use of TAR might otherwise generate. Another source of dispute about using TAR to find relevant documents is that there is a natural tension between requesting and producing parties. For instance, a small environmental organization that is going after a big corporation is going to be skeptical of how the company fine tunes its algorithm to search for relevant documents.

Click here to learn about the new guidelines being framed for TAR.

Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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