Relativity Fest 2017 Review – Part II
Rise of the Machine

Part I was of the R Fest 2017 review – "The Popsicle Moment" - can be found here.

Introduction

History in this industry teaches us that we can’t place all our eggs in a single basket.  As noted below, been there and still have the T-Shirt to remind me.  After all, iControl ESI has been stepping out on our own technology for years.  We are agnostic.  I didn’t attend R Fest simply because we have clients that love Relativity.  No, I attended primarily because of history.  I personally made a big mistake in 2008, as did many.  I try not to make the same mistake twice.

Relativity Fest 2017 Review – Part I

The Popsicle Moment

This is the first in a two-part review of Relativity Fest 2017.  Part two is titled – Relativity Fest 2017 Review – Part II – The Rise of The Machine.  Subscribe to ESI Gladiator to receive notification when it's published tomorrow.

Introduction

The verdict - TWO THUMBS UP!

This was my very first Relativity Fest!  That’s right, I went kicking and screaming into the den of the dragon I have somehow avoided all these years.  Avoidance of Relativity has not been by accident.  Like other old industry war horses, I’ve used virtually every “eDiscovery” application that’s hit the market over the past 25 years, but not Relativity.  Not until past few years.  I am no different than the average consumer – I have my favorites.  My favorite historically has not been Relativity, for reasons that are unimportant here.

Like many service providers in the industry these days, the company I work for offers clients options.  A few years back, a client pulled us into both Eclipse AND Relativity.  That law firm client has an even mix of Relativity and Eclipse cases.  Even though we have our own review software, Recenseo, and highly advanced “predictive analytics” application, Envize, we had to bring in the technology the customer wants.   Of course, we believe our technology is just as good as those other guys.  Yet, we now support substantial matters in all three review tools.  Clients have choices.  We are not alone; many providers now offer multiple options.  FTI, one of the largest companies in our space, recently announced that it has rolled out Relativity and spoke about why during the keynote speech at R Fest 2017.  Yes, that’s the same FTI that bought both Attenex and Ringtail.  More on that in Part II. 

We are rapidly reaching the point where you can get a wide variety of technology on any street corner.  So how does a consumer differentiate between “providers”?   Service – create those Popsicle Moments.  What the heck is a Popsicle Moment?  Read on.

Do You Agree to Untested Search Terms?

Introduction

Unless you are using a proven key term validation methodology that utilizes sampling, you are almost certain to agree to terms that are likely overly broad. Obviously, agreeing to terms that are overly broad is costly, but common.  The greater risk - what are you missing?  

Too many declare that search terms "look good" without the appropriate testing.

ePizza as a Service – Cloud Explained (OP, IaaS, PaaS and SaaS)

By Mark G. Walker, VP Advisory Services and Susan Kavanagh, Senior Advisor -iControl ESI

Introduction

We recently came across again this LinkedIn post from 2014 by Albert Barron, a software architect at IBM. His analogy does a great job of explaining the various “… as a service” models using everyone’s food favorite – PIZZA!!!. We are taking a few liberties with Albert’s original version and hope he doesn’t mind. 

Top Adviser warns of out-of-control autonomous robot lawyers

Mark Walker, an eDiscovery adviser, author and frequent commentator, advocated Wednesday for "creating rules that govern how we use Artificial Intelligence (AI) with legal technology lest we unleash on humanity a set of autonomous robot lawyers that we don't know how to control.”

 “I’ve spent my entire adult life serving lawyers. Non-lawyers like myself have a hard-enough time now getting the human kind to listen", Walker stated.

Is AI Replacing Lawyers and their Staff?

By Mark G. Walker, VP Advisory Services and Susan Kavanagh, Senior Advisor -iControl ESI

Introduction
Don’t shoot the messenger! You can’t make this stuff up. Or can you? There have been several articles recently suggesting that Artificial Intelligence (AI) is creeping into the legal arena and that “Lawyers could be the next profession to be replaced by computers”. That’s literally the title of a recent CNBC “Future of Work” article.

“The legal profession — tradition-bound and labor-heavy — is on the cusp of a transformation in which artificial-intelligence platforms dramatically affect how legal work gets done.”

How do I prove that TAR makes sense?

By Mark Walker, VP Advisory Services at iControl ESI

Please post a response with your thoughts, especially if you disagree with any of this, or I get anything wrong. This post is intended to prompt discussion on this topic.

Introduction

When does it make sense to use a TAR workflow? Those of us that work with predictive analytics (a/k/a predictive coding) and TAR workflows have been asked this question more times than we can count. The answer is usually the unpopular “it depends” response. At the end of the day, there should be a cost vs. benefit math exercise. However, non-monetary factors can also impact the decision. The time allotted to get the production out the door, resources available, and the budget are also factors. Even case strategy can factor into the equation. There are a lot of variables. Virtually everyone agrees that in most cases we simply cannot review everything. Most just resort to using date, file type and search term filters. We can do better.

Those of us that have been using TAR workflows for years know that a well-planned TAR workflow using machine learning (preferably active learning) will save both time and money. We know that using the right technology is highly accurate when based upon sound sampling methods where humans teach the technology to find what they seek. But how do we prove it to someone who has never traveled that road? Lawyers are all about proof. That’s what they do. We have a tough audience.. 

Defining the Problem

A few weeks ago, I reconnected with a LitSupport manager at a major law firm. He has been in the industry a very long time and closely follows the most cutting-edge technology. As a LitSup manager, he has had success convincing lawyers within his firm to use TAR workflows. Well, some of them. This time, I asked him the dreaded question, but in a slightly difference way – “What kind of cases should your lawyers consider using predictive analytics.” His answer, tongue in cheek: “Every Case!” We both got a good chuckle out of that answer. While we chuckled, he is exactly right. But, like everyone else in the industry, he is also frustrated with the industry as a whole’s inability to make the argument in a way that resonates with lawyers. Some use fear to convince – if you don’t do it, others will. Lawyers like litmus tests. Bright lines. They don’t like grey. Lawyers don’t react well to threats and attempts to invoke fear.

When reviewing documents, lawyers want documents that are relevant. Sure, good lawyers are concerned about cost and one would think would be interested in anything that will make them more efficient. But, they are also concerned about risk and trust.

Here’s the root of the problem: Relevancy rates in collected documents are often as low as 1% in most cases. That means 99 out of every 100 documents collected have no value. Sure, there are exceptions, but it is rare that a document review relevancy rate is above 50% using traditional search and review work flows. No matter how you cut it, when 50% of what you review (best case) is wasted effort, there is an expensive problem that needs to be solved. By the way, a search and review workflow that achieves a 50% reduction and relevancy rate is a phenomenal achievement. We traditionally see closer to 30% without leveraging a TAR workflow. We can do better! We must get as close to the 1% we seek as possible.

Using a document count litmus test to determine whether to use predictive analytics doesn’t work. For example, “use predictive analytics when you have 10,000 documents to review”. The average single custodian (witness) has on average 10,000 documents collected. If 1% of that is what we expect to be relevant, then out of 10,000 documents your seeking 100 that are relevant. There are too many other factors that might make it more cost effective to just review the 10,000 documents. Document count is not the right litmus test. 

Solving the Problem - Do the math

Using our 10,000 document, single custodian example, we arrive at a conservative 50% relevancy rate litmus test. That is, if you expect that whatever method you use to filter down before review will yield less than a 50% relevancy rate during review, then it makes sense for you to deploy TRUSTED predictive analytics technology to your review, often in conjunction with validating search terms to exchange with the opposition. See Combining Search and Predictive Coding in 5 Easy Steps. While you can’t really know for certain what the actual relevancy rate will be up front, obviously, we can usually have a pretty good idea if it’s going to be above 50%.

In our 10,000-document example using a traditional filter, search and review methods, one might cut the review in half and only review 5,000 documents. At a billing rate of $250 per hour, and a typical review rate of 55 docs per hour, the cost to review 5,000 documents is $22,727.27. $250 an hour is low compared to the market rate for associates. Make your estimates conservative.

If predictive analytics rate is $0.06 per document, the cost to classify with predictive analytics the 10,000 documents available for review is $600. All other technology costs such as processing and hosting will be incurred no matter what review method you chose.

Leveraging predictive analytics, you should typically see an 80% or above relevancy rate during review. If you only achieve 50% using traditional search and review, then spending $600 on analytics achieves at least 30% improvement, which is very conservative. Therefore, in this very conservative example you reduce the review by 1,500 documents and avoid 27.7 hours of review time. At $250 per hour, that’s $6,818.18 of review cost avoided. Since the analytics cost just $600, the net savings is $6,218.18. How can anyone ignore that advantage?

Ah, naysayers might say, we are going to use contract reviewers at $55 per hour! Even with the dramatically reduced billing rate, there is still a net savings of $900, and don’t discount speed either. 

Predictive Analytics is not just for Big cases anymore.

In the example above, we’ve used a very small case - a 10,000 document case hosted in a review platform is, well, rare these days. Many of the cases we deal with are multi-million document cases. 100,000 hosted is common. Using the same modeling as outlined above, the savings achieved on a 100,000-document population is persuasive and undeniable.

At a $250 per hour review rate

At a $55 per hour review rate

Conclusion

With very few exceptions, leveraging a TAR workflow that includes predictive analytics (a/k/a predictive coding) will save considerable time and money. The courts have been encouraging lawyers to leverage technology. Clients are demanding their outside counsel reduce costs.  Fixed fee arrangements are becoming common place where lawyers have skin in the game to keep the time they spend on matters low. For contingent fee lawyers, time really is money.

Do the math yourself. Apply whatever assumptions you feel appropriate. Increase document decisions per hour, lower hourly rates, increase the per doc cost of analytics. What you will find is that using even the most extreme and efficient methodology, leveraging predictive analytics simply makes financial sense for everyone involved. Reach out to me and I’ll provide you with a calculator so you can input your own assumptions.

So, what’s keeping you from leveraging predictive analytics? Inquiring minds want to know.