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Predictive Coding is an instance of Technology-Assisted Review processes used to speed up eDiscovery data searches. Since 2012, it has revolutionized the analysis of documents for quickly finding material most relevant to a particular legal case.
In 2015, the Federal Rules of Civil Procedures further encouraged litigants to perform electronic discovery in the most efficient manner and to utilize technological solutions whenever possible to do so in order to increase transparency and reduce the time and money spent on eDiscovery.
How It Works
Predictive coding is a software-based, machine-learning process. The software is “seeded” by applying it to a subset of documents, the seed set, to test document responsiveness to selected search terms. Search terms are refined as the software generates on-the-fly algorithms for subsequent searches. These steps are repeated until a suitable responsiveness threshold is achieved. The final algorithm is then applied to much larger document sets.
Predictive coding may also be referred to as predictive intelligence, computer-assisted review or technology-assisted review (TAR).
Predictive Coding’s Acceptance
A ruling by Federal Magistrate Judge Andrew Peck in 2012 was the first official endorsement of predictive coding for the discovery phase of a case, after which many jurisdictions accepted it as a means to speed up discovery. However, recent surveys of judges across the country suggest that slightly less than half of legal teams actually employ it regularly.
Obstacles to Full Acceptance
The alternative to predictive coding is, of course, manual document review despite its laborious nature. The preference for manual review can be attributed to three main reasons:
Predictive Coding Outside the Legal Arena
The increasing adoption of the concept of Forensic Readiness by businesses provides a good example of where predictive coding can be utilized beyond litigation discovery. It can streamline the process of pre-discovery of enterprise data from any source, in-house or in the cloud, including email, databases, social media, system logs, archives and so on.
In the hands of a company that offers a highly qualified, experienced eDiscovery service, predictive coding would drastically cut the costs of locating data from these sources that is relevant to, say, regulatory compliance, customer service activities, intellectual property protection or data retention policies.
Staying Power
It seems that regardless of the obstacles to predictive coding acceptance in the legal area, it is here to stay. Further judicial rulings accepting predictive coding for eDiscovery, improvements in the software and the larger trends of improving efficiency through digital technology are sure to make its use ever more widespread.
