Redaction? There’s an App for That..

Raise your hand if you've either participated in or managed a group of contract attorneys sitting in rows of cubicles (or "stations") redacting documents for production. Whether your tool of choice was a black marker or a cursor, I'm betting it was a thankless, tedious, pain-staking job and you hated it. Wouldn’t it have been nice if you could've taught the computer how to recognize the patterns of PII (private identification information) and have it make the redactions itself?

Well, guess what, Valora heard your anguished cries and we've built an AutoRedaction engine that rivals any group of manual redactors. With blazing speeds, impressive accuracy and astounding savings, our PowerHouse^TM system literally autoredacts paper and ESI documents in seconds.

Capitalizing on our extensive experience with pattern-matching technology^[1], Valora has built a custom software program that automatically determines the presence of sensitive PII, confidential or privileged information, and then redacts out that information on the image. AutoRedaction takes the form of a black block, with or without a representative stamp, such as "Redacted" or "Employee 123." Redactions can be made permanent, such as for production purposes, or kept temporary, with a technique for "lift and peek," when desired. Redactions can also be made to the underlying text, or on both text and image, if desired.

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^[1] For more on Probabilistic Hierarchical Context-Free Grammars, see this link on Google Scholar.

Statistical Pattern Matching Accurately Predicts Presidential Winners and Electoral College Counts, Why Not Privilege and Responsiveness in Litigation?

The technology utilized by political statisticians is finally getting the attention it deserves.  Not because it is partisan, but because it is accurate.  The excellent article in today’s LA Times explains how mathematical models predicted the election outcome well before the first polls had opened. How? By taking the information from numerous sample sets and re-modeling over and over again with different assumptions and weightings. If this sounds a lot like statistical sampling and pattern-matching, then you have been paying attention! The techniques used by the Nate Silvers of the world to classify and label voting patterns are being used right now in litigation to “predict” (or diagnose, if you prefer) for privilege, responsiveness and issues.

At Valora, we call this technique Probabilistic Hierarchical Context-Free Grammars, but others have shortened it to Statistical Pattern Matching, which works just fine. The point is that information about documents (or voter behavior or music choices) has been available for a long time. The only missing piece is the human comfort level with statistics and probabilistic systems.

If the statisticians can call elections, baseball winners and consumer preferences, isn’t it time we let them loose onto document analysis and review? If you’d like a primer on or a demonstration of Probabilistic Hierarchical Context-Free Grammars in litigation, contact us at valoratech.com.

Valora Technologies CEO, Sandra Serkes, Responds to Craig Ball’s LTN Article on “Next Level” Technology Assisted Review

Original article: Imagining the Evidence

I am pleased to inform both Mr. Ball and the world that the “next level” of TAR, meaning the use of whole documents and populations, rather than selected seed sets, is already here and doing fine.  Rules-Based approaches to TAR are not constrained by the need to create and perfect the selection of a seed set.  Instead, they apply their algorithms and iterations across the entire population, at once, each time.  There is no need for any exemplar document, as the exemplar is the rule itself – thus any document can be evaluated for its “exemplary-ness” and to what degree, where and when.

Furthermore, Mr. Ball discusses the thorny issue of self-interested collection and seed set tagging.  He suggests the opposing party should be the one to set the seed set tags into motion.  This is a step in the right direction.  But, the best approach would be to have both producing and opposing working together to determine relevance – an option easily afforded by a Rules-Based approach.  Rather than having any one party have to sit down and hand-craft a seed set, both sides can agree on the RULES of responsiveness, rather than on whether this document or that one is the better exemplar.  With agreed-upon rules in place, documents are easily assessed not just for yes/no relevance, but also to what degree.

Finally, the notion of “imagining” the documents is very much alive and well in the field of Data Visualization.  We often use this technique in a descriptive way (here’s what your data shows), but it can also very much be used in a proscriptive way (is there anything that looks like this?  How close?).  This concept is very much connected to the current practice of iterating for performance optimization (aka: trading off precision and recall).  TAR systems that utilize the notion of DocType or Attribute templates already have the concept of a “generic” or “iconic” version, essentially an exemplar.  It is trivial to create more templates and use them in a hierarchical manner to test how much a potential document matches the generic exemplars, by relevance priority.

Valora Technologies CEO, Sandra Serkes, Invited to Speak to ILTA South Pacific Region About Technology Assisted Review

ILTA Program to Cover "Exploring Predictive Coding and Technology Assisted Review: Valora Technologies' Approach"

July 25, 2012, 6:00pm EST

During her presentation, Ms. Serkes will layout the Technology-Assisted review (TAR) landscape and Valora Technologies' overall approach.  With Document Review the most costly phase of ediscovery, there are considerable savings to be achieved when it's possible to automate some or most of that review process in a reliable and defensible manner.  This theory is the motivation behind the hype about predictive coding and TAR. Unlike most of the solutions on offer in this market space, Valora Technologies' approach is Rule-Based and transparent.  Come learn about the developing TAR landscape and one provider's unique vision for this space.

Remote & physical presentation sign-up now open.

3 Drawbacks To Predictive Coding

Valora’s Response to LTN article: Take Two: Reactions to 'Da Silva Moore' Predictive Coding Order

What is missing there, and elsewhere, is a discussion of the specific weaknesses of the overall Predictive Coding technique.  Here are just three drawbacks of the technique: 

1. PC tagging algorithms are not transparent.  No one really knows why the PC engine "chose" the documents it did.  Typically, the “choosing” algorithm is hidden and not disclosed.  All we know is that somehow the document recognized is a lot like another tagged.  
2. PC has no checks or balances on the skill set, education, consistency or motivations of the seed set coder(s).  The entire Predictive Coding approach assumes that the seed set coder(s) know what they are doing, and that they are correct, consistent and honest.  Would you defend that position, particularly given that the “human being as gold standard" concept has been roundly deflated (see Blair & Maron, Grossman, TREC, etc.)?
3. Typically, seed set creation and audit sampling for PC use a random sampling technique, the weakest of all types. 

Other sampling techniques (stratified, cluster, panel, etc.) are aware of document attributes and utilize intelligent groupings to create a much stronger, more representative sample for seed set coding and auditing purposes.

Since at present, all Predictive Coding solutions are products, which means they have limited functionality and flexibility for specific case matters, perhaps we should be thinking about the broader picture of Technology-Assisted Review (TAR) as a service – customizable, measurable and transparent.