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In Pyrrho, the High Court approved the use of “predictive coding”
(to facilitate the technology/computer assisted review of documents) in the disclosure process.
While judicial approval was not actually required – and Pyrrho is not the first case in which the technology has been used for e-disclosure in England and Wales – the judgment will serve as a useful guide and precedent to litigants
considering the use of this technology in future cases.
But what exactly is predictive coding and why did the Court approve its use?
Most e-disclosure is currently conducted by searching digital documents for keywords and then manually reviewing those that do. This is inherently time-intensive (and hence expensive).
A particular challenge of this approach is to strike a balance between excluding potentially relevant documents (keywords are too few or too narrow) and wasting time and/or money reviewing too many irrelevant documents (using too many keywords or keywords
that are too wide).
Surfacing potentially relevant documents based on keywords results in a binary output. Either the document contains one or more keywords – or it does not. Actual relevance is not determined until the resultant batch of documents has been manually
By contrast, predictive coding seeks to determine the likely relevance of each document, thus automating much/most of the review process itself.
The central part of the process is known as “machine learning” and essentially involves 3 steps:
A batch of documents is selected to form a “seed set”. The parties are free to agree selection criteria (which may include some use of keywords as identifiers) but the process would work equally well with a randomly generated seed set.
There are no prescribed parameters around what percentage of the total data set should be included. In Pyrrho, the parties estimated th
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