There is a new article by Gordon Cormack and Maura Grossman that stands out as one of their best and most accessible. It is called Continuous Active Learning for TAR (Practical Law, April/May 2016). The purpose of this blog is to get you to read the full article by enticing you with some of the information and knowledge it contains. But before we go into the five reasons, we will examine the purpose of the article, which aligns with our own, and touch on the differences between their trademarked TAR CAL method and our CAR Hybrid Multimodal method. Both of our methods use continuous, active learning, the acronym for which, CAL, they now claim as a Trademark. Since they clearly did invent the acronym, CAL, we for one will stop using it – CAL – as a generic term.
The Legal Profession’s Remarkable Slow Adoption of Predictive Coding
The article begins with the undeniable point of the remarkably slow adoption of TAR by the legal profession, in their words:
Adoption of TAR has been remarkably slow, considering the amount of attention these offerings have received since the publication of the first federal opinion approving TAR use (see Da Silva Moore v. Publicis Groupe, 287 F.R.D. 182 (S.D.N.Y. 2012)).
I remember getting that landmark ruling in our Da Silva Moore case, a ruling that pissed off plaintiffs’ counsel, because, despite what you may have heard to the contrary, they were strenuously opposed to predictive coding. Like most other lawyers at the time who were advocating for advanced legal search technologies, I thought Da Silva would open the flood gates, that it would encourage attorneys to begin using the then new technology in droves. In fact, all it did was encourage the Bench, but not the Bar. Judge Peck’s more recent ruling on the topic contains a good summary of the law. Rio Tinto PLC v. Vale S.A., 306 F.R.D. 125 (S.D.N.Y. 2015). There were a flood of judicial rulings approving predictive coding all around the country, and lately, around the world. See Eg. Pyrrho Investments v MWB Property, EWHC 256 (Ch) (2/26/16).
The rulings were followed in private arbitration too. For instance, I used the Da Silva More ruling a few weeks after it was published to obtain what was apparently the first ruling by an arbitrator in AAA approving use of predictive coding. The opposition to our use of cost-saving technology in that arbitration case was again fierce, and again included personal attacks, but the arguments for use in arbitration are very compelling. Discovery in arbitration is, after all, supposed to be constrained and expedited.
After the Da Silva Moore opinion, Maura Grossman and I upped our speaking schedule (she far more than me), and so did several tech-minded judges, including Judge Peck (although never at the same events as me, until the cloud of false allegations created by a bitter plaintiff’s counsel in Da Silva Moore could be dispelled). At Legal Tech for the next few years Predictive Coding is all anybody wanted to talk about. Then IG, Information Governance, took over as the popular tech-child of the day. In 2015 we had only a few predictive coding panels at Legal Tech, but they were well attended.
The Grossman Cormack speculates that the cause of the remarkably slow adoption is:
The complex vocabulary and rituals that have come to be associated with TAR, including statistical control sets, stabilization, F1 measure, overturns, and elusion, have dissuaded many practitioners from embracing TAR. However, none of these terms, or the processes with which they are associated, are essential to TAR.
We agree. The vendors killed what could have been their golden goose with all this control set nonsense and their engineers love of complexity and misunderstanding of legal search. I have ranted about this before. See Predictive Coding 3.0. I will not go into that again here, except to say the statistical control set nonsense that had large sampling requirements was particularly toxic. It was not only hard and expensive to do, it led to mistaken evaluations of the success or failure of projects because it ignored the reality of the evolving understand of relevance, so called concept drift. Another wrong turn involved the nonsense of using only random selection to find training documents, a practice that Grossman and I opposed vigorously. See Latest Grossman and Cormack Study Proves Folly of Using Random Search For Machine Training – Part One, Part Two, Part Three, and Part Four. Grossman and Cormack correctly criticize these old vendor driven approaches in Continuous Active Learning for TAR. They call them SAL and SPL protocols (a couple of acronyms that no one wants to trademark!).
Bottom line, the tide is changing. Over the last several years the few private attorneys who specialize in legal search, but are not employed by a vendor, have developed simpler methods. Maura and I are just the main ones writing and speaking about it, but there are many others who agree. Many have found that it is counter-productive to use control sets, random input, non-continuous training with its illogical focus on the seed set, and misleading recall point projections.
We do so in defiance of the vendor establishment and other self-proclaimed pundits in this area who benefitted by such over-complexity. Maura and Gordon, of course, have their own software (Gordon’s creation), and so never needed any vendors to begin with. Not having a world renowned information scientist like Professor Cormack as my life partner, I had no choice but to rely on vendors for their software. (Not that I complaining, mind you. I’m married to a mental health counselor, and it does not get any better than that!)
After a few years I ultimately settled on one vendor, Kroll Ontrack, but I continue to try hard to influence all vendors. It is a slow process. Even Kroll Ontrack’s software, which I call Mr. EDR, still has control set functions built in. Thanks to my persistence, it is easy to turn off these settings and do things my way, with no secret control sets and false recall calculations. Hopefully soon that will be the default setting. Their eyes have been opened. Hopefully all of the other major vendors will soon follow suit.
All of the Kroll Ontrack experts in predictive coding are now, literally, a part of my Team. They are now fully trained and believers in the simplified methods, methods very similar to those of Grossman and Cormack, albeit, as I will next explain, slightly more complicated. We proved how well these methods worked at TREC 2015 when the Kroll Ontrack experts and I did 30 review projects together in 45 days. See e-Discovery Team at TREC 2015 Total Recall Track, Final Report (116 pg. PDF), and (web page with short summary). Also see – Mr. EDR with background information on the Team’s participation in the TREC 2015 Total Recall Track.
We Agree to Disagree with Grossman and Cormack on One Issue, Yet We Still Like Their Article
We are fans of Maura Grossman and Gordon Cormack’s work, but not sycophants. We are close, but not the same; colleagues, but not followers. For those reasons we think our recommendation for you to read this article means more than a typical endorsement. We can be critical of their writings, but, truth is, we liked their new article, although we continue to dislike the name TAR (not important, but we prefer CAR). Also, and this is of some importance, my whole team continues to disagree with what we consider the somewhat over-simplified approach they take to finding training documents, namely reliance on the highest ranking documents alone.
Despite what some may think, the high-ranking approach does eventually find a full diversity of relevant documents. All good predictive coding software today pretty much uses some type of logistic regression based algorithms that are capable of building out probable relevance in that way. That is one of the things we learned by rubbing shoulders with text retrieval scientists from around the world at TREC when participating in the 2015 Total Recall Track that Grossman and Cormack helped administer. This regression type of classification system works well to avoid the danger of over-training on a particular relevancy type. Grossman and Cormack have proven that before to our satisfaction (so have our own experiments), and they again make a convincing case for this approach in this article.
Still, we disagree with their approach of only using high-ranking documents for training, but we do so on the grounds of efficiency and speed, not effectiveness. The e-Discovery Team continues to advocate a Hybrid Multimodal approach to active machine learning. We use what I like to call a four-cylinder type of CAR search engine, instead of one-cylinder, like they do.
- High-ranking documents;
- Mid-level, uncertain documents;
- A touch, a small touch, of random documents; and,
- Human ingenuity found documents, using all type of search techniques (multimodal) that seem appropriate to the search expert in charge, including keyword, linear, similarity (including chains and families), concept (including passive machine learning, clustering type search).
Predictive Coding 3.0 – The method is here described as an eight-part work flow (Step 6 – Hybrid Active Training).
The latest Grossman and Cormack’s versions of CAL (their trademark) only uses the highest-ranking documents for active training. Still, in spite of this difference, we liked their article and recommend you read it.
The truth is, we also emphasize the high-probable relevant documents for training. The difference between us is that we use the three other methods as well. On that point we agree to disagree. To be clear, we are not talking about continuous training or not, we agree on that. We are not talking about active training, or not (passive), we agree on that. We are not talking about using what they call using SAL or SPL protocols (read their article for details), we agree with them that these protocols are ineffective relics invented by misguided vendors. We are only talking about a difference in methods to find documents to use to train the classifier. Even that is not a major disagreement, as we agree with Grossman and Cormack that high-ranking documents usually make the best trainers, just not in the first seed set. There are also points in a search, depending on the project, where the other methods can help you get to the relevant documents in a fast, efficient manner. The primary difference between us is that we do not limit ourselves to that one retrieval method like Grossman and Cormack do in their trademarked CAL methodology.
Cormack and Grossman emphasize simplicity, ease of use, and reliance on the software algorithms as another way to try to overcome the Bar’s continued resistance to TAR. The e-Discovery Team has the same goal, but we do not think it is necessary to go quite that far for simplicity sake. The other methods we use, the other three cylinders, are not that difficult and have many advantages. e-Discovery Team at TREC 2015 Total Recall Track, Final Report (116 pg. PDF and web page with short summary). Put another way, we like the ability of fully automatic driving from time to time, but we want to keep an attorney’s learned hand at or near the wheel at all times. See Why the ‘Google Car’ Has No Place in Legal Search.
Accessibility with Integrity: The First Reason We Recommend the Article
Here’s the first reason we like Grossman & Cormack’s article, Continuous Active Learning for TAR: you do not have to be one of Professor Cormac’s PhD students to understand it. Yes. It is accessible, not overly technical, and yet still has scientific integrity, still has new information, accurate information, and still has useful knowledge.
It is not easy to do both. I know because I try to make all of my technical writings that way, including the 57 articles I have written on TAR, which I prefer to call Predictive Coding, or CAR. I have not always succeeded in getting the right balance, to be sure. Some of my articles may be too technical, and perhaps some suffer from breezy information over-load and knowledge deficiency. Hopefully none are plain wrong, but my views have changed over the years. So have my methods. If you compare my latest work-flow (below) with earlier ones, you will see some of the evolution, including the new emphasis over the past few years with continuous training.
The Cormacks and I are both trying hard to get the word out to the Bar as to the benefits of using active machine learning in legal document review. (We all agree on that term, active machine learning, and all agree that passive machine learning is not an acceptable substitute.) It is not easy to write on this subject in an accurate, yet still accessible and interesting manner. There is a constant danger that making a subject more accessible and simple will lead to inaccuracies and misunderstandings. Maura and Gordon’s latest article meets this challenge.
At the outset, CAL resembles a web search engine, presenting first the documents that are most likely to be of interest, followed by those that are somewhat less likely to be of interest. Unlike a typical search engine, however, CAL repeatedly refines its understanding about which of the remaining documents are most likely to be of interest, based on the user’s feedback regarding the documents already presented. CAL continues to present documents, learning from user feedback, until none of the documents presented are of interest.
That is a good way to start an article. The comparison with a Google search having continued refinement based on user feedback is well thought out; simple, yet accurate. It represents a description honed by literally hundreds of presentations on the topic my Maura Grossman. No one has talked more on this topic than her, and I for one intend to start using this analogy.
Rare Description of Algorithm Types – Our Second Reason to Recommend the Article
Another reason our Team liked Continuous Active Learning for TAR is the rare description of search algorithm types that it includes. Here we see the masterful touch of one of the world’s leading academics on text retrieval, Gordon Cormack. First, the article makes clear the distinction between effective analytic algorithms that truly rank documents using active machine learning, and a few other popular programs now out there that use passive learning techniques and call it advanced analytics.
The supervised machine-learning algorithms used for TAR should not be confused with unsupervised machine-learning algorithms used for clustering, near-duplicate detection, and latent semantic indexing, which receive no input from the user and do not rank or classify documents.
These other older, unsupervised search methods are what I call concept search. It is not predictive coding. It is not advanced analytics, no matter what some vendors may tell you. It is yesterday’s technology – helpful, but far from state-of-the-art. We still use concept search as part of multimodal, just like any other search tool, but our primary reliance to properly rank documents is placed on active machine learning.
The Cormack-Grossman article goes farther than pointing out this important distinction, it also explains the various types of bona fide active machine learning algorithms. Again, some are better than others. First Professor Cormack explains the types that have been found to be effective by extensive research over the past ten years or so.
Supervised machine-learning algorithms that have been shown to be effective for TAR include:
– Support vector machines. This algorithm uses geometry to represent each document as a point in space, and deduces a boundary that best separates relevant from not relevant documents.
– Logistic regression. This algorithm estimates the probability of a document’s relevance based on the content and other attributes of the document.
Conversely Cormack explains:
Popular, but generally less effective, supervised machine-learning algorithms include:
– Nearest neighbor. This algorithm classifies a new document by finding the most similar training document and assuming that the correct coding for the new document is the same as its nearest neighbor.
– Naïve Bayes (Bayesian classifier). This algorithm estimates the probability of a document’s relevance based on the relative frequency of the words or other features it contains.
Ask your vendor which algorithms its software includes. Prepare yourself for double-talk.
If you try out your vendors software and the Grossman-Cormack CAL method does not work for you, and even the e-Discovery Team’s slightly more diverse Hybrid Multimodal method does not work, then your software may be to blame. As Grossman-Cormack put it, where the phrase “TAR tool” means software:
[I]t will yield the best possible results only if the TAR tool incorporates a state-of-the-art learning algorithm.
That means software that uses a type of support vector machine and/or logistic regression.
Teaching by Example – Our Third Reason to Recommend the Article
The article uses a long example involving search of Jeb Bust email to show you how their CAL method works. This is an effective way to teach. We think they did a good job with this. Rather than spoil the read with quotes and further explanation, we urge you to check out the article to see for yourself. Yes, it is an oversimplification, after all this is a short article, but it is a good one, and is still accurate.
Quality Control Suggestions – Our Fourth Reason to Recommend the Article
Another reason we like the article are the quality control suggestions it includes. They essentially speak of using other search methods, which is exactly what we do in Hybrid Multimodal. Here are their words:
To increase counsel’s confidence in the quality of the review, they might:
Review an additional 100, 1,000, or even more documents.
Experiment with additional search terms, such as “Steve Jobs,” “iBook,” or “Mac,” and examine the most-likely relevant documents containing those terms.
Invite the requesting party to suggest other keywords for counsel to apply.
Review a sample of randomly selected documents to see if any other documents of interest are identified.
We like this because it shows that the differences are small between the e-Discovery Team’s Hybrid Multimodal method (hey, maybe I should claim Trademark rights to Hybrid Multimodal, but then again, no vendors are using my phrase to sell their products) using continuous active training, and the Grossman-Cormack trademarked CAL method. We also note that their section on Measures of Success essentially mirrors our own thoughts on metric analysis and ei-Recall. Introducing “ei-Recall” – A New Gold Standard for Recall Calculations in Legal Search – Part One, Part Two and Part Three.
Article Comes With an Online “Do it Yourself” CAL Trial Kit – Our Fifth Reason to Recommend the Article
We are big believers in learning by doing. That is especially true in legal tasks that seem complicated in the abstract. I can write articles and give presentations that provide explanations of AI-Enhanced Review. You may get an intellectual understanding of predictive coding from these, but you still will not know how to do it. On the other hand, if we have a chance to show someone an entire project, have them shadow us, then they will really learn how it is done. It is like teaching a young lawyer how to try a case. For a price, we will be happy to do so (assuming conflicts clear).
Maura and Gordon seem to agree with us on that learn by doing point and have created an online tool that anyone can use to try out their method. In allows for a search of the Jeb Bush email, the same set of 290,099 emails that we used in ten of the thirty topics in 2015 TREC. In their words:
There is no better way to learn CAL than to use it. Counsel may use the online model CAL system to see how quickly and easily CAL can learn what is of interest to them in the Jeb Bush email dataset. As an alternative to throwing up their hands over seed sets, control sets, F1 measures, stabilization, and overturns, counsel should consider using their preferred TAR tool in CAL mode on their next matter.
You can try out their method with their online tool, or in a real project using your vendor’s tool. By the way, we did that as part of our TREC 2015 experiments, and the Kroll Ontrack software worked about the same as theirs, even when we used their one-cylinder, high ranking only, CAL (their trademark) method.
Here is where you can find their CAL testing tool: cormack.uwaterloo.ca/cal. Those of you who are still skeptical can see for yourself how it works. You can follow the example given in the article about searching for documents relevant to Apple products, to verify their description of how that works. For even more fun, you can dream up your own searches.
Perhaps, if you try hard enough, you can find some example searches where their high-end only method, which is built into the test software, does not work well. For example, try finding all emails that pertain to, or in any way mention, the then President, George Bush. Try entering George Bush in the demo test and see for yourself what happens.
It becomes a search for George + Bush in the same document, and then goes from there based on your coding the highest ranked documents presented as either relevant or non-relevant. You will see that you quickly end up in a TAR pit. The word Bush is in every email (I think), so you are served up with every email where George is mentioned, and believe me, there are many Georges, even if there is only one President George Bush. Here is the screen shot of the first document presented after entering George Bush. I called it relevant.
These kind of problem searches do not discredit TAR, or even the Grossman Cormack one-cylinder search method. If this happened to you in a real search project, you could always use our Hybrid Multimodal™ method for the seed set (1st training), or start over with a different keyword or keywords to start the process. You could, for instance, search for President Bush, or President within five of George, or “George Bush.” There are many ways, some faster and more effective than others.
Even using the single method approach, if you decided to use the keywords “President + Bush”, then the search will go quicker than “George + Bush.” Even just using the term “President” works better than George + Bush, but still seems like a TAR pit, and not a speeding CAR. It will probably get you to the same destination, high recall, but the journey is slightly longer and, at first, more tedious. This high recall result was verified in TREC 2015 by our Team, and by a number of Universities who participated in the fully automatic half of the Total Recall Track, including Gordon’s own team. This was all done without any manual review by the fully automatic participants because there was instant feedback of relevant or irrelevant based on a prejudged gold standard. See e-Discovery Team at TREC 2015 Total Recall Track, Final Report (116 pg. PDF), and (web page with short summary). With this instant feedback protocol, all of the teams attained high recall and good precision. Amazing but true.
You can criticized this TREC experiment protocol, which we did in our report, as unrealistic to legal practice because:
(1) there is no SME who works like that (and there never will not be, until legal knowledge itself is learned by an AI); and,
(2) the searches presented as tasks were unrealistically over-simplistic. Id.
But you cannot fairly say that CAL (their trademark) does not work. The glass is most certainly not half empty. Moreover, the elixir in this glass is delicious and fun, especially when you use our Hybrid Multimodal™ method. See Why I Love Predictive Coding: Making document review fun with Mr. EDR and Predictive Coding 3.0.
Active machine learning (predictive coding) using support vector or logistic regression algorithms, and a method that employs continuous active training, using either one cylinder (their CAL), or four (our Hybrid Multimodal), really works, and is not that hard to use. Try it out and see for yourself. Also, read the Grossman Cormack article, it only takes about 30 minutes. Continuous Active Learning for TAR (Practical Law, April/May 2016). Feel free to leave any comments below. I dare say you can even ask questions of Grossman or Cormack here. They are avid readers and will likely respond quickly.