Organize Your Doc Review Like You Organize Your Netflix Queue… – Electronic Discovery

Feb 3rd, 2012 | By | Category: E-Discovery News

Organize Your Doc Review Like You Organize Your Netflix Queue…

By Michael Bunyi, CCE, RCA, Case Manager

“Predictive Coding” (future link to Recommind’s cease and desist letter), “Technology Assisted Review”, “Categorization”…whatever you want to call it…the new trend in e-discovery is trying to utilize technology that you’re probably already familiar with.

Scientists and mathematicians have devised methods that computer systems can use to think about documents and their content in terms of concepts and meaning instead of just literal content.

(Probably not as familiar as this guy, but this post should get you up to speed.)

Have you ever rated movies in Netflix?

How about Pandora? (I know quite a few attorneys who use this web radio service to help power them through their doc review.)

Pandora, Amazon, Netflix-Computer Scientists generally refer to these sorts of processes as “recommender systems”, “recommender platforms” or “recommender engines”
(The slightest thing can set anyone off after they’ve been engaged in 4 straight hours of doc review.)

And I’m guessing you’ve made a few purchases through Amazon? So then you’re familiar with the wave of recommendations that aren’t particularly relevant because they’re all based off a recent gift purchase.

Dear Amazon,

That Dave Barry book wasn’t for me. It was for my Grandfather. I understand that Mr. Barry’s a prolific writer (and I’m counting on that so I’ll have something to give my Grandfather next Christmas), but please do not send me emails every time Dave Barry publishes something.

Thanks.

Computer Scientists generally refer to these sorts of processes as “recommender systems”, “recommender platforms” or “recommender engines”, and the name says a lot about the intended purpose: they’re designed to make predictions and recommendations about books, movies, music, etc. based on previously stated “preferences” or “ratings”. If you like something, it tries to bring up similar instances of that thing.

So you can probably already see where this is going…the e-discovery world is now applying this same technology to the arduous process of document review (in the hopes of making it less arduous).

How does it work?

It basically works the same way Neflix, Pandora and Amazon work, but instead of assigning a one star rating to “Analyze This” so that you’ll never have to see Robert DeNiro in a comedic role ever again (at least in the world of your Netflix queue…Hollywood still seems to think it’s a good idea), you’re making responsiveness calls on a sample set of data from your review set.

So, as you begin to mark the sample set for responsiveness, the system learns your preferences and then attempts to make judgments about the remaining data set based on these initial responsiveness calls. The larger the sample set, the better informed the system becomes. (It’s like assigning one star to “Meet the Fockers” so that Netflix definitely understands you don’t want to see DeNiro in a comedy while assigning five stars to “Raging Bull”, “Goodfellas” and “Heat” so that the system understands that you still like other movies that DeNiro was in.)

But how does it know which documents are similar?

Good question. This is where we get a little more acquainted with the world of our friend:

Scientists and mathematicians have devised methods that computer systems can use to think about documents and their content in terms of concepts and meaning instead of just literal content.

(Hey! Check out this cool sine wave I drew on my TI-92.)

Scientists and mathematicians have devised methods that computer systems can use to think about documents and their content in terms of concepts and meaning instead of just literal content. For example, you’re probably familiar with employing text searches to find relevant documents. And if you are, you’re also probably familiar with the limitations of this approach: searching for a specific term, or set of terms, will often yield irrelevant data, or it will fail to capture relevant material simply because a relevant document didn’t contain the term, or terms, listed in the search.

Review platforms, like Relativity, have started incorporating this sophisticated technology to allow the user to organize documents around the concepts and meanings expressed within the content of a document. (Explaining how these technologies work would require a much, much longer conversation…). This technology is how the software can then extrapolate from your initial coding set and generate coding “recommendations”/”predictions” for the remaining set of un-reviewed data.

From here, you can devise workflows that will allow you and a review team to focus on the most relevant data set while placing the less relevant material aside for another time.

Ultimately, the purpose of this technology isn’t to completely remove attorneys from the equation, but rather expedite the review process and hopefully save everyone time and money.

Wait! What about the defensibility of all this?

I’ll address that in more detail in a future blog post, but there are signs of support amongst judges and courts for the use of these technologies. One of the clearest endorsements came in an article written by Judge Andrew Peck, United States magistrate judge for the Southern District of New York.

Judge Andrew Peck - “Until there is a judicial opinion approving (or even critiquing) the use of predictive coding, counsel will just have to rely on this article as a sign of judicial approval.”

(“In his free time, Judge Peck is a member of the Baker Street Irregulars and other Sherlock Holmes societies.”)

“Until there is a judicial opinion approving (or even critiquing) the use of predictive coding, counsel will just have to rely on this article as a sign of judicial approval.”

From “Search, Forward: Will manual document review and keyword searches be replaced by computer-assisted coding?”, Law Technology News (October 2011)

So for Judge Peck, when it comes deciding on whether or not to use “predictive coding”, the answer is elementary*…

*I apologize to anyone who was offended by this cheap Sherlock Holmes joke.

Background:

Organize Your Doc Review Like You Organize Your Netflix Queue…
Source: original article
Author: d4admin
Categories: Electronic discovery, e-discovery, ediscovery

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