Even the best queries had lower recall with review of 6,000 documents than TAR 3.0 CAL achieved with review of only 3,000 documents, but a few of the queries did achieve higher recall than TAR 3.0 SAL when twice as much document review was performed with the search query compared to TAR 3.0 SAL.
If there is any shortcoming of TAR technologies, the blame may fairly be placed at the feet (and in the minds) of humans.
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.
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.
EDRM and the Bolch Judicial Institute at Duke Law are seeking comments from the bench, bar, and public on a preliminary draft of Technology Assisted Review (TAR) Guidelines.
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.