The Predictive Coding Technologies and Protocols Survey is a non-scientific semi-annual survey designed to help provide a general understanding of the current application of predictive coding technologies, protocols, workflows, and uses by data discovery and legal discovery professionals.
The basic idea behind topic modeling, according to eDiscovery expert and author Herbert Roitblat, is that documents consist of words that are derived from some mixture of topics. The goal of eDiscovery, argues the author, is to get the information contained in a collection of documents, not to get the documents themselves. The documents are just a means to reach the information.
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, protocols, and workflows by data discovery and legal discovery professionals within the eDiscovery ecosystem.
Developed from the aggregate results of three administrations of the semi-annual Predictive Coding Technologies and Protocols Survey from ComplexDiscovery, the following overview of survey informational request responses regarding primary predictive coding platforms is provided with the hope that the results may be helpful for eDiscovery professionals as they consider predictive coding.
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, protocols, and workflows by data discovery and legal discovery professionals within the eDiscovery ecosystem.
The Predictive Coding Technologies and Protocols Survey is a non-scientific semi-annual survey designed to help provide a general understanding of the current application of predictive coding technologies, protocols, workflows, and uses by data discovery and legal discovery professionals.
As your opinion is essential in helping form a complete picture of the interest and impact of predictive coding in eDiscovery, please do take the time to complete this short, anonymized survey, as the results will help legal, business, and technology professionals in the eDiscovery ecosystem better understand the current use of predictive coding.
Even with a large pool of participants, ample time, and the ability to hone search queries based on instant feedback, nobody was able to generate a better production than Technology-Assisted Review (TAR) when the same amount of review effort was expended. It seems fair to say that keyword search often requires twice as much document review to achieve a production that is as good as what you would get TAR.
A Nearest Neighbor Search is perhaps the simplest procedure you might conceive of if presented with a machine-learning-type problem while under the influence of some sort of generalized “veil of ignorance”. Though there exist slightly more complicated variations in the algorithm, the basic principle of all of them is effectively the same.
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, protocols, and workflows by data discovery and legal discovery professionals within the eDiscovery ecosystem.
The Predictive Coding Technologies and Protocols Survey is a non-scientific semi-annual survey designed to help provide a general understanding of the current application of predictive coding technologies, protocols, workflows, and uses by data discovery and legal discovery professionals.
As your opinion is essential in helping form a complete picture of the interest and impact of predictive coding in eDiscovery, please do take the time to complete this short, anonymized survey, as the results will help legal, business, and technology professionals in the eDiscovery ecosystem better understand the current use of predictive coding.
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