Herb Roitblat continues to argue that how you cooked your eDiscovery turkey in the laboratory may not be a good indicator of its taste or wholesomeness when served from your kitchen.
Based on a website review of this year’s Inc. 5000, the following list provides a quick, non-all inclusive reference of some of the eDiscovery enablers that have been included in the 2014 list. The sortable list includes the provider’s name, 2014 Inc. 5000 ranking (#), three year revenue growth (%), 2013 revenue ($) and industry categorization.
Grossman and Cormack argue, attorneys should rely on scientific studies of the efficacy of CAR/TAR systems based on an analogy to the Daubert standard. They argue that evaluating the success of eDiscovery is burdensome and can be misleading. They liken the process of eDiscovery to that of roasting a turkey.
A random only search method for predictive coding training documents is ineffective. The same applies to any other training method if it is applied to the exclusion of all others. Any experienced searcher knows this.
Terms like Predictive Coding or Machine Learning can involve an infinite number of combinations of technologies, sampling strategies, training iterations and much more.
Data transfer risk may be minimized by automation and standards or increased by the requirement of human intervention. As automation and standards are still slowly maturing in the realm of electronic discovery technology, it seems important that legal professionals understand and properly consider the impact of potential data transfer risk as they plan, source, and conduct their electronic discovery activities.
International Legal Technology Association: “Released in May 2014, Legal Technology Future Horizons (LTFH) is a report that provides insights and practical ideas to inform the development of future business and IT strategies for law firms, law departments and legal technology vendors. The research, analysis and interpretation of the findings were undertaken by Fast Future Research and led by Rohit Talwar.
The hype cycle around Predictive Coding/Technology Assisted Review (PC/TAR) has focused around court acceptance and actual review cost savings. The last couple weeks have seen a bit of blogging kerfuffle over the conclusions, methods and implications of the new study by Gordon Cormack and Maura Grossman, “Evaluation of Machine-Learning Protocols for Technology-Assisted-Review in Electronic Discovery”. Pioneering analytics guru Herbert L. Roitblat of OrcaTec has published two blogs (first and second links) critical of the study and its conclusions. As much as I love a spirited debate and have my own history of ‘speaking truth’ in the public forum, I can’t help wondering if this tussle over Continuous Active Learning (CAL) vs. Simple Active Learning (SAL) has lost view of the forest while looking for the tallest tree in it.
Recent case law has shown strong support for the use of technology-assisted review (TAR): its accuracy and efficiency have been praised by judges and parties alike. However, despite her approval of the process as a “far more accurate means of producing responsive ESI in discovery … than human review or keyword searches,” Magistrate Judge Peggy A. Leen rejected the plaintiff’s use of the tool in Progressive Casualty Insurance v. Delaney after many months of delay in the production of requested electronic evidence.
By Herbert L. Roitblat, Ph.D. Let’s face it. “Active learning,” where the computer picks the training examples sounds cooler than “passive learning,” where the training examples are chosen randomly. Who wants to think that they are passively sitting by when they can be actively going out and finding responsive documents? But when you get past the feel-good aspects of the name, there are some real advantages to a system based on “passive” random sampling. Predictive coding uses machine learning algorithms to construct computational criteria for separating responsive from non-responsive documents. There are many protocols and algorithms that can be […]