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.
Technology Assisted Review (TAR) is useful for many tasks within the Electronic Discovery Reference Model (EDRM), with one of its central applications being its use in determining the relevancy of documents in the review stage of eDiscovery in support of document production obligations. To engage in the management of this important workflow, the producing party needs access to TAR software and the decision on what software to use goes hand-in-hand with the service provider selection.
EDRM has released a comprehensive set of guidelines that aim to objectively define and explain technology-assisted review for members of the judiciary and the legal profession. The TAR Guidelines represent the first step in a multifaceted effort to develop a broad understanding of TAR and to encourage its adoption. Under the auspices of the Bolch Judicial Institute and EDRM, a second document, a protocol for when and under what circumstances TAR should be used, is currently being developed by a drafting team of 40 judges, lawyers, and e-discovery experts who attended a 2017 conference focused on TAR best practices, hosted by Duke and EDRM.
Technology-Assisted Review is being used increasingly and, combined with recently-proposed changes to the English disclosure regime, could result in more legal cases becoming economically viable to fight and lead to greater recoveries for creditors.
Contextual diversity refers to documents that are different from the ones already seen and judged by human reviewers. The contextual diversity algorithm identifies documents based on how significant and how different they are from the ones already seen and then selects training documents that are the most representative of those unseen topics for human review.
EDRM and the Bolch Judicial Institute at Duke Law are seeking comments from the bench, bar, and public on a preliminary draft of Technology Assisted Review (TAR) Guidelines.