Part of the Solution

Idealistic musings about eDiscovery

Category Archives: My $0.02

Why Is eDiscovery So Spooky?

 With Halloween around the corner, let’s try something different. Here’s a little poll, in which I ask why you think attorneys (as a whole) have been reluctant to embrace eDiscovery.

You may choose more than one option (and the more cynical of you may choose to select all of them), but I especially welcome your comments explaining what you think are the root causes of the profession’s ambivalence (including – especially – if you think adoption and acceptance are proceeding at exactly the right pace).

I’ll use the highly-unscientific results as the basis for a future post. Thanks for taking the time to participate!

Advertisements

On TAR 1.0, TAR 2.0, TAR 1.5 and … TAR 3.0?

The problem with technology-assisted review is that the best practices to bring about the most accurate, defensible review are, quite frankly, too onerous for most attorneys to accept.

In “TAR 1.0”, the initial iteration of computer-aided document analysis, as many documents as possible from the total corpus had to be loaded up into the TAR system and, from this nebulous blob of relevant data, non-relevant data, fantasy football updates and cat memes, a statistically-valid sample was drawn at random. It then fell to a senior attorney on the litigation team to manually review and code this “seed set”, after which the computer would identify similarities among documents with similar tags and try to extrapolate those similarities to the entire document corpus.

There are a number of aspects to modern document review that aren’t practical with this scenario – using unculled data to generate the seed set, assuming that you have most of the corpus documents to draw from at the outset – but the most glaring impracticality is also the most critical requirement of TAR 1.0:

Senior attorneys, as a rule, HATE to review documents.

It’s why they hire junior attorneys or contract reviewers. It’s because generally, senior attorneys’ time is better spent on tasks that are more overtly significant to their clients, which in turn justifies them to bill a lot more per hour than the reviewers do. And, if a statistically valid seed set contains some 2,400 randomly selected documents (presuming a confidence score of >95 percent and a margin of error of +/- two percent), that’s the better part of an entire workweek the senior attorney would have to devote to the review.

No wonder TAR 1.0 never caught on. It was designed by technologists – and brilliantly so – but completely ignored the realities of modern law practice.

Now we’re up to “TAR 2.0”, the “continuous active learning” method which has received less attention but is nonetheless a push in the right direction toward legal industry-wide acceptance. In TAR 2.0, the computer constantly re-trains itself and refines its notions of what documents do and do not meet each tag criterion, so that the initial seed set can be smaller and more focused more on documents that are more likely to be responsive, rather than scattershooting randomly across the entire document corpus. As more documents are loaded into the system, the tag criteria can be automatically applied during document processing (meaning that the new documents are classified as they enter the system), and refinements crafted as humans review the newly loaded docs would then in turn be re-applied to the earlier-predicted docs.

Now, that last paragraph makes perfect sense to me. The fact that, despite my editing and revisions, it still would appear confusing to the average non-techie is one of the big problems with TAR 2.0: those of us who work with it get it, but explaining it to those who don’t is a challenge. But the biggest problem I see with TAR 2.0 once again must be laid at the feet of the attorneys.

Specifically, most of the training and re-training in a TAR 2.0 system will come courtesy of the manual document reviewers themselves. Ignoring for a moment the likelihood that review instructions to an outsourced document review bullpen tend to be somewhat less than precise anyway, several reviewers can look at the same document and draw very different conclusions. Let’s say you have a non-practicing JD with a liberal arts background, a former corporate attorney with engineering and IP experience, an inactive plaintiff’s trial lawyer, and a paralegal who was formerly a nurse. Drop the same document – let’s say, a communiqué from an energy trader to a power plant manager – in front of all four, and ask them to tag for relevance, privilege, and relevant issues. You’re likely to get four different results.

Which of these results would a TAR 2.0 system use to refine its predictive capabilities? All of them. And TAR has not yet advanced to the sophistication required to analyze four different tagging responses to the same document and refine from them the single most useful combination of criteria. Instead, it’s more likely to cloud up the computer’s “understanding” of what made this document relevant or not relevant.

The IT industry uses the acronym GIGO: garbage in, garbage out. Blair and Maron proved back in 1985* that human reviewers tend not only to be inaccurate in their review determinations, but that they are also overconfident in their abilities to find sufficient documents that meet their criteria. In TAR 2.0, ultimately, the success or failure of the computer’s ability to accurately tag documents may be in the hands of reviewers whose only stake in the litigation is a paycheck.

Until last week, I was strongly in favor of a “TAR 1.5” approach: start with a smaller seed set reviewed and tagged by a more-senior attorney, let the TAR system make its initial definitions and determinations, use those determinations to cull and prioritize the document corpus, then let the document reviewers take it from there and use “continuous active learning” to further iterate and refine the results. It seemed to me that this combined the best practices from both versions of the process: start with the wisdom and craftsmanship of an experienced litigator and apply it to all the available documents, then leave the document-level detail to contract reviewers using the TAR-suggested predictions as guidance.

But last week, I interviewed with the founders of a small company that have a different approach. Neither desiring to put any pressure on the company nor wanting to inadvertently divulge any trade secrets that might have been shared, I won’t identify them and won’t talk about their processes other than to say that perhaps they’ve come up with a “TAR 3.0” approach: make automatic TAR determinations based on statistical similarity of aspects of the document, rather than on the entire content of each document. It’s a lawyerly, rather than a technical, approach to the TAR problem, which to me is what makes it brilliant (and brilliantly simple).

Whether I become part of this company or not, the people who run it have given me a lot to think about, and I’ll be sharing my thoughts on these new possibilities in the near future.

*David C. Blair & M.E. Maron, An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System, 28 COMMC’NS ACM 289 (1985).

H-P Is Out, iManage Is Back In

On May 15, I was laid off from Hewlett-Packard as they prepared for their big corporate meiosis* in November. I found out in short order that about three-fourths of the remaining eDiscovery experts company-wide were also let go. My private opinion was that this likely signaled HP’s intent to get out of the eDiscovery software business.

Looks like my hunch is at least partially right. My friends at iManage (née Interwoven), formerly a part of Autonomy and later assimilated by HP, have bought their company back.

The press release is here. Former-and-new-CEO Neil Araujo’s first blog post on the buyout is here. Neil writes:

For the iManage leadership, this transaction is about much more than a product: it’s about a community that spans people, partners and hundreds of thousands of users, many of whom have used this solution for more than a decade. iManage also represents a set of values, based on our history of listening, innovating and delivering great products and support. Our buyout enables the team to continue to innovate with a community of thought leaders that share this passion.

My heartiest congratulations to my old colleagues in Chicago. (Hmmm … wonder if they need an eDiscovery expert?)

*After all these years, I finally found a use for that word from high-school biology! HP is splitting into two distinct companies, HP and HP Enterprise, on November 1.

Jack Halprin

We lost Jack Halprin yesterday. Greg Buckles has a great tribute to Jack on his site, but I want to add a couple of words of my own.

When I applied to join Autonomy in 2010, the company was not looking for an eDiscovery expert. Because I presented myself as one, however, the Powers That Were asked their VP of eDiscovery and Compliance – a well-established eDiscovery expert – to evaluate my candidacy. Yep, it was Jack.

Knowing I was from Houston, Jack called his friend Greg to check me out. Fortunately, Greg and I had met socially a few times and the feedback was positive. So positive, in fact, that I was able to collaborate on a couple of projects with Jack before he left for his dream job at Google. I was never privileged to meet Jack in person, but we spent plenty of time on the phone with each other.

Jack died of cancer Thursday morning. He was 46. As Greg wrote:

If you are up to it, raise Jack’s favorite Jägermeister shot in remembrance. If you really do remember what that tastes like from your college days, try a memorial donation to Lymphoma Research Foundation or Larkin Street Youth Services in Jack’s name.
Thanks for your support, Jack. We’ll miss you.

Why Hasn’t TAR Caught On? Look In The Mirror.

Oh, this is good. If you haven’t already signed up for the ALM Network (it’s free, as is most of their content), it’s worth doing so just to read this post (first of a two-part series) from Geoffrey Vance on Legaltech News. It pins the failure of acceptance of technology-assisted review (TAR) right where it belongs: on attorneys who refuse to get with the program.

As I headed home, I asked myself, how is it—in a world in which we rely on predictive technology to book our travel plans, decide which songs to download and even determine who might be the most compatible on a date—that most legal professionals do not use predictive technology in our everyday client-serving lives?

I’ve been to dozens of panel discussions and CLE events specifically focused on using technology to assist and improve the discovery and litigation processes.  How can it possibly be—after what must be millions of hours of talk, including discussions about a next generation of TAR—that we haven’t really even walked the first-generation TAR walk?

Geoffrey asks why attorneys won’t get with the program. In a comment to the post, John Tredennick of Catalyst lays out the somewhat embarrassing answer:

Aside from the fact that it is new (which is tough for our profession), there is the point that TAR 2.0 can cut reviews by 90% or more (TAR 1.0 isn‘t as effective). That means a lot of billable work goes out the window. The legal industry (lawyers and review companies) live and die by the billable hour. When new technology threatens to reduce review billables by a substantial amount, are we surprised that it isn‘t embraced? This technology is driven by the corporate counsel, who are paying the discovery bills. As they catch on, and more systems move toward TAR 2.0 simplicity and flexibility, you will see the practice become standard for every review.

Especially with respect to his last sentence, I hope John is right.

Can You Be a “Salesman” and Still Be Part of the Solution?

I had a job interview by telephone last week. The position’s job posting read as though it had been lifted from my career bucket list; everything I want my career to be, and all the experience I have obtained, meshed perfectly with the contents of the job description.

I knew, however, that there might be more here than meets the eye when, upon initial contact, the reviewer mentioned that in addition to everything listed on the job posting, this would be “a true sales position”. I love to evangelize and identify solutions. I HATE to “sell”.

I thought the interview went fairly well (at least, for purposes of demonstrating my expertise). The interviewer disagreed; he even told me so during the call, saying that he didn’t hear me steering the conversation forcefully enough to specific solutions that could be presented. (Never mind the fact that the list of solutions this company represents is outdated and incomplete on their website, so I wasn’t sure what to recommend. The message was clear: I wasn’t SELLING hard enough.)

This brings me to a recent post on LinkedIn by Damian A. Durrant of Catalyst, entitled “More solving, less ‘selling'”. He believes as I do: don’t sell, SOLVE.

Sales is push, it says I am ramming something, anything, down your throat lubricated with lunch whether you need it or not. Unpleasant. Consulting is pull, it says I believe I have something that will help you, let’s talk about it. Better.

I have been a salesman. I have been a consultant. I much prefer the latter, as I am working to provide solutions. A salesman will make his numbers for the month. A solution provider will be someone the client goes back to again and again, because the provider makes the client’s job easier and less expensive. It’s the difference between making a one-time sale, and building a true relationship.

The e-discovery industry needs to shed itself of its copying and scanning “salesy” origins and start behaving more like the advisory firms, albeit more creatively, more nimbly and without the hefty billing rates.

Nicely said, Damian. Nicely said indeed.

I highly recommend you read his message.

Proportionality in Discovery: Example #243

Courtesy K&L Gates, this recent opinion from USDC California in which the judge points out that you can’t very well conduct discovery with any sense of proportionality if you don’t know what the damages in question are:

[T]he court indicated that Plaintiff’s “tight-lipped” disclosures regarding damages, including indicating its desire for the defendant to wait for Plaintiff’s expert report, were “plainly insufficient.”  The court went on to reason that “[e]ven if [Defendant] were willing to wait to find out what this case is worth—which it is not—the court still needs to know as it resolves the parties’ various discovery-related disputes.  Proportionality is part and parcel of just about every discovery dispute.” (Emphasis added.)

Moral of the story: Modern discovery is not compatible with a plaintiff mindset of “We won’t specify an amount of damages sought, because then we can’t shortchange our potential recovery.”

“Someday You are Bound to Crash and Burn”

Ralph Losey’s e-Discovery Team blog is often highly technical but always interesting. Ralph is one of the (if not the) leading theorist on search and prediction, and he excels at finding simple metaphors to explain his headache-inducing mathematical constructs. (Hey, I was a liberal arts major. I know my intellectual limits.)

In his latest post, Ralph compares Kroll Ontrack’s EDR software to a race car. The far-ranging post is worth a read, if only to get to his final paragraph, of which I agree with every syllable:

What passes as a good faith use of predictive coding by some law firms is a disgrace. Of course, if hide the ball is still your real game of choice, then all of the good software in the world will not make any difference. Keep breaking the law like that and someday you are bound to crash and burn.

Craig Ball, Predictive Coding, and Wordsmithing

Boy, I wish I could write like Craig Ball does.

I have written many articles and blog posts on technology-assisted review, but all my thousands of words cannot communicate my beliefs on the subject as gracefully, powerfully, and concisely as Craig recently put it:

Indeed, there is some cause to believe that the best trained reviewers on the best managed review teams get very close to the performance of technology-assisted review. …

But so what?  Even if you are that good, you can only achieve the same result by reviewing all of the documents in the collection, instead of the 2%-5% of the collection needed to be reviewed using predictive coding.  Thus, even the most inept, ill-managed reviewers cost more than predictive coding; and the best trained and best managed reviewers cost much more than predictive coding.  If human review isn’t better (and it appears to generally be far worse) and predictive coding costs much less and takes less time, where’s the rational argument for human review?

So, um … yeah, what he said.

Oh, THIS Ought To Be Fun …

Via Ralph Losey and his e-Discovery Team blawg comes this surprising opinion out of Delaware Chancery Court: EOHB, Inc., et al v. HOL Holdings, LLC, C.A. No. 7409-VCL (Del. Ch. Oct. 15, 2012). The ruling REQUIRES both parties to this case to use technology-assisted review (i.e., “predictive coding”), even though neither party raised the question; and REQUIRES both to use the same vendor.

Interesting.

From the transcript of Vice Chancellor J. Travis Laster’s ruling in open court:

I would like you all to talk about a single discovery provider that could be used to warehouse both sides’ documents to be your single vendor. Pick one of these wonderful discovery super powers that is able to maintain the integrity of both side’s documents and insure that no one can access the other side’s information. If you cannot agree on a suitable discovery vendor, you can submit names to me and I will pick one for you.

Now, there are activist judges … and then there is Chancellor Laster. But as Ralph observes:

The parties will probably suggest in very polite language that it is none of the court’s business how either side goes about producing their own electronically stored information, much less select a vendor for them. There has been no dispute between the parties to justify this kind of intervention, no allegations of unreasonable search and inadequate production. Unlike the Kleen Products case, where the plaintiff tried to force predictive coding on defendants, there is not even a hint of wrongdoing on either side, much less a suggestion by anone that predictive coding be used. See eg. Kleen Products, LLC, et al. v. Packaging Corp. of Amer., et al.Case: 1:10-cv-05711, Document #412 (ND, Ill., Sept. 28, 2012).

While I definitely believe in the power and cost savings of technology-assisted review, isn’t it a bit beyond the pale for a judge to impose, sua sponte, a requirement of predictive coding where nobody has asked for it and neither party may have the tech-savvy personnel or the budget required to be the guinea pigs for this test case? Plus, if Ralph’s recitation of the case is complete, the chancery judge has missed a key point: Technology-assisted review can’t be used completely in lieu of human reviewers. It must work hand-in-hand with real live attorneys in order for the system to be of any valid use whatsoever.

Please read it for yourself, and respond with your thoughts in the comments.