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What is Ediscovery? eDiscovery fits into both of those categories, which makes it the third type of concept/process. Ediscovery. is the understanding of preserving, processing, reviewing, and producing electronically stored information (ESI). I Googled “What is ediscovery?” What is metadata?
Lighthouse said that this acquisition marks its first entry into document review, with the addition of advanced search and analytics technology, and experts who can help clients use these tools to find and classify sensitive data and automate key review workflows.
Learn how adopting new technologies built for big data can help to maximize budgets, optimize resources, and make strategic business decisions. The post Demystifying TAR And Advanced AI To Up-Level Your eDiscovery appeared first on Above the Law.
For at least two decades, artificial intelligence has been used in e-discovery to help surface and prioritize review of potentially responsive documents from large document collections.
Listen to this podcast interview of Michael Quartararo in its entirety here or read the highlights below to get important insights on getting your e-discovery certification. He is also the author of the 2016 book Project Management in ElectronicDiscovery , now in its second edition. Dive into what is e-discovery ?
It is not possible to talk about eDiscovery or document review heading in to 2021 without mention of technology-assistedreview. In its broadest use as a technical term, TAR can refer to virtually any manner of technical assistance. and, more recently, TAR 3.0. The TAR Landscape.
E-discovery company Casepoint is today introducing enhancements to CaseAssist, its AI-based active reviewtechnology, and to its analytics and classification tools, that it says give users more insight and control over the review process, including enhanced visualization capabilities and configuration templates.
Slowly but surely” may be an apt phrase to describe the results from the 2022 ABA Legal Technology Survey Report covering Litigation Technology & E-Discovery. Software for Litigation & E-Discovery. Every Wednesday, we’ll be posting a new report from one of our experts, so stay tuned!
Especially when it comes to technology, many lawyers are reluctant to put their confidence in workflows and solutions that seem to operate in a black box. Technology-assistedreview is a prime example—although the use of predictive coding and other advanced analytics have been widely accepted in the U.S.
At its Relativity Fest user conference in Chicago today, the e-discovery company Relativity announced the forthcoming release of Relativity aiR for Review, the first of a planned series of products that will use generative artificial intelligence to help legal professionals in their work.
This increase in complexity and volume required a different set of professional skills needed for litigation practitioners that combined an intimate knowledge of both the litigation process and how technology can be used to drive efficiencies in the e-discovery process. Automation.
With workplace and regulatory investigations on the rise, leveraging e-discoverytechnologies are no longer exclusively a litigation-specific endeavor. As such, the principles and criteria guiding the management of investigations differ from those guiding a responsive review.
Document review has changed. Attorneys must confront dense forests of data to uncover what matters quickly, and doing so without a little help from AI has become exceedingly difficult.
Legal document review is often viewed as the most expensive part of eDiscovery, and many attorneys believe that there’s no way around it. If you hold that belief too, then I have good news for you: Document review doesn’t have to come with such a high price tag. Inefficient processes tend to add up and increase eDiscovery bills.
Company founder and CEO John Tredennick formerly founded the e-discovery company Catalyst, one of the first cloud-based discovery platforms and one of the first to develop advanced technology-assistedreview. While these claims might seem audacious, they come from a team with a proven track record.
Our latest post dives into the world of artificial intelligence advancements in e-discovery, specifically focusing on the use of LLMs for document review. The experiment had two phases: An initial pass by GPT-4, based on the same instructions provided to the attorney reviewers who performed the original review.
Alex Chatzistamatis, senior principal enterprise architect with Nuix, discusses the ways that technological advancements like machine learning and artificial intelligence are shaping the future of e-discovery and other areas of the legal profession. These days, we call it CAL, continuous active learning.
For example, in my briefing with DISCO, the focus was on expanding the platform from a traditional e-discovery tool into a comprehensive litigation intelligence system. The tool is designed to automate over 80% of review processes and complete them up to 90% faster than TAR.
Legal teams have long used AI for e-discovery in the form of Technology-AssistedReview (TAR) and Continuous Active Learning (CAL). These technologies are leveraged for use cases like auto-coding documents, concept clustering, and near-duplicate identification.
Using a well-established tool like BlackBoiler to redline third party NDAs can significantly reduce the amount of time your team is spending on NDA review, and in time help the wider business self-serve where it makes sense.
CCBJ: ECA is a nebulous term that has been widely used to describe certain early-stage challenges and processes in e-discovery and investigations. It’s a single platform for all ECA and investigation needs and takes users seamlessly to full review, including advanced technology-assistedreview, and production where required.
First, let me say that we’ve come a long way in the discovery process, and this question highlights that fact. When Iinitially got involved with discovery, and really electronicdiscovery (although, from my experience, you can drop the word “electronic,” because it’s all discovery.
Going beyond the more widely used predictive coding solutions for eDiscovery, there are several other AI tools available that can automate and improve different areas of practice. Advanced tools can detect sentiment, communication patterns, and hidden connections in ways that search terms and traditional TAR or CAL workflows may not.
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