In January we published a tongue in cheek bingo card before Legalweek that poked fun of many of the words and phrases used by eDiscovery software and services providers, like “voluminous data” and “explosion of data.”
It was a silly thing, but it resonated because the terms are all ubiquitous.
The most viral story in the legal industry in 2021 was the attorney who assured a judge that he was not, in fact, a cat.
The year 2023 may have provided this definitely human attorney’s most potent challenger in the form of the Mata v. Avianca. Plaintiff’s counsel utilized ChatGPT to draft a court filed document, ChatGPT “hallucinated” and provided several fictitious case citations. After attempting to cover up the use of Chat GPT, the attorney was sanctioned, and the case directly led to...
As legal technology has evolved from linear review to TAR to CAL to Generative AI, we've seen many other key developments. Starting in the “old days” of eDiscovery – a.k.a. 2006 – with TAR 1.0, attorneys once had to rely solely on a subject matter expert (SME) who would code a seed set of documents that ranged anywhere from 1,000 to 5,000 depending on the consistency of choices the SME made during the review.
Technology-assisted review (TAR) has more than a decade under its belt, so while it’s not cutting-edge in today's AI-driven legal landscape, its impact on eDiscovery is undeniable. TAR paved the way for the more advanced solutions we see today and understanding this journey informs us how eDiscovery evolved along with it.
Dear reader,
Artificial Intelligence (AI) has been the shiny object in the legal space for the past 12 months or so. If you are like me, it’s all you have been seeing on legal website headlines and across LinkedIn.
Litigators and eDiscovery practitioners often face a common document review challenge when preparing for litigation – too many documents (and too little time to get through them). Linear review, that is, reviewing every document individually, is the most appropriate option in certain situations, but can often be complemented by predictive coding. This is a defensible way to create massive time and cost savings, preserving budget for merits counsel, not for eDiscovery and document review partners
