publication of the International Legal Technology Association
Issue link: https://epubs.iltanet.org/i/669172
LITIGATION AND PRACTICE SUPPORT 27 WWW.ILTANET.ORG | ILTA WHITE PAPER An Australian Perspective on Avoiding Hidden E-Discovery Costs to make sure all the information has been captured. Due to the complexity of the task, many people would rather review documents in chronological order and deal with duplicates in the review platform rather than work out the logic of which field/tag means what. But when you have more than one person on a review team, this on-the-fly approach does not ensure review consistency, which adds more work for secondary reviewers, i.e., senior lawyers. The good news is review platforms today oen come with integrated duplication and email threading analysis functions that help identify documents with over 95 percent similar content. Color codes provide additional details about how similar the documents are. It immediately becomes obvious for the reviewer that the duplicates identified by the review platform should be coded consistently. The same logic goes for email threading analysis. Predictive Coding Some people embrace the concept of predictive coding, while others remain skeptical. Predictive coding is a machine learning process. Artificial intelligence has challenged the best human chess master and best human driver, and there is no reason it cannot outsmart the best human document reviewer. We use predictive coding for review prioritization. The model can be used to identify documents most likely to be relevant and put them as the first few review assignments. In a recent maer, aer a small review of 3,000 documents identified by keyword searching, the predictive model identified 1,000 documents with a predictive relevance over 80 percent. By searching all the unreviewed documents among that 1,000, we identified 524 documents not yet reviewed by the legal team, who was very SHENGSH ZHAO Shengshi Zhao is a Senior Consultant at NuLegal (Sydney, LTS consulting company). She has an actuarial science degree from Worcester Polytechnic Institute and obtained her J.D. from the University of New South Wales. Prior to beginning a career in the legal technology industry, Shengshi worked as an actuarial software engineer at Fidelity Investments. She also worked in legal project management at Allens where she developed specialized knowledge in dealing with litigation processes, e-discovery, competition matters and government regulatory notices. Contact Shengshi at shengshi. zhao@nulegal.com.au.