With the increased focus within the discipline of eDiscovery on Technology-Assisted Review, three references are provided to help legal professionals establish a solid base of definitional and contextual information for considering machine learning.
Reference #1: Book: New Advances in Machine Learning. Chapter: Types of Machine Learning Algorithms. Author: Taiwo Oldipupo Ayodele (University of Portsmouth, United Kingdom).
Reference #2: Video: Lectures on Machine Learning. Lecturer: Andrew Nq (Director , Stanford Artificial Intelligence Lab, Stanford University).
Available via Video Series Link: https://class.coursera.org/ml/lecture/preview
Reference #3: Video Lectures on Machine Learning. Lecturer: Pedros Domingos (Professor of Computer Science & Engineering, University of Washington.
Available via Video Series Link: https://class.coursera.org/machlearning-001/lecture/preview
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Since its 2007 introduction, kCura’s Relativity product has become one of the world’s leading attorney review platforms. One of the elements of Relativity’s strong growth and marketplace acceptance has been kCura’s focus on and support of partnerships. Provided as a by-product of review platform research and presented in the form of a simple and sortable table is an aggregation of kCura Premium Hosting Partners and Consulting Partners.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for both the software and service areas of the electronic discovery market for the years between 2013 and 2018.
“So what’s the big deal?” I asked Mark Noel, one of our senior Predict consultants (and much smarter than me about this stuff). “Moving from one document in 100 to seven doesn’t seem like much of an improvement,” I added. “Why couldn’t we get these numbers up to 35% or, heck, even higher to 60% or more?”
Discovery, as all lawyers know, is the process of collecting and exchanging information about the court case to prepare for the trial. Traditionally, this was done by many lawyers over countless billable hours in which every page of potential evidence was examined for important information. Because of this, the more information existed in reference to a case, the more expensive the case was.
The consensus view is that after the purchase Microsoft will essentially disband Equivio and absorb its technology, its software designs, and some of its experts. Then, as Craig Ball predicts, they will wander the halls of Redmond like the great cynic Diogenes. No one seems to think that Microsoft will continue Equivio’s business.
In my previous post, I found that relevance and uncertainty selection needed similar numbers of document relevance assessments to achieve a given level of recall. I summarized this by saying the two methods had similar cost. The number of documents assessed, however, is only a very approximate measure of the cost of a review process, and richer cost models might lead to a different conclusion.
One distinction that is sometimes made is between the cost of training a document, and the cost of reviewing it. It is often assumed that training is performed by a subject-matter expert, whereas review is done by more junior reviewers. The subject-matter expert costs more than the junior reviewers—let’s say, five times as much. Therefore, assessing a document for relevance during training will cost more than doing so during review.
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The Actionable Intelligence (@ActionableINT) Weekly "Quick 10" Corporate Risk Review provides in-house counsel with a weekly overview of ten significant...