This is the sixth installment of the article explaining the e-Discovery Team’s latest enhancements to electronic document review using Predictive Coding. Here are Parts One, Two, Three, Four and Five. This series explains the nine insights behind the latest upgrade to version 4.0 and the slight revisions these insights triggered to the eight-step workflow. We have already covered the nine insights. Now we will begin to review the revised eight-step workflow.
The eight-step chart provides a model of the Predictive Coding 4.0 methods. (You may download and freely distribute this chart without further permission, so long as you do not change it.) The circular flows depict the iterative steps specific to the predictive coding features. Steps four, five and six iterate until the active machine training reaches satisfactory levels and thereafter final quality control and productions are done.
Although presented as sequential steps for pedantic purposes, Predictive Coding 4.0 is highly adaptive to circumstances and does not necessarily follow a rigid linear order. For instance, some of the quality control procedures are used throughout the search and review, and rolling productions can begin at any time.
To fully understand the 4.0 method, it helps to see how it is fits into an overall Dual-Filter Culling process. See License to Cull The Two-Filter Document Culling Method (2015) (see illustrative diagram right). Still more information on predictive coding and electronic document review can be found in the over sixty articles published here on the topic since 2011. Reading helps, but we have found that the most effective way to teach this method, like any other legal method, is by hands-on guidance. Our eight-step workflow can be taught to any legal professional who already has experience with document review by the traditional second-chair type of apprenticeship training.
This final segment of our explanation of Predictive Coding 4.0 will include some of the videos that I made earlier this year describing our document review methods. Document Review and Predictive Coding: an introductory course with 7 videos and 2,982 words. The first video below introduces the eight-step method. Once you get past my attempt at Star Wars humor in the opening credits of the video you will hear my seven-minute talk. It begins with why I think predictive coding and other advanced technologies are important to the legal profession and how we are now at a critical turning point of civilization.
Step One – ESI Communications
Good review projects begin with ESI Communications, they begin with talking. You need to understand and articulate the disputed issues of fact. If you do not know what you are looking for, you will never find it. That does not mean you know of specific documents. If you knew that, it would not be much of a search. It means you understand what needs to be proven at trial and what documents will have impact on judge and jury. It also means you know the legal bounds of relevance, including especially Rule 26(b)(1).
ESI Communications begin and end with the scope of the discovery, relevance and related review procedures. The communications are not only with opposing counsel or other requesting parties, but also with the client and the e-discovery team assigned to the case. These Talks should be facilitated by the lead e-Discovery specialist attorney assigned to the case. But they should include the active participation of the whole team, including all trial lawyers not otherwise very involved in the ESI review.
The purpose of all of this Talk is to give everyone an idea as to the documents sought and the confidentiality protections and other special issues involved. Good lines of communication are critical to that effort. This first step can sometimes be difficult, especially if there are many new members to the group. Still, a common understanding of relevance, the target searched, is critical to the successful outcome of the search. This includes the shared wisdom that the understanding of relevance will evolve and grow as the project progresses.
We need to Talk to understand what we are looking for. What is the target? What is the information need? What documents are relevant? What would a hot document look like? A common understanding of relevance by a review team, of what you are looking for, requires a lot of communication. Silent review projects are doomed to failure. They tend to stagnate and do not enjoy the benefits of Concept Drift, where a team’s understanding of relevance is refined and evolves as the review progresses. Yes, the target may move, and that is a good thing. See: Concept Drift and Consistency: Two Keys To Document Review Quality – Parts One, Two and Three.
Review projects are also doomed where the communications are one way, lecture down projects where only the SME talks. The reviewers must talk back, must ask questions. The input of reviewers is key. Their questions and comments are very important. Dialogue and active listening are required for all review projects, including ones with predictive coding.
You begin with analysis and discussions with your client, your internal team, and then with opposing counsel, as to what it is you are looking for and what the requesting party is looking for. The point is to clarify the information sought, the target. You cannot just stumble around and hope you will know it when you find it (and yet this happens all too often). You must first know what you are looking for. The target of most searches is the information relevant to disputed issues of fact in a case or investigation. But what exactly does that mean? If you encounter unresolvable disputes with opposing counsel on the scope of relevance, which can happen during any stage of the review despite your best efforts up-front, you may have to include the Judge in these discussions and seek a ruling.
Here is my video explaining the first step of ESI Communications.
“ESI Discovery Communications” is about talking to your review team, including your client, key witnesses; it is about talking to opposing counsel; and, eventually, if need be, talking to the judge at hearings. Friendly, informal talk is a good method to avoid the tendency to polarize and demonize “the other side,” to build walls and be distrustful and silent.
The amount of distrust today between attorneys is at an all-time high. This trend must be reversed. Mutually respectful talk is part of the solution. Slowing things down helps too. Do not respond to a provocative text or email until you calm down. Take your time to ponder any question, even if you are not upset. Take your time to research and consult with others first. This point is critical. The demand for instant answers is never justified, nor required under the rules of civil procedure. Think first and never respond out of anger. We are all entitled to mutual respect. You have a right to demand that. So do they.
This point about not actually speaking with people in realtime, in person, or by phone or video, is, to some extent, generational. Many younger attorneys seem to have an inherent loathing of the phone and speaking out loud. They let their thumbs do the talking. (This is especially true in e-discovery where the professionals involved tend to be very computer oriented, not people oriented. I know because I am like that.) Meeting in person in real-time is distasteful to many, not just Gen X. Many of us prefer to put everything in emails and texts and tweets and posts, etc. That may make it easier to pause to reflect, especially if you are loathe to say in person that you do not know and will need to get back to them on that. But real time talking is important to full communication. You may need to force yourself to real-time interpersonal interactions. Many people are better at real-time talk than others, just like many are better at fast comprehension of documents than others. It is often a good idea for a team to have a designated talker, especially when it comes to speaking with opposing counsel or the client.
In e-discovery, where the knowledge levels are often extremely different, with one side knowing more about the subject than the other, the fist step of ESI Communications or Talk usually requires patient explanations. ESI Communications often require some amount of educational efforts by the attorneys with greater expertise. The trick is to do that without being condescending or too pedantic, and, in my case at least, without losing your patience.
Some object to the whole idea of helping opposing counsel by educating them, but the truth is, this helps your clients too. You are going to have to explain everything when you take a dispute to the judge, so you might as well start upfront. It helps save money and moves the case along. Trust building is a process best facilitated by honest, open talk.
I use of the term Talk to invoke the term listen as well. That is one reason we also refer to the first step as “Relevance Dialogues” because that is exactly what it should be, a back and forth exchange. Top down lecturing is not intended here. Even when a judge talks, where the relationship is truly top down, the judge always listens before rendering his or her decision. You are given the right to be heard at a hearing, to talk and be listened to. Judges listen a lot and usually ask many questions. Attorneys should do the same. Never just talk to hear the sound of your own voice. As Judge David Waxse likes to say, talk to opposing counsel as if the judge were listening.
The same rules apply when communicating about discovery with the judge. I personally prefer in-person hearings, or at least telephonic, as opposed to just throwing memos back and forth. This is especially true when the memorandums have very short page limits. Dear Judges: e-discovery issues are important and can quickly spiral out of control without your prompt attention. Please give us the hearings and time needed. Issuing easy orders that just split the baby will do nothing but pour gas on a fire.
In my many years of lawyering I have found that hearings and meetings are much more effective than exchanging papers. Dear brothers and sisters in the BAR: stop hating, stop distrusting and vilifying, and start talking to each other. That means listening too. Understand the other-side. Be professional. Try to cooperate. And stop taking extreme positions that assume the judge will just split the baby.
It bears emphasis that by Talk in this first step we intend dialogue. A true back and forth. We do not intend argument, nor winners and losers. We do intend mutual respect. That includes respectful disagreement, but only after we have heard each other out and understood our respective positions. Then, if our talks with the other side have reached an impasse, at least on some issues, we request a hearing from the judge and set out the issues for the judge to decide. That is how our system of justice and discovery are designed to work. If you fail to talk, you not only doom the document review project, you doom the whole case to unnecessary expense and frustration.
This dialogue method is based on a Cooperative approach to discovery that was promoted by the late, great Richard Braman of The Sedona Conference. Cooperation is not only a best practice, but is, to a certain extent, a minimum standard required by rules of professional ethics and civil procedure. The primary goal of these dialogues for document review purposes is to obtain a common understanding of the e-discovery requests and reach agreement on the scope of relevancy and production.
ESI Communications in this first step may, in some cases, require disclosure of the actual search techniques used, which is traditionally protected by work product. The disclosures may also sometimes include limited disclosure of some of the training documents used, typically just the relevant documents. See Judge Andrew Peck’s 2015 ruling on predictive coding, Rio Tinto v. Vale, 2015 WL 872294 (March 2, 2015, SDNY). In Rio Tinto Judge Peck wisely modified somewhat his original views stated in Da Silva on the issue of disclosure. Moore v. Publicis Groupe, 2012 WL 607412 (S.D.N.Y. Feb. 24, 2012) (approved and adopted in Da Silva Moore v. Publicis Groupe, 2012 WL 1446534, at *2 (S.D.N.Y. Apr. 26, 2012)). Judge Peck no longer thinks that parties should necessarily disclose any training documents, and may instead:
… insure that training and review was done appropriately by other means, such as statistical estimation of recall at the conclusion of the review as well as by whether there are gaps in the production, and quality control review of samples from the documents categorized as non-responsive. See generally Grossman & Cormack, Comments, supra, 7 Fed. Cts. L.Rev. at 301-12.
The Court, however, need not rule on the need for seed set transparency in this case, because the parties agreed to a protocol that discloses all non-privileged documents in the control sets. (Attached Protocol, ¶¶ 4(b)-(c).) One point must be stressed — it is inappropriate to hold TAR to a higher standard than keywords or manual review. Doing so discourages parties from using TAR for fear of spending more in motion practice than the savings from using TAR for review.
Id. at *3. Also see Rio Tinto v. Vale, Stipulation and Order Re: Revised Validation and Audit Protocols for the use of Predictive Coding in Discovery, 14 Civ. 3042 (RMB) (AJP), (order dated 9/2/15 by Maura Grossman, Special Master, and adopted and ordered by Judge Peck on 9/8/15).
Judge Peck here follows the current prevailing view on disclosure that I also endorse. Disclose the relevant documents used in active machine learning, but not the irrelevant documents used in training. If there are borderline, grey area documents classified as irrelevant, you may need to disclose these type of documents by description, not actual production. Again, talk to the requesting party on where you are drawing the line. Talk about the grey area documents that you encounter. If they disagree, ask for a ruling before your training is complete.
The goals of Rule 1 of the Federal Rules of Civil Procedure (just, speedy and inexpensive) are impossible in all phases of litigation, not just discovery, unless attorneys communicate with each other. The parties may hate each other and refuse to talk. That sometimes happens. But the attorneys must be above the fray. That is a key purpose and function of an attorney in a dispute. It is sad that so many attorneys do not seem to understand that. If you are faced with such an attorney, my best advice is to lead by example, document the belligerence and seek the help of your presiding judge.
Although Talk to opposing counsel is important, even more important is talking within the team. It is an important method of quality control and efficient project management. Everyone needs to be on the same page of relevance and discoverability. Work needs to be coordinated. Internal team Talk needs to be very close. Although a Vulcan mind meld might be ideal, it is not really necessary. Still, during a project a steady flow of talk, usually in the form of emails or chats, is normal and efficient. Clients should never complain about time spent communicating to manage a document review project. It can save a tremendous amount of money in the long run, so long as it is focused on the task at hand.
Step Two – Multimodal ECA
Multimodal Early Case Assessment – ECA – summarizes the second step in our 8-step work flow. We used to call the second step “Multimodal Search Review.” It is still the same activity, but we tweaked the name to emphasize the ECA significance of this step. After we have an idea of what we are looking for from ESI Communications in step one, we start to use every tool at our disposal to try to find the relevant documents. Every tool that is, except for active machine learning. Our first look at the documents is our look, not the machine’s. That is not because we do not trust the AI’s input. We do. It is because there is no AI yet. The predictive coding only begins after you feed training documents into the machine. That happens in step four.
Our Multimodal ECA step-two does not take that long, so the delay in bringing in our AI is usually short. In our experiments at TREC in 2015 and 2016 under the auspicious of NIST, where we skipped steps three and seven to save time, and necessarily had little ESI Communications in step one, we would often complete simple document reviews of several hundred thousand documents in just a few hours. We cannot match these results in real-life legal document review projects because the issues in law suits are usually much more complicated than the issues presented by most topics at TREC. Also, we cannot take the risk of making mistakes in a real legal project that we did in an academic event like TREC.
Again, the terminology revision to say Multimodal ECA is more a change of style than substance. We have always worked in this manner. The name change is just to better convey the idea that we are looking for the low hanging fruit, the easy to find documents. We are getting an initial assessment of the data by using all of the tools of the search pyramid except for the top tier active machine learning. The AI comes into play soon enough in steps four and five, sometimes as early as the same day.
I have seen projects where key documents are found during the first ten minutes of looking around. Usually the secrets are not revealed so easily, but it does happen. Step two is the time to get to know the data, run some obvious searches, including any keyword requests for opposing counsel. You use the relevant and irrelevant documents you find in step two as the documents you select in step four to train the AI.
In the process of this initial document review you start to get a better understanding of the custodians, their data and relevance. This is what early case assessment is all about. You will find the rest of the still hidden relevant documents in the iterated rounds of machine training and other searches that follow. Here is my video description of step two.
Although we speak of searching for relevant documents in step two, it is important to understand that many irrelevant documents are also incidentally found and coded in that process. Active machine learning does not work by training on relevant documents alone. It must also include examples of irrelevant documents. For that reason we sometimes actively search for certain kinds of irrelevant documents to use in training. One of our current research experiments with Kroll Ontrack is to determine the best ratios between relevant and irrelevant documents for effective document ranking. See TREC reports at Mr. EDR as updated from time to time. At this point we have that issue nailed.
The multimodal ECA review in step two is carried out under the supervision of the Subject Matter Experts on the case. They make final decisions where there is doubt concerning the relevance of a document or document type. The SME role is typically performed by a team, including the partner in charge of the case – the senior SME – and senior associates, and e-Discovery specialist attorney(s) assigned to the case. It is, or should be, a team effort, at least in most large projects. As previously described, the final arbitrator on scope is made by the senior SME, who in turn is acting as the predictor of the court’s views. The final, final authority is always the Judge. The chart below summarizes the analysis of the SME and judge on the discoverability of any document. See Predictive Coding 4.0, Part Five.
When I do a project, acting as the e-Discovery specialist attorney for the case, I listen carefully to the trial lawyer SME as he or she explains the case. By extensive Q&A the members of the team understand what is relevant. We learn from the SME. It is not exactly a Vulcan mind-meld, but it can work pretty well with a cohesive team. Most trial lawyers love to teach and opine on relevance and their theory of the case.
Although a good SME team communicates and plans well, they also understand, typically from years of experience, that the intended relevance scope is like a battle plan before the battle. As the famous German military strategist, General Moltke the Elder said: No battle plan ever survives contact with the enemy. So too no relevance scope plan ever survives contact with the corpus of data. The understanding of relevance will evolve as the documents are studied, the evidence is assessed, and understanding of what really happened matures. If not, someone is not paying attention. In litigation that is usually a recipe for defeat. See Concept Drift and Consistency: Two Keys To Document Review Quality – Parts One, Two and Three.
The SME team trains and supervises the document review specialists, aka, contract review attorneys, who usually then do a large part of the manual reviews (step-six), and few if any searches. Working with review attorneys is a constant iterative process where communication is critical. Although I sometimes use an army-of-one approach where I do everything myself (that is how I did the EDI Oracle competition and most of the TREC topics), my preference now is to use two or three reviewers to help with the document review. With good methods, including culling methods, and good software, it is rarely necessary to use more reviewers than that. With the help of strong AI, say that included in Mr. EDR, we can easily classify a million or so documents for relevance with that size team. More reviewers than that may well be needed for complex redaction projects and other production issues, but not for a well-designed first-pass relevance search.
One word of warning when using document reviewers, it is very important for all members of the SME team to have direct and substantial contact with the actual documents, not just the reviewers. For instance, everyone involved in the project should see all hot documents found in any step of the process. It is especially important for the SME trial lawyer at the top of the expert pyramid to see them, but that is rarely more than a few hundred documents, often just a few dozen. Otherwise, the top SME need only see the novel and grey area documents that are encountered, where it is unclear on which side of the relevance line they should fall in accord with the last instructions. Again, the burden on the senior, and often technologically challenged senior SME attorneys, is fairly light under these Version 4.0 procedures.
The SME team relies on a primary SME, who is typically the trial lawyer in charge of the whole case, including all communications on relevance to the judge and opposing counsel. Thereafter, the head SME is sometimes only consulted on an as-needed basis to answer questions and make specific decisions on the grey area documents. There are always a few uncertain documents that need elevation to confirm relevance, but as the review progresses, their number usually decreases, and so the time and attention of the senior SME decreases accordingly.
Step Three – Random Prevalence
There has been no change in this step from Version 3.0 to Version 4.0. The third step, which is not necessarily chronological, is essentially a computer function with statistical analysis. Here you create a random sample and analyze the results of expert review of the sample. Some review is thus involved in this step and you have to be very careful that it is correctly done. This sample is taken for statistical purposes to establish a baseline for quality control in step seven. Typically prevalence calculations are made at this point. Some software also uses this random sampling selection to create a control set. As explained at length in Predictive Coding 3.0, we do not use a control set because it is so unreliable. It is a complete waste of time and money and does not produce reliable recall estimates. Instead, we take a random sample near the beginning of a project solely to get an idea on Prevalence, meaning the approximate number of relevant documents in the collection.
Unless we are in a very rushed situation, such as in the TREC projects, where we would do a complete review in a day or two, or sometimes just a few hours, we like to take the time for the sample and prevalence estimate.
It is all about getting a statistical idea as to the range of relevant documents that likely exist in the data collected. This is very helpful for a number of reasons, including proportionality analysis (importance of the ESI to the litigation and cost estimates) and knowing when to stop your search, which is part of step seven. Knowing the number of relevant documents in your dataset can be very helpful, even if that number is a range, not exact. For example, you can know from a random sample that there are between four thousand and six thousand relevant documents. You cannot know there are exactly five thousand relevant documents. See: In Legal Search Exact Recall Can Never Be Known. Still, knowledge of the range of relevant documents (red in the diagram below) is helpful, albeit not critical to a successful search.
In step three an SME is only needed to verify the classifications of any grey area documents found in the random sample. The random sample review should be done by one reviewer, typically your best contract reviewer. They should be instructed to code as Uncertain any documents that are not obviously relevant or irrelevant based on their instructions and step one. All relevance codings should be double checked, as well as Uncertain documents. The senior SME is only consulted on an as-needed basis.
Document review in step three is limited to the sample documents. Aside from that, this step is a computer function and mathematical analysis. Pretty simple after you do it a few times. If you do not know anything about statistics, and your vendor is also clueless on this (rare), then you might need a consulting statistician. Most of the time this is not necessary and any competent Version 4.0 vendor expert should be able to help you through it.
It is not important to understand all of the math, just that random sampling produces a range, not an exact number. If your sample size is small, then the range will be very high. If you want to reduce your range in half, which is a function in statistics known as a confidence interval, you have to quadruple your sample size. This is a general rule of thumb that I explained in tedious mathematical detail several years ago in Random Sample Calculations And My Prediction That 300,000 Lawyers Will Be Using Random Sampling By 2022. Our Team likes to use a fairly large sample size of about 1,533 documents that creates a confidence interval of plus or minus 2.5%, subject to a confidence level of 95% (meaning the true value will lie within that range 95 times out of 100). More information on sample size is summarized in the graph below. Id.
The picture below this paragraph illustrates a data cloud where the yellow dots are the sampled documents from the grey dot total, and the hard to see red dots are the relevant documents found in that sample. Although this illustration is from a real project we had, it shows a dataset that is unusual in legal search because the prevalence here was high, between 22.5% and 27.5%. In most data collections searched in the law today, where the custodian data has not been filtered by keywords, the prevalence is far less than that, typically less than 5%, maybe even less that 0.5%. The low prevalence increases the range size, the uncertainties, and requires a binomial calculation adjustment to determine the statistically valid confidence interval, and thus the true document range.
For example, in a typical legal project with a few percent prevalence range, it would be common to see a range between 20,000 and 60,000 relevant documents in a 1,000,000 collection. Still, even with this very large range, we find it useful to at least have some idea of the number of relevant documents that we are looking for. That is what the Baseline step can provide to you, nothing more nor less.
As mentioned, your vendor can probably help you with these statistical estimates. Just do not let them tell you that it is one exact number. It is always a range. The one number approach is just a shorthand for the range. It is simply a point projection near the middle of the range. The one number point projection is the top of the typical probability bell curve range shown right, which illustrates a 95% confidence level distribution. The top is just one possibility, albeit slightly more likely than either end points. The true value could be anywhere in the blue range.
To repeat, the step three prevalence baseline number is always a range, never just one number. Going back to the relatively high prevalence example, the below bell cure shows a point projection of 25% prevalence, with a range of 22.2% and 27.5%, creating a range of relevant documents of from between 225,000 and 275,000. This is shown below.
The important point that many vendors and other “experts” often forget to mention, is that you can never know exactly where within that range the true value may lie. Plus, there is always a small possibility, 5% when using a sample size based on a 95% confidence level, that the true value may fall outside of that range. It may, for example, only have 200,000 relevant documents. This means that even with a high prevalence project with datasets that approach the Normal Distribution of 50% (here meaning half of the documents are relevant), you can never know that there are exactly 250,000 documents, just because it is the mid-point or point projection. You can only know that there are between 225,000 and 275,000 relevant documents, and even that range may be wrong 5% of the time. Those uncertainties are inherent limitations to random sampling.
Shame on the vendors who still perpetuate that myth of certainty. Lawyers can handle the truth. We are used to dealing with uncertainties. All trial lawyers talk in terms of probable results at trial, and risks of loss, and often calculate a case’s settlement value based on such risk estimates. Do not insult our intelligence by a simplification of statistics that is plain wrong. Reliance on such erroneous point projections alone can lead to incorrect estimates as to the level of recall that we have attained in a project. We do not need to know the math, but we do need to know the truth.
The short video that follows will briefly explain the Random Baseline step, but does not go into the technical details of the math or statistics, such as the use of the binomial calculator for low prevalence. I have previously written extensively on this subject. See for instance:
- In Legal Search Exact Recall Can Never Be Known
- Random Sample Calculations And My Prediction That 300,000 Lawyers Will Be Using Random Sampling By 2022
- Borg Challenge: Part Two where I begin the search with a random sample (text and video)
If you prefer to learn stuff like this by watching cute animated robots, then you might like: Robots From The Not-Too-Distant Future Explain How They Use Random Sampling For Artificial Intelligence Based Evidence Search. But be careful, their view is version 1.0 as to control sets.
Thanks again to William Webber and other scientists in this field who helped me out over the years to understand the Bayesian nature of statistics (and reality).
To be continued …