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Idealistic musings about eDiscovery
With all the views the eDiscovery acceptance poll in my last post has received, only eight votes (and one comment) have been counted. Since people can vote multiple times, I suspect only two or three people have offered their opinions. (I don’t expect a statistically-valid data set, but c’mon …)
I’m posting this one more time; please share your thoughts. Meanwhile, I’ve got an essay in the works regarding Ralph Losey’s magnum opus regarding Predictive Coding 3.0. (I’m reading all 15,000 words so you don’t have to!)
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!
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).