Law firms can reduce the cost of review in large e-discovery projects by using advanced search technologies such as active machine learning, known as Predictive Coding, TAR (Technology Assisted Review) or CAR (Computer Assisted Review). For medium to large law firms looking to cut the costs of admin, this will become more and more important over the next 10 years.
Predictive coding helps firms save on admin and system costs by finding relevant documents in a large collection, faster than traditional methods.
It’s estimated that 70 percent of total e-discovery production costs are taken up by outside counsel according to Rand corporation.
Machine learning is a type of artificial intelligence that involves programming a computer to find relevant documents in a large collection and it is generally accepted by most courts and has been for a number of years.
Machine learning is now a big part of most people’s lives without them realising, from Amazon recommendations to watching movies on Netflix of listening to music on Spotify. The computer learns what our preferences are and this is the same with legal software.
During the last 10 years, there has been an increase in great studies and explanation of predictive coding software. Including that of Jason Baron.
Scientific research and AI has made great progress over the past 10 years, so if you are working on ediscovery projects, bear it in mind.