As defined in The Grossman-Cormack Glossary of Technology-Assisted Review(1), Predictive Coding is an industry-specific term generally used to describe a technology-assisted review process involving the use of a machine learning algorithm to distinguish relevant from non-relevant documents, based on a subject matter expert’s coding of a training set of documents. This definition provides a baseline description that identifies one particular function that a general set of commonly accepted machine learning algorithms may used for in technology-assisted review.
With the growing awareness and use of the technology-assisted review feature of predictive coding in the legal arena today, it appears that it is increasingly more important for electronic discovery professionals to have a general understanding of the algorithm approaches that may be implemented in electronic discovery platforms to facilitate predictive coding of electronically stored information. This general understanding is important as each potential algorithmic approach has efficiency advantages and disadvantages that my impact the efficacy of predictive coding.
To help in developing this general understanding of potential predictive coding algorithms and to provide an opportunity for electronic discovery providers to share the approaches they use in their platforms to accomplish predictive coding, the following working list of predictive coding technologies and corresponding one-question provider implementation survey are provided for your consideration and use.
Note: The running results of a previously presented general survey on eDiscovery provider use of predictive coding are available for review(2) (click here for survey results). The initial 120-second survey(3) (click here for initial survey form) contained six high level questions related to technology development, offering integration, machine learning approach and sampling approach of providers in relation to predictive coding. The following working list and one-question provider implementation survey are designed to build on the machine learning question from the initial general survey by providing additional and important layers of detail.
Courtesy of industry search expert Herb Roitblat, provided below is a working list of identified machine learning approaches that have been applied or have the potential to be applied to the discipline of eDiscovery to facilitate predictive coding. This working list is designed to provide a reference point for identified predictive coding technologies and may over time include additions, adjustments and/or amendments based on feedback from experts and organizations applying and implementing these technologies in their specific eDiscovery platforms.
Listed in Alphabetical Order
Click here to provide specific additions, corrections and/or updates.
Provided below is a simple one-question survey designed to help electronic discovery professionals identify the specific machine learning approaches used by eDiscovery providers in delivering the technology-assisted review feature of predictive coding. This one-question survey is a detailed follow-up to the provider-centric 120-Second Survey on predictive coding initiated earlier this year.
Representatives of leading eDiscovery providers(4) are encouraged to complete the short one-question survey on behalf of their organizations. Results of survey (excluding responder contact information) will be aggregated and published on the ComplexDiscovery website for usage by the eDiscovery community. (Click here for an example of responders and results from the previously initiated general survey on predictive coding.)
(1) The Grossman-Cormack Glossary of Technology-Assisted Review (2013 Fed. Cts.L. Rev. 7) by Maura Grossman and Gordan Cormack. EDRM.
(2) Predictive Discovery? Initial Results of 120-Second Provider Predictive Coding Survey (February 2013), ComplexDiscovery.
(3) Predict Coding and Providers: A 120-Second Survey (February 2013), ComplexDiscovery.
(4) Got Technology-Assisted Review? A Short List of Providers and Terms (January 2013), ComplexDiscovery.
Current Responders and Results available at: http://bit.ly/pc-one-question-results.