e-discovery 2.0

Judge Paul Grimm’s recent opinion in Victor Stanley, Inc. v. Creative Pipe, Inc., 2008 WL 2221841 (D. Md. May 29, 2008) provides valuable guidance on one of the most important issues in e-discovery: how to conduct keyword searches in a defensible manner given that keyword searches are prone to produce over- and under-inclusive results.  The ruling suggests one of two approaches: either producing parties should adopt a “collaborative” approach to conducting keyword searches, whereby each party agrees on a search methodology; or, they should use a “best practices” approach, such as the one suggested by Sedona, where the producing party tests, samples, and iteratively refines searches so that they can demonstrate they have taken reasonable measures to reduce over- and under-inclusive results.

While the guidance is clear, following the guidance in practice is very difficult.  The primary reason for this is that the search technology being used in e-discovery today is not up to the task.  Specifically, today’s search technology suffers from three problems:

1. The over- and under-inclusive tradeoff. Many technologies have been developed to address the tendency of keyword searches to miss relevant documents and produce under-inclusive results.  Wildcard and stemming technology has been developed in order to address the issue of finding common word variations in specified keywords.  Concept search has been designed to find documents containing words with similar meanings to the keywords in a search.  And fuzzy search technologies have been put in place to find misspellings of words. However, all of these suffer from the same problem: they produce too many non-relevant or “false positive” documents thus driving up the cost of review. For example, if someone runs the wildcard search “divers*”, then he or she not only gets the desired documents containing “diverse” and “diversity”, but also gets a large number of false positive documents containing “diversion”, “diversification”, and so on.  In the case of concept and fuzzy search, the problem is so great that these technologies to date have rarely been used in e-discovery.
2. Too expensive to test, sample and refine searches. Today’s search technologies are largely designed to run one search at a time, not the dozens of searches that are typical in e-discovery. As a result, anyone trying to follow the best practices of testing, sampling, and refining each search will find themselves missing deadlines and running over budget because it takes so long. This also makes collaboration with the opposing party close to impossible, since there’s little time to iterate on – and agree upon - a set of keyword searches.
3. Manual documentation. It’s not enough for producing parties to use best practices, they have to document them so that they can “show their work” to the court. Currently, documenting the search refinement process is mostly manual, with the result that it is either done inadequately or not at all.

The reason why the search technology used for e-discovery has these problems is surprisingly simple: it’s because the technology was not designed for e-discovery in the first place. Rather, it was built for enterprise search, and was only later repurposed towards e-discovery.

The “Black Box” Of Enterprise Search

The core issue is that enterprise search technology has been designed to be a “black box”. Users enter a single search query into one end, and get results at the other, with no visibility into what happens in between. Going back to our previous example, when a user searches for “divers*” intending to find documents related to “diversity” or “diverse”, enterprise search engines give the user no visibility into the crucial step of query expansion and how it expands the search query into relevant and non-relevant terms like “diversion” and “diversification”. As a result, the user has no ability to minimize the false positives.

In the same vein, when a user enters multiple queries into a “black box” enterprise search engine, all of the queries run as a single search, and the user has no visibility into which results are associated with which query. For example, a user that searches for “hiring OR interview” will get the results for the combination of the queries “hiring” and “interview”. He or she won’t know that only 5 of documents contained “hiring” while 100 documents contained “interview.”  This limitation makes analyzing, sampling and refining searches costly and time consuming.

That’s not say that enterprise search products like Autonomy or Endeca are flawed. Far from it.  Their “black box” design works exceedingly well for the simple and quick queries that people want to run across the enterprise for general business purposes. If a sales manager is looking for a single proposal for her meeting the following day, then she doesn’t care how the search was performed or if it’s over-inclusive.  She’s only interested in the first page of relevant results, and for that use case enterprise search engines do a great job.

But e-discovery is a whole different world.  In e-discovery, users typically must review every single document in the search results, not just the most relevant ones.  As a result, over-inclusive searches can dramatically increase the costs of downstream production and review.  And under-inclusive searches raise the issue of defensibility.  Finally, e-discovery users have to run a lot of search queries and understand which documents are associated with each of those queries.

So, going back to the original problem, if current search technologies cannot help lawyers and litigation support professionals follow Judge Grimm’s guidance and address the “well-known limitations” of keyword search, what can? That will be the subject of my next post.

Most science fiction visions of the distant future seem to contain a rather singular fear: that the human race will be taken over by computers.  Think “Terminator” series, preferably without the naked Arnold Schwarzenegger visual.  Regardless of whether this vision fills you with trepidation or excitement there is a very real possibility that we’re on the cusp of computers taking over a significant e-discovery task for attorneys.

For past several decades, attorneys have had to manually review information for relevancy and privilege in response to the e-discovery process.  Quoting from Information Inflation: Can the Legal System Adapt? by George Paul and Jason Baron, this task has always been viewed as sacrosanct “because of ‘death penalty’ waiver doctrine that evolved long ago when information was still manageable.”

Like so many industries, the legal profession has attempted to grapple with the transformation that the digital revolution has brought to the forefront.  The latest revisions to the Federal Rule of Civil Procedure (FRCP) is the most obvious case in point.  And yet, electronically stored information (ESI) is proving difficult to fit into traditional, even remodeled, paradigms.  Even ignoring (for the moment) the proliferation of novel data types (i.e., blog content, voice over IP or VOIP, webmail, text messaging, web services, etc.) the amount of data that attorneys are being required to review has reached a tipping point of review feasibility.

Back in the day, information was viewed in terms banker boxes of information, and even in the most document intensive discovery matters this measuring stick belied the belief that armies of attorneys could conceivably conquer the massive document review problem.  But now, we often see clients that process routine matters containing terabytes of information.  Most of us in the e-discovery space have become numbed to the abstract nomenclature of megabytes, gigabytes, terabytesⁱ, petabytesⁱⁱ, and in the process we may have failed to realize that we have moved well beyond the scale of information that can be reasonably attacked with even the largest armada of contract attorneys (assuming that the client could conceivably bear the astronomical costs).

“At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later.”ⁱⁱⁱ

I’m certainly not the first to point out that this tipping point is coming, but now we are really starting to see early adopters respond to this sea change. In their linked article above, George Paul and Jason Baron state “It is no exaggeration to say that litigation, as we have known it, is threatened by information’s new hyper-flow. The amount of electronically stored information relevant to a case is already a stress point in litigation.  […]  Litigators can no longer depend on manual review alone….”

Up until now, attorneys and the clients that are footing the bill have had to make a Hobson’s choice:  either “force parties to continue hugely expensive privilege reviews, or to forego the attorney-client privilege or work-product privilege altogether.”   But, now it appears that another way is evolving.

The following lays out a scenario where a non-manual review methodology may make sense.  ***Please note: this approach is not without risk.  At this moment in time neither clawback provisions, the potential adoption of Evidence Rule 502 nor any other know prophylactic measure can completely insulate a producing party from the unforeseen consequences of an inadvertent disclosure.  But, as they say, desperate times call for desperate measures….

Step one: Evaluate the Environment

The following factors represent some of the elements that should be taken into consideration prior to skipping the normal, human based review steps that are seen in most e-discovery matters.

1. Large data set.  This may sound a bit obvious, but a non-manual approach is best suited for large, unwieldy data sets.  The corpus doesn’t need to be in the terabytes, but the data set should be evaluated in term of discovery processing costs and attorney review estimates.
2. Short Production Timelines.  Once the above calculations are conducted, the next step is to determine if a human based review could even conceivably be conducted in the given time frame.  In many instances, an eyes-on review process just won’t be feasible since there won’t be enough bodies to throw at the problem.
3. Next Gen “PAR” Tools.  In order to pull this “review-less” review process off, both safely and quickly, the responding party needs to have access to fast, robust processing, analysis and review (“PAR”) tools.  Certainly, it’s possible to have this scenario work with an e-discovery service provider, if they have the capability.
4. Relatively Small Amount in Controversy.  For the time being, this approach should not be considered for any “bet the company” litigation, nor anything with significant downside risk (governmental inquiries, punitive damages, class actions, 2nd requests, etc.).  Yet, for many standard commercial lawsuits, corporate investigations, HR claims, etc. this review-less approach may be worth considering.
5. Ability to Use a Clawback Provision.  Entering into a clawback provision with the opposition is mandatory in this methodology since the chances of an inadvertent production are statistically ever-present.  Yet, until Evidence Rule 502 is resolved, there will always be a risk that the clawback won’t be enforceable against 3rd parties.
6. Non-governmental Production.  Most information in governmental productions becomes part of the public record, meaning that a clawback isn’t going to be feasible.  Here, trade secret information, personally identifiably data and the like would be disastrous if pushed out into the public domain.

Step two: Perform a Risk/Benefit Analysis

Next, take all the above factors into consideration and determine if the risks (of inadvertent production, the clawback being ineffective, etc.) are worth the benefits (reduced costs, lower attorney review fees, ability to meet deadlines, etc.).

Sure this is hard work, but the alternative (manual review) is more ephemeral than realistic.

[In my next post, I’ll address the tactical steps to conduct a review-less review process.  Stay tuned……]

ⁱ One terabyte is generally estimated to contain 75 million pages and could conceivably cost $18,750,000 to review.  Anne Kershaw, Automated Document Review Proves Its Reliability, 5 DIGITAL DISCOVERY & E-EVIDENCE 11 (2005).

ⁱⁱ According to Wired, we’re now in the “Petabyte Age” where that amount of data is processed by Google’s servers every 72 minutes.

ⁱⁱⁱ Wired article, above.