United gave the passengers more than 3.5 million “core” documents during discovery. But only about 600,000—17 percent— of the documents were responsive to the passengers’ discovery requests.
The Predictive Coding Technologies and Protocols Survey is a non-scientific survey designed to help provide a general understanding of the use of predictive coding technologies and protocols from data discovery and legal discovery professionals within the eDiscovery ecosystem.
Technology-Assisted Review is being used increasingly and, combined with recently-proposed changes to the English disclosure regime, could result in more legal cases becoming economically viable to fight and lead to greater recoveries for creditors.
During this iteration of the TAR vs. Keyword Search Challenge held at the Education Hub at ILTACON 2018, TAR won across the board, as in previous iterations of the challenge.
With the growing awareness and use of predictive coding in the legal arena today, it appears that it is increasingly more important for electronic discovery professionals to have a general understanding of the technologies that may be implemented in electronic discovery platforms to facilitate predictive coding of electronically stored information.
Are lawyers who use platforms lacking a simple tweak of a bad algorithm committing malpractice by doing so?
Contextual diversity refers to documents that are different from the ones already seen and judged by human reviewers. The contextual diversity algorithm identifies documents based on how significant and how different they are from the ones already seen and then selects training documents that are the most representative of those unseen topics for human review.
TAR is not meant to replace standard review processes and protocols, but instead to help streamline those processes so that review can be more targeted, fruitful and efficient.
Technology-Assisted Review (TAR) is a concept-based method of document coding that leverages machine-learning techniques with the input of human reviewers to automate the review process.
During my [Bill Dimm] presentation at the NorCal eDiscovery & IG Retreat, I challenged the audience to create keyword searches that would work better than technology-assisted review (predictive coding) for two topics. Half of the room was tasked with finding articles about biology (science-oriented articles, excluding medical treatment) and the other half searched for articles about current law (excluding proposed laws or politics). TAR beat keyword search across the board for both tasks.