Predictive Coding 4.0 – Nine Key Points of Legal Document Review and an Updated Statement of Our Workflow – Part Three

September 26, 2016

This is the third installment of my lengthy article explaining the e-Discovery Team’s latest enhancements to electronic document review using Predictive Coding. Here are Parts One and Two. This series explains the nine insights (6+3) behind the latest upgrade to version 4.0 and the slight revisions these insights triggered to the eight-step workflow. This is all summarized by the diagram below, which you may freely copy and use if you make no changes.

To summarize this series explains the seventeen points, listed below, where the first nine are insights and the last eight are workflow steps:

  1. Active Machine Learning (aka Predictive Coding)
  2. Concept & Similarity Searches (aka Passive Learning)
  3. Keyword Search (tested, Boolean, parametric)
  4. Focused Linear Search (key dates & people)
  5. GIGO & QC (Garbage In, Garbage Out) (Quality Control)
  6. Balanced Hybrid (man-machine balance with IST)
  7. SME (Subject Matter Expert, typically trial counsel)
  8. Method (for electronic document review)
  9. Software (for electronic document review)
  10. Talk (step 1 – relevance dialogues)
  11. ECA (step 2 – early case assessment using all methods)
  12. Random (step 3 – prevalence range estimate, not control sets)
  13. Select (step 4 – choose documents for training machine)
  14. AI Rank (step 5 – machine ranks documents according to probabilities)
  15. Review (step 6 – attorneys review and code documents)
  16. Zen QC (step 7 – Zero Error Numerics Quality Control procedures)
  17. Produce (step 8 – production of relevant, non-privileged documents)

So far in Part One of this series we explained how these insights came about and provided other general background. In Part Two we explained the first of the nine insights, Active Machine Learning, including the method of double-loop learning. In the process we introduced three more insights, Balanced Hybrid, Concept & Similarity Searches, and Software. For continuity purposes we will address Balanced Hybrid next. (I had hoped to cover many more of the seventeen in this third installment, but turns out it all takes more words than I thought.)

Balanced Hybrid
Using Intelligently Spaced Training  – IST™

The Balanced Hybrid insight is complementary to Active Machine Learning. It has to do with the relationship between the human training the machine and the machine itself. The name itself says it all, namely that is it balanced. We rely on both software and skilled attorneys using the software.

scales_hybrid

We advocate reliance on the machine after it become trained, after it starts to understand your conception of relevance. At that point we find it very helpful to rely on what the machine has determined to be the documents most likely to be relevant. We have found it is a good way to improve precision in the sixth step of our 8-step document review methodology shown below. We generally use a balanced approach where we start off relying more on human selections of documents for training based on their knowledge of the case and other search selection processes, such as keyword or passive machine learning, a/k/a concept search. See steps 2 and 4 of our 8-step method – ECA and Select. Then we switch to relying more on the machine as it’s understanding catches one. See steps 4 and 5 – Select and AI Rank. It is usually balanced throughout a project with equal weight given to the human trainer, typically a skilled attorney, and the machine, a predictive coding algorithm of some time, typically logistic regression or support vector.


predictive_coding_4-0_8-steps_ist

Unlike other methods of Active Machine Learning we do not completely turn over to the machine all decisions as to what documents to review next. We look to the machine for guidance as to what documents should be reviewed next, but it is always just guidance. We never completely abdicate control over to the machine. I have gone into this before at some length in my article Why the ‘Google Car’ Has No Place in Legal Search. In this article I cautioned against over reliance on fully automated methods of active machine learning. Our method is designed to empower the humans in control, the skilled attorneys. Thus although our Hybrid method is generally balanced, our scale tips slightly in favor of humans, the team of attorneys who run the document review. We favor humans. So while we like our software very much, and have even named it Mr. EDR, we have an unabashed favoritism for humans. More on this at the conclusion of the Balanced Hybrid section of this article.

scales_hybrid_tipped

Three Factors That Influence the Hybrid Balance

We have shared the previously described hybrid insights before in earlier e-Discovery Team writings on predictive coding. The new insights on Balanced Hybrid are described in the rest of this segment. Again, they are not entirely new either. They represent more of a deepening of understanding and should be familiar to most document review experts. First, we have gained better insight into when and why the Balanced Hybrid approach should be tipped one way or another towards greater reliance on humans or machine. We see three factors that influence our decision.

  1. On some projects your precision and recall improves by putting greater reliance of the AI, on the machine. These are typically projects where one or more of the following conditions exist:

the data itself is very complex and difficult to work with, such as specialized forum discussions; or,

* the search target is ill-defined, i.w. – no one is really sure what they are looking for; or,

* the Subject Matter Expert (SME) making final determinations on relevance has limited experience and expertise.

3-factors_hybrid_balance2. On some projects your precision and recall improves by putting even greater reliance of the humans, on the skilled attorneys working with the machine. These are typically projects where the converse of one or more of the three criteria above are present:

* the data itself is fairly simple and easy to work with, such as a disciplined email user (note this has little or nothing to do with data volume) or,

* the search target is well-defined, i.w. there are clearly defined search requests and everyone is on the same page as to what they are looking for; or,

* the Subject Matter Expert (SME) making final determinations on relevance has extensive experience and expertise.

What was somewhat surprising from our 2016 TREC research is how one-sided you can go on the Human side of the equation and still attain near perfect recall and precision. The Jeb Bush email underlying all thirty of our topics in TREC Total Recall Track 2016 is, at this point, well-known to us. It is fairly simple and easy to work with. Although the spelling of the thousands of constituents who wrote to Jeb Bush was atrocious (far worse than general corporate email, except maybe construction company emails), Jeb’s use of the email was fairly disciplined and predictable. As a Florida native and lawyer who lived through the Jeb Bush era, and was generally familiar with all of the issues, and have become very familiar with his email, I have become a good SME, and, to a somewhat lesser extent, so has my whole team. (I did all ten of the Bush Topics in 2015 and another ten in 2016.)  Also, we had fairly well-defined, simple search goals in most of the topics.

feedback_loops_human_compFor these reasons in many of these 2016 TREC document review projects the role of the machine and machine ranking became fairly small. In some that I handled it was reduced to a quality control, quality assurance method. The machine would pick up and catch a few documents that the lawyers alone had missed, but only a few. The machine thus had a slight impact on improved recall, but not much effect at all on precision, which was anyway very high. (More on this experience with easy search topics later in this essay when we talk about our Keyword Search insights.)

On a few of the 2016 TREC Topics the search targets were not well-defined. On these Topics our SME skills were necessarily minimized. Thus in these few Topics, even though the data itself was simple, we had to put greater reliance on the machine (in our case Mr. EDR) than on the attorney reviewers.

It bears repeating that the volume of emails has nothing to do with the ease or difficulty of the review project. This is a secondary question and is not dispositive as to how much weight you need to give to machine ranking. (Volume size may, however, have a big impact on project duration.)

We use IST, Not CAL

ist-smAnother insight in Balanced Hybrid in our new version 4.0 of Predictive Coding is what we call Intelligently Spaced Training, or IST™. See Part Two of this series for more detail on IST. We now use the term IST, instead of CAL, for two reasons:

1. Our previous use of the term CAL was only to refer to the fact that our method of training was continuous, in that it continued and was ongoing throughout a document review project. The term CAL has come to mean much more than that, as will be explained, and thus our continued use of the term may cause confusion.

2. Trademark rights have recently been asserted by Professors Grossman and Cormack, who originated this acronym CAL. As they have refined the use of the mark it now not only stands for Continuous Active Learning throughout a project, but also stands for a particular method of training that only uses the highest ranked documents.

Under the Grossman-Cormack CAL method the machine training continues throughout the document review project, as it does under our IST method, but there the similarities end. Under their CAL method of predictive coding the machine trains automatically as soon as a new document is coded. Further, the document or documents are selected by the software itself. It is a fully automatic process. The only role of the human is to say yes or no as to relevance of the document. The human does not select which document or documents to review next to say yes or no to. That is controlled by the algorithm, the machine. Their software always selects and presents for review the document or documents that it considers to have the highest probability of relevance, which have, of course, not already been coded.

The CAL method is only hybrid, like the e-Discovery Team method, in the sense of man and machine working together. But, from our perspective, it is not balanced. In fact, from our perspective the CAL method is way out of balance in favor of the machine. This may be the whole point of their method, to limit the human role as much as possible. The attorney has no role to play at all in selecting what document to review next and it does not matter if the attorney understands the training process. Personally, we do not like that. We want to be in charge and fully engaged throughout. We want the computer to be our tool, not our master.

sorry_dave_ai

Under our IST method the attorney chooses what documents to review next. We do not need the computer’s permission. We decide whether to accept a batch of high-ranking documents from the machine, or not. The attorney may instead find documents that they think are relevant from other methods. Even if the high ranked method of selection of training documents is used, the attorney decides the number of such documents to use and whether to supplement the machine selection with other training documents.

In fact, the only thing in common between IST and CAL is that both process continue throughout the life of a document review project and both are concerned with the Stop decision (when to when to stop the training and project). Under both methods after the Stopping point no new documents are selected for review and production. Instead, quality assurance methods that include sampling reviews are begun. If the quality assurance tests affirm that the decision to stop review was reasonable, then the project concludes. If they fail, more training and review are initiated.

Time_SpiralAside from the differences in document selection between CAL and IST, the primary difference is that under IST the attorney decides when to train.  The training does not occur automatically after each document, or specified number of documents, as in CAL, or at certain arbitrary time periods, as is common with other software. In the e-Discovery Team method of IST, which, again, stands for Intelligently Spaced (or staggered) Training, the attorney in charge decide when to train. We control the clock, the clock does not control us. The machine does not decide. Attorneys use their own intelligence to decide when to train the machine.

This timing control allows the attorney to observe the impact of the training on the machine. It is designed to improve the communication between man and machine. That is the double-loop learning process described in Part Two as part of the insights into Active Machine Learning. The attorney trains the machine and the machine is observed so that the trainer can learn how well the machine is doing. The attorney can learn what aspects of the relevance rule have been understood and what aspects still need improvement. Based on this student to teacher feedback the teacher is able to custom the next rounds of training to fit the needs of the student. This maximizes efficiency and effectiveness and is the essence of double-loop learning.

Pro Human Approach to Hybrid Man-Machine Partnership

ai_brainTo wrap up the new Balanced Hybrid insights we would like to point out that our terminology speaks of Training– ISTrather than Learning – CAL. We do this intentionally because training is consistent with our human perspective. That is our perspective whereas the perspective of the machine is to learn. The attorney trains and the machine learns. We favor humans. Our goal is empowerment of attorney search experts to find the truth (relevance), the whole truth (recall) and nothing but the truth (precision). Our goal is to enhance human intelligence with artificial intelligence. Thus we prefer a Balanced Hybrid approach with IST and not CAL.

This is not to say the CAL approach of Grossman and Cormack is not good and does not work. It appears to work fine. It is just a tad too boring for us and sometimes too slow. Overall we think it is less efficient and may sometimes even be less effective than our Hybrid Multimodal method. But, even though it is not for us, it may be well be great for many beginners. It is very easy and simple to operate. From language in the Grossman Cormack patents that appears to be what they are going for – simplicity and ease of use. They have that and a growing body of evidence that it works. We wish them well, and also their software and CAL methodology.

Robot_handshake

I expect Grossman and Cormack, and others in the pro-machine camp, to move beyond the advantages of simplicity and also argue safety issues. I expect them to argue that it is safer to rely on AI because a machine is more reliable than a human, in the same way that Google’s self-driving car is safer and more reliable than a human driven car. Of course, unlike driving a car, they still need a human, an attorney, to decide yes or no on relevance, and so they are stuck with human reviewers. They are stuck with a least a partial Hybrid method, albeit one favoring as much as possible the machine side of the partnership. We do not think the pro-machine approach will work with attorneys, nor should it. We think that only an unabashedly pro-human approach like ours is likely to be widely adopted in the legal marketplace.

The goal of the pro-machine approach of Professors Cormack and Grossman, and others, is to minimize human judgments, no matter how skilled, and thereby reduce as much as possible the influence of human error and outright fraud. This is a part of a larger debate in the technology world. We respectfully disagree with this approach, at least in so far as legal document review is concerned. (Personally I tend to agree with it in so far as the driving of automobiles is concerned.) We instead seek enhancement and empowerment of attorneys by technology, including quality controls and fraud detection. See Why the ‘Google Car’ Has No Place in Legal Search. No doubt you will be hearing more about this interesting debate in the coming years. It may well have a significant impact on technology in the law, the quality of justice, and the future of lawyer employment.

robots_newspaper

 

To be continued …


Predictive Coding 4.0 – Nine Key Points of Legal Document Review and an Updated Statement of Our Workflow – Part Two

September 18, 2016

Team_TRECIn Part One we announced the latest enhancements to our document review method, the upgrade to Predictive Coding 4.0. We explained the background that led to this upgrade – the TREC research and hundreds of projects we have done since our last upgrade a year ago. Millions have been spent to develop the software and methods we now use for Technology Assisted Review (TAR). As a result our TAR methods are more effective and simpler than ever.

The nine insights we will share are based on our experience and research. Some of our insights may be complicated, especially our lead insight on Active Machine Learning covered in this Part Two with our new description of ISTIntelligently Spaced Training. We consider IST the smart, human empowering alternative to CAL. If I am able to write these insights up here correctly, the obviousness of them should come through. They are all simple in essence. The insights and methods of Predictive Coding 4.0 document review are partially summarized in the chart below (which you are free to reproduce without edit).

predictive_coding_6-9

1st of the Nine Insights: Active Machine Learning

Our method is Multimodal in that it uses all kinds of document search tools. Although we emphasize active machine learning, we do not rely on that method alone. Our method is also Hybrid in that we use both machine judgments and human (lawyer) judgments. Moreover, in our method the lawyer is always in charge. We may take our hand off the wheel and let the machine drive for a while, but under our versions of Predictive Coding, we watch carefully. We remain ready to take over at a moment’s notice. We do not rely on one brain to the exclusion of another. See eg. Why the ‘Google Car’ Has No Place in Legal Search (caution against over reliance on fully automated methods of active machine learning). Of course the converse is also true, we never just rely on our human brain alone. It has too many limitations. We enhance our brain with predictive coding algorithms. We add to our own natural intelligence with artificial intelligence. The perfect balance between the two, the Balanced Hybrid, is another of insights that we will discuss later.

Active Machine Learning is Predictive Coding – Passive Analytic Methods Are Not

Even though our methods are multimodal and hybrid, the primary search method we rely on is Active Machine Learning. The overall name of our method is, after all, Predictive Coding. And, as any information retrieval expert will tell you, predictive coding means active machine learning. That is the only true AI method. The passive type of machine learning that some vendors use under the name Analytics is NOT the same thing as Predictive Coding. These passive Analytics have been around for years and are far less powerful than active machine learning.

concept-searches-brainThese search methods, that used to be called Concept Search, were a big improvement upon relying on keyword search alone. I remember talking about concepts search techniques in reverent terms when I did my first Legal Search webinar in 2006 with Jason Baron and Professor Doug Oard. That same year, Kroll Ontrack bought one of the original developers and patent holders of concept search, Engenium. For a short time in 2006 and 2007 Kroll Ontrack was the only vendor to have these concept search tools. The founder of Engenium, David Chaplin came with the purchase, and became Kroll Ontrack’s VP of Advanced Search Technologies for three years. (Here is an interesting interview of Chaplin that discusses what he and Kroll Ontrack were doing with advanced search analytic-type tools when he left in 2009.)

search_globalBut search was hot and soon boutique search firms like, Clearwell, Cataphora, Content Analyst (the company recently purchased by popular newcomer, kCura), and other e-discovery vendors developed their own concept search tools. Again, they were all using passive machine learning. It was a big deal ten years ago. For a good description of these admittedly powerful, albeit now dated search tools, see the concise, well-written article by D4’s Tom Groom, The Three Groups of Discovery Analytics and When to Apply Them.

Search experts and information scientists know that active machine learning, also called supervised machine learning, was the next big step in search after concept searches, which are, in programming language, also known as passive or unsupervised machine learning. I am getting out of my area of expertise here, and so am unable go into any details, other than present the below instructional chart by Hackbright Academy that sets forth key difference between supervised learning (predictive coding) and unsupervised (analytics, aka concept search).

machine_learning_algorithms

What I do know is that the bonafide active machine learning software in the market today all use either a form of Logistic Regression, including Kroll Ontrack, or SVM, which means Support Vector Machine.

e-Discovery Vendors Have Been Market Leaders in Active Machine Learning Software

Kroll_IRTAfter Kroll Ontrack absorbed the Engenium purchase, and its founder Chaplin completed his contract with Kroll Ontrack and moved on, Kroll Ontrack focused their efforts on the next big step, active machine learning, aka predictive coding. They have always been that kind of cutting edge company, especially when it comes to search, which is one reason they are one of my personal favorites. A few of the other, then leading e-discovery vendors did too, including especially Recommind and the Israeli based search company, Equivo. Do not get me wrong, the concept search methods, now being sold under the name of TAR Analytics, are powerful search tools. They are a part of our multimodal tool-kit and should be part of yours. But they are not predictive coding. They do not rank documents according to your external input, your supervision. They do not rely on human feedback. They group documents according to passive analytics of the data. It is automatic, unsupervised. These passive analytic algorithms can be good tools for efficient document review, but they not active machine learning and are nowhere near as powerful.

ghosts

Search Software Ghosts

Many of the software companies that made the multi-million dollar investments necessary to go to the next step and build document review platforms with active machine learning algorithms have since been bought out by big-tech and repurposed out of the e-discovery market. They are the ghosts of legal search past. Clearwell was purchased by Symantec and has since disappeared. Autonomy was purchased by Hewlett Packard and has since disappeared. Equivio was purchased by Microsoft and has since disappeared. See e-Discovery Industry Reaction to Microsoft’s Offer to Purchase Equivio for $200 Million – Part One and Part Two. Recommind was recently purchased by OpenText and, although it is too early to tell for sure, may also soon disappear from e-Discovery.

Slightly outside of this pattern, but with the same ghosting result, e-discovery search company, Cataphora, was bought by Ernst & Young, and has since disappeared. The year after the acquisition, Ernst & Young added predictive coding features from Cataphora to its internal discovery services. At this point, all of the Big Four Accounting Firms, claim to have their own proprietary software with predictive coding. Along the same lines, at about the time of the Cataphora buy-out, consulting giant FTI purchased another e-discovery document review company, Ringtail Solutions (known for its petri dish like visualizations). Although not exactly ghosted by FTI from the e-discovery world after the purchase, they have been absorbed by the giant FTI.

microsoft_acquiresOutside of consulting/accountancy, in the general service e-discovery industry for lawyers, there are, at this point (late 2016) just a few document review platforms left that have real active machine learning. Some of the most popular ones left behind certainly do not. They only have passive learning analytics. Again, those are good features, but they are not active machine learning, one of the nine basic insights of Predictive Coding 4.0 and a key component of the e-Discovery Team’s document review capabilities.

predictive_coding_9_2

The power of the advanced, active learning technologies that have been developed for e-discovery is the reason for all of these acquisitions by big-tech and the big-4 or 5. It is not just about wild overspending, although that may well have been the case for Hewlett Packard payment of $10.3 Billion to buy Autonomy. The ability to do AI-enhanced document search and review is a very valuable skill, one that will only increase in value as our data volumes continue to explode. The tools used for such document review are also quite valuable, both inside the legal profession and, as the ghostings prove, well beyond into big business. See e-Discovery Industry Reaction to Microsoft’s Offer to Purchase Equivio for $200 MillionPart Two.

The indisputable fact that so many big-tech companies have bought up the e-discovery companies with active machine learning software should tell you a lot. It is a testimony to the advanced technologies that the e-discovery industry has spawned. When it comes to advanced search and document retrieval, we in the e-discovery world are the best in the world my friends, primarily because we have (or can easily get) the best tools. Smile.

usain-bolt-smiling

Search is king of our modern Information Age culture. See Information → Knowledge → Wisdom: Progression of Society in the Age of ComputersThe search for evidence to peacefully resolve disputes is, in my most biased opinion, the most important search of all. It sure beats selling sugar water. Without truth and justice all of the petty business quests for fame and fortune would crumble into anarchy, or worse, dictatorship.

With this background it is easy to understand why some of the e-discovery vendors left standing are not being completely candid about the capabilities of their document review software. (It is called puffing and is not illegal.) The industry is unregulated and, alas, most of our expert commentators are paid by vendors. They are not independent. As a result, many of the lawyers who have tried what they thought was predictive coding, and had disappointing results, have never really tried predictive coding at all. They have just used slightly updated concept search.

Ralph Losey with this "nobody read my blog" sad shirtAlternatively, some of the disappointed lawyers may have used one of the many now-ghosted vendor tools. They were all early version 1.0 type tools. For example, Clearwell’s active machine learning was only on the market for a few months with this feature before they were bought and ghosted by Symantec. (I think Jason Baron and I were the first people to see an almost completed demo of their product at a breakfast meeting a few months before it was released.) Recommind’s predictive coding software was well-developed at the time of their sell-out, but not its methods of use. Most of its customers can testify as to how difficult it is to operate. That is one reason that OpenText was able to buy them so cheaply, which, we now see, was part of their larger acquisition plan culminating in the purchase of Dell’s EMC document management software.

All software still using early methods, what we call version 1.0 and 2.0 methods based on control sets, are cumbersome and hard to operate, not just Recommind’s system. I explained this in my article last year, Predictive Coding 3.0. I also mentioned in this article that some vendors with predictive coding would only let you use predictive coding for search. It was, in effect, mono-modal. That is also a mistake. All types of search must be used – multimodal – for the predictive coding type of search to work efficiently and effectively. More on that point later.

Maura Grossman Also Blows the Whistle on Ineffective “TAR tools”

Maura Grossman aka "Mr. Grossman" to her email friends

Maura Grossman, who is now an independent expert in this field, made many of these same points in a recent interview with Artificial Lawyer, a periodical dedicated to AI and the Law. AI and the Future of E-Discovery: AL Interview with Maura Grossman (Sept. 16, 2016). When asked about the viability of the “over 200 businesses offering e-discovery services” Maura said, among other things:

In the long run, I am not sure that the market can support so many e-discovery providers …

… many vendors and service providers were quick to label their existing software solutions as “TAR,” without providing any evidence that they were effective or efficient. Many overpromised, overcharged, and underdelivered. Sadly, the net result was a hype cycle with its peak of inflated expectations and its trough of disillusionment. E-discovery is still far too inefficient and costly, either because ineffective so-called “TAR tools” are being used, or because, having observed the ineffectiveness of these tools, consumers have reverted back to the stone-age methods of keyword culling and manual review.

caveman lawyerNow that Maura is no longer with the conservative law firm of Wachtell Lipton, she has more freedom to speak her mind about caveman lawyers. It is refreshing and, as you can see, echoes much of what I have been saying. But wait, there is still more that you need to hear from the interview of new Professor Grossman:

It is difficult to know how often TAR is used given confusion over what “TAR” is (and is not), and inconsistencies in the results of published surveys. As I noted earlier, “Predictive Coding”—a term which actually pre-dates TAR—and TAR itself have been oversold. Many of the commercial offerings are nowhere near state of the art; with the unfortunate consequence that consumers have generalised their poor experiences (e.g., excessive complexity, poor effectiveness and efficiency, high cost) to all forms of TAR. In my opinion, these disappointing experiences, among other things, have impeded the adoption of this technology for e-discovery. …

ulNot all products with a “TAR” label are equally effective or efficient. There is no Consumer Reports or Underwriters Laboratories (“UL”) that evaluates TAR systems. Users should not assume that a so-called “market leading” vendor’s tool will necessarily be satisfactory, and if they try one TAR tool and find it to be unsatisfactory, they should keep evaluating tools until they find one that works well. To evaluate a tool, users can try it on a dataset that they have previously reviewed, or on a public dataset that has previously been labelled; for example, one of the datasets prepared for the TREC 2015 or 2016 Total Recall tracks. …

She was then asked by the Artificial Lawyer interviewer (name never identified), which is apparently based in the UK, another popular question:

As is often the case, many lawyers are fearful about any new technology that they don’t understand. There has already been some debate in the UK about the ‘black box’ effect, i.e., barristers not knowing how their predictive coding process actually worked. But does it really matter if a lawyer can’t understand how algorithms work?

Maura_Goog_GlassesThe following is an excerpt of Maura’s answer. Suggest you consult the full article for a complete picture. AI and the Future of E-Discovery: AL Interview with Maura Grossman (Sept. 16, 2016). I am not sure whether she put on her Google Glasses to answer (probably not), but anyway, I rather like it.

Many TAR offerings have a long way to go in achieving predictability, reliability, and comprehensibility. But, the truth that many attorneys fail to acknowledge is that so do most non-TAR offerings, including the brains of the little black boxes we call contract attorneys or junior associates. It is really hard to predict how any reviewer will code a document, or whether a keyword search will do an effective job of finding substantially all relevant documents. But we are familiar with these older approaches (and we think we understand their mechanisms), so we tend to be lulled into overlooking their limitations.

The brains of the little black boxes we call contract attorneys or junior associates. So true. We will go into that more throughly in our discussion of the GIGO & QC insight.

Recent Team Insights Into Active Machine Learning

To summarize what I have said so far, in the field of legal search, only active machine learning:

  • effectively enhances human intelligence with artificial intelligence;
  • qualifies for the term Predictive Coding.

I want to close on this discussion of active machine learning with one more insight. This one is slightly technical, and again, if I explain it correctly, should seem perfectly obvious. It is certainly not new, and most search experts will already know this to some degree. Still, even for them, there may some nuances to this insight that they have not thought of. It can be summarized as follows: active machine learning should have a double feedback loop with active monitoring by the attorney trainers.

robot-friend

feedback_loopsActive machine learning should create feedback for both the algorithm (the data classified) AND the human managing the training. Both should learn, not just the robot. They should, so to speak, be friends. They should get to know each other

Many predictive coding methods that I have read about, or heard described, including how I first used active machine learning, did not sufficiently include the human trainer in the feedback loop.  They were static types of training using single a feedback loop. These methods are, so to speak, very stand-offish, aloof. Under these methods the attorney trainer does not even try to understand what is going on with the robot. The information flow was one-way, from attorney to machine.

Mr_EDRAs I grew more experienced with the EDR software I started to realize that it is possible to start to understand, at least a little, what the black box is doing. Logistic based AI is a foreign intelligence, but it is intelligence. After a while you start to understand it. So although I started just using one-sided machine training, I slowly gained the ability to read how EDR was learning. I then added another dimension, another feedback loop that was very interesting one indeed. Now I not only trained and provided feedback to the AI as to whether the predictions of relevance were correct, or not, but I also received training from the AI as to how well, or not, it was learning. That in turn led to the humorous personification of the Kroll Ontrack software that we now call Mr. EDR. See MrEDR.com. When we reached this level, machine training became a fully active, two-way process.

We now understand that to fully supervise a predictive coding process you to have a good understanding of what is happening. How else can you supervise it? You do not have to know exactly how the engine works, but you at least need to know how fast it is going. You need a speedometer. You also need to pay attention to how the engine is operating, whether it is over-heating, needs oil or gas, etc. The same holds true to teaching humans. Their brains are indeed mysterious black boxes. You do not need to know exactly how each student’s brain works in order to teach them. You find out if your teaching is getting through by questions.

For us supervised learning means that the human attorney has an active role in the process. A role where the attorney trainer learns by observing the trainee, the AI in creation. I want to know as much as possible, so long as it does not slow me down significantly.

In other methods of using predictive coding that we have used or seen described the only role of the human trainer is to say yes or no as to the relevance of a document. The decision as to what documents to select for training has already been predetermined. Typically it is the highest ranked documents, but sometimes also some mid-ranked “uncertain documents” or some “random documents” are added in the mix. The attorney
has no say in what documents to look at. They are all fed to him or her according to predetermined rules. These decision making rules are set in ralph_boredadvance and do not change. These active machine learning methods work, but they are slow, and less precise, not to mention boring as hell.

The recall of these single-loop passive supervision methods may also not be as good. The jury is still out on that question. We are trying to run experiments on that now, although it can be hard to stop yawning. See an earlier experiment on this topic testing the single loop teaching method of random selection: Borg Challenge: Report of my experimental review of 699,082 Enron documents using a semi-automated monomodal methodology.

These mere yes or no, limited participation methods are hybrid Man-Machine methods, but, in our opinion, they are imbalanced towards the Machine. (Again, more on the question of Hybrid Balance will be covered in the next installment of this article.) This single versus dual feedback approach seems to be the basic idea behind the Double Loop Learning approach to human education depicted in the diagram below. Also see Graham Attwell, Double Loop Learning and Learning Analytics (Pontydysgu, May 4, 2016).

double-loop-learning

To quote Wikipedia:

The double loop learning system entails the modification of goals or decision-making rules in the light of experience. The first loop uses the goals or decision-making rules, the second loop enables their modification, hence “double-loop.” …

Double-loop learning is contrasted with “single-loop learning”: the repeated attempt at the same problem, with no variation of method and without ever questioning the goal. …

Double-loop learning is used when it is necessary to change the mental model on which a decision depends. Unlike single loops, this model includes a shift in understanding, from simple and static to broader and more dynamic, such as taking into account the changes in the surroundings and the need for expression changes in mental models.

double-loop-learning2

The method of active machine learning that we use in Predictive Coding 4.0 is a type of double loop learning system. As such it is ideal for legal search, which is inherently ad hoc, where even the understanding of relevance evolves as the project develops. As Maura noted near the end of the Artificial Lawyer interview:

… e-discovery tends to be more ad hoc, in that the criteria applied are typically very different for every review effort, so each review generally begins from a nearly zero knowledge base.

The driving impetus behind our double feedback look system is to allow for training document selection to vary according to the circumstances encountered. Attorneys select documents for training and then observe how these documents impact the AI’s overall ranking of the documents. Based on this information decisions are then made by the attorney as to which documents to next submit for training. A single fixed mental model is not used, such as only submitting the ten highest ranked documents for training.

The human stays involved and engaged and selects the next documents to add to the training based on what she sees. This makes the whole process much more interesting. For example, if I find a group of relevant spreadsheets by some other means, such as a keyword search, then, when I add these document to the training, I observe how these documents impact the overall ranking of the dataset. For instance, did this training result in an increase of relevance ranking of other spreadsheets? Was the increase nominal or major? How did it impact the ranking of other documents? For instance, were emails with a lot of numbers in them suddenly much higher ranked? Overall, was this training effective? Were the documents in fact relevant as predicted that moved up in rank to the top, or near top of probable relevance? What was the precision rate like for these documents? Does the AI now have a good understanding of relevance of spreadsheets, or need more training on that type of document? Should we focus our search on other kinds of documents?

You see all kinds of variations on that. If the spreadsheet understanding (ranking) is good, how does it compare to its understanding (correct ranking) of Word Docs or emails? Where should I next focus my multimodal searches? What documents should I next assign to my reviewers to read and make a relevancy determination? These kind of considerations keep the search interesting, fun even. Work as play is the best kind. Typically we simply assign the documents for attorney review that have the highest ranking (which is the essence of what Grossman and Cormack call CAL), but not always. We are flexible. We, the human attorneys, are the second positive feedback loop.

EDR_lookWe like to remain in charge of teaching the classifier, the AI. We do not just turn it over to the classifier to teach itself. Although sometimes, when we are out of ideas and are not sure what to do next, we will do exactly that. We will turn over to the computer the decision of what documents to review next. We just go with his top predictions and use those documents to train. Mr. EDR has come through for us many times when we have done that. But this is more of an exception, than the rule. After all, the classifier is a tabula rasa. As Maura put it: each review generally begins from a nearly zero knowledge base. Before the training starts, it knows nothing about document relevance. The computer does not come with built-in knowledge of the law or relevance. You know what you are looking for. You know what is relevant, even if you do not know how to find it, or even whether it exists at all. The computer does not know what you are looking for, aside from what you have told it by your yes-no judgments on particular documents. But, after you teach it, it knows how to find more documents that probably have the same meaning.

raised_handsBy observation you can see for yourself, first hand, how your training is working, or not working. It is like a teacher talking to their students to find out what they learned from the last assigned reading materials. You may be surprised by how much, or how little they learned. If the last approach did not work, you change the approach. That is double-loop learning. In that sense our active monitoring approach it is like continuous dialogue. You learn how and if the AI is learning. This in turn helps you to plan your next lessons. What has the student learned? Where does the AI need more help to understand the conception of relevance that you are trying to teach it.

Only_Humans_Need_ApplyThis monitoring of the AI’s learning is one of the most interesting aspects of active machine learning. It is also a great opportunity for human creativity and value. The inevitable advance of AI in the law can mean more jobs for lawyers overall, but only for those able step up and change their methods. The lawyers able to play the second loop game of active machine learning will have plenty of employment opportunities. See eg. Thomas H. Davenport, Julia Kirby, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (Harper 2016).

Going down into the weeds a little bit more, our active monitoring dual feedback approach means that when we use Kroll Ontrack’s EDR software, we adjust the settings so that new learning sessions are not created automatically. They only run when and if we click on the Initiate Session button shown in the EDR screenshot below (arrow and words were added). We do not want the training to go on continuously in the background (typically meaning at periodic intervals of every thirty minutes or so.) We only want the learning sessions to occur when we say so. In that way we can know exactly what documents EDR is training on during a session. Then, when that training session is complete, we can see how the input of those documents has impacted the overall data ranking.  For instance, are there now more documents in the 90% or higher probable relevance category and if so, how many? The picture below is of a completed TREC project. The probability rankings are on the far left with the number of documents shown in the adjacent column. Most of the documents in the 290,099 collection of Bush email were in the 0-5% probable relevant ranking not included in the screen shot.

edr_initiate_session

This means that the e-Discovery Team’s active learning is not continuous, in the sense of always training. It is instead intelligently spaced. That is an essential aspect of our Balanced Hybrid approach to electronic document review. The machine training only begins when we click on the “Initiate Session” button in EDR that the arrow points to. It is only continuous in the sense that the training continues until all human review is completed. The spaced training, in the sense of staggered  in time, is itself an ongoing process until the production is completed. We call this Intelligently Spaced Training or IST. Such ongoing training improves efficiency and precision, and also improves Hybrid human-machine communications. Thus, in our team’s opinion, IST is a better process of electronic document review than training automatically without human participation, the so-called CAL approach promoted (and recently trademarked) by search experts and professors, Maura Grossman and Gordon Cormack.

ist-sm

Exactly how we space out the timing of training in IST is a little more difficult to describe without going into the particulars of a case. A full, detailed description would require the reader to have intimate knowledge of the EDR software. Our IST process is, however, software neutral. You can follow the IST dual feedback method of active machine learning with any document review software that has active machine learning capacities and also allows you to decide when to initiate a training session. (By the way, a training session is the same thing as a learning session, but we like to say training, not learning, as that takes the human perspective and we are pro-human!) You cannot do that if the training is literally continuous and cannot be halted while you input a new batch of relevance determined documents for training.

The details of IST, such as when to initiate a training session, and what human coded documents to select next for training, is an ad hoc process. It depends on the data itself, the issues involved in the case, the progress made, the stage of the review project and time factors. This is the kind of thing you learn by doing. It is not rocket science, but it does help keep the project interesting. Hire one of our team members to guide your next review project and you will see it in action. It is easier than it sounds. With experience Hybrid Multimodal IST becomes an intuitive process, much like riding a bicycle.

ralph_trecTo summarize, active machine learning should be a dual feedback process with double-loop learning. The training should continue throughout a project, but it should be spaced in time so that you can actively monitor the progress, what we call IST. The software should learn from the trainer, of course, but the trainer should also learn from the software. This requires active monitoring by the teacher who reacts to what he or she sees and adjusts the training accordingly so as to maximize recall and precision.

This is really nothing more than a common sense approach to teaching. No teacher who just mails in their lessons, and does not pay attention to the students, is ever going to be effective. The same is true for active machine learning. That’s the essence of the insight. Simple really.

Next, in Part Three, I will address the related insights of Balanced Hybrid.

To be Continued …


Predictive Coding 4.0 – Nine Key Points of Legal Document Review and an Updated Statement of Our Workflow – Part One

September 11, 2016

This blog introduces the e-Discovery Team’s latest insights and methods of document review. We call this Predictive Coding 4.0 because it substantially improves upon, and replaces the methods and insights we announced in our October 2015 publication – Predictive Coding 3.0. In that two-part blog we explained the history of predictive coding software and methods in legal review, including versions 1.0 and 2.0. Then we described our new version 3.0 in some detail. Since that publication we have developed more enhancements to our methods, including many new, innovative uses of  the predictive coding features of Kroll Ontrack’s EDR software. We even developed some new features not related to predictive coding. (Try out the new Folder Similar search in EDR for example.) Most of our new insights, just like our prior 3.0 version methodologies, can also be used on other software platforms. To use all of the features, however, the software will have to have bona fide active machine learning capacities. Most do not. More on that later.

Team_TREC_2These improvements naturally evolved to a certain degree as part of the e-Discovery Team members normal work supervising hundreds, maybe even thousands of document review projects over the past year. But the new insights that require us to make a complete restatement, a new Version 4.0, arose just recently. Major advances were attained as part of an intensive three months of experiments, all conducted outside of our usual legal practice and document reviews. The e-Discovery Team doing this basic research consisted of myself and several of Kroll Ontrack’s top document review specialists, including especially Jim Sullivan and Tony Reichenberger. They have now fully mastered the e-discovery team search and review Hybrid Multimodal methodologies. As far as I can see, at this point in the race for the highest quality legal document review, no one else comes even close to their skill level. Yes, e-discovery is highly competitive, but they trained hard and are now looking back and smiling.

usain-bolt-smiling

The insights we gained, and the skills we honed, including speed, did not come easily. It took full time work on client projects all year, plus three full months of research, often in lieu of real summer vacations (my wife is still waiting). This is hard work, but we love it. See: Why I Love Predictive Coding. This kind of dedication of time and resources by an e-discovery vendor or law firm is unprecedented. There is a cost to attain the research benefits realized, both hard out-of-pocket costs and lost time. So I hope you understand that we are only going to share some of our techniques. The rest we will keep as trade-secrets. (Retain us and watch. Then you can see them in action.)

mark_williams

Mark Williams, CEO Kroll Ontrack

Kroll Ontrack understands the importance of pure research and enthusiastically approved these expenditures. (My thanks again to CEO Mark Williams, a true visionary leader in this industry who approved and supported the research program.) I suggest you ask your vendor, or law firm, how much time they spent last year researching and experimenting with document review methods? As far as we know, the only other vendor with an active research program is Catalyst, whose work is also to be commended. (No one else showed up for TREC.) The only other law firm we know of is Maura Grossman’s new solo practice. Her time spent with research is also impressive.

The results we attained certainly make this investment worthwhile, even if many in the profession do not realize it, much less appreciate it. They will in time, so will the consumers. This is a long term investment. Pure research is necessary for any technology company, including all companies in the e-Discovery field. The same holds true, albeit to a lesser extent, to any law firm claiming to have technological superiority.

mad scientistExperience from handling live projects alone is too slow an incubator for the kind of AI breakthrough technologies we are now using. It is also too inhibiting. You do not experiment on important client data or review projects. Any expert will improvise somewhat during such projects to match the circumstances, and sometimes do post hoc analysis. But such work on client projects alone is not enough. Pure research is needed to continue to advance in AI-enhanced review. That is why the e-Discovery Team spent a substantial part of our waking hours in June, July and August 2016 working on experiments with Jeb Bush email.  The Jeb Bush email collection was our primary laboratory this year. As a result of the many new things we learned, and new methods practiced and perfected, we have now reached a point where a complete restatement of our method is in order. Thus we here release Predictive Coding 4.0.

NIST-Logo_RLOur latest breakthroughs this summer primarily came out of the e-Discovery Team’s participation in the annual Text Retrieval Conference, aka TREC, sponsored by the National Institute of Standards and Technology. This is the 25th year of the TREC event. We were honored to again participate, as we did last year, in the Total Recall Track of TREC. This is the closest Track that TREC now offers to a real legal review project. It is not a Legal Track, however, and so we necessarily did our own side-experiments, and had our own unique approach different from the Universities that participated. The TREC leadership of the Total Recall Track was once again in the capable hands of Maura Grossman, Gordon Cormack and other scientists.

This blog will not report on the specifics of the 2016 Total Recall Track. That will come at a later time after we finish analyzing the enormous amount of data we generated and submit our formal reports to TREC. In any event, the TREC related work we did this Summer went beyond the thirty-four research topics included in the TREC event. It went well beyond the 9,863,366 documents we reviewed with Mr. EDR’s help as part of the formal submittals. Countless more documents were reviewed for relevance if you include our side-experiments.

MrEdr_CapedAt the same time that we did the formal tests specified by the Total Recall Track we did multiple side-experiments of our own. Some of these tests are still ongoing. We did so to investigate our own questions that are unique to legal search and thus beyond the scope of the Total Recall Track. We also performed experiments to test unique attributes of Kroll Ontrack’s EDR software. It uses a proprietary type of logistic regression algorithm that was awarded a patent this year. Way to go KO and Mr. EDR!

Although this blog will not report on our TREC experiments per se, we will share the bottom line, the take-aways of this testing. Not everything will be revealed. We keep some of our methods and techniques trade-secret.

fcsi_forensicsWe will also not be discussing in this multi-part blog our future plans and spin-off projects. Let’s just say for now that we have several in mind. One in particular will, I think, be very exciting for all attorneys and paralegals who do document review. Maybe even fun for those of you who, like us, are really into and enjoy a good computer search. You know who you are! If my recommendations are accepted, we will open that one up to all of our fellow doc-review freaks. I will say no more at this point, but watch for announcements in the coming year from Kroll Ontrack and me. We are having too much fun here not to share some of the good times.

Even if we did adopt 100% transparency on our methods, it would take a book to write it all down, and it would still be incomplete. Many things can only be learned by doing, especially methods. Document review is, after all, a part of legal practice. As the scientists like to put it, legal search is essentially ad hoc. It changes and is customized to fit the particular evidence search assignments at hand. But we will try to share all of the basic insights. They have all been discussed here before. The new insights we gained are more like a deepening understanding and matter of emphasis. They are refinements, not radical departures, although some are surprising.

Nine Insights Concerning the Use of Predictive Coding in Legal Document Review

The diagram below summarizes the nine basic insights that have come out of our work this year. These are the key concepts that we now think are important to understand and implement. [Just like the 8-Step Workflow diagram above, this, and other diagrams in this blog may be freely used with attribution. But please do not change anything without my permission. I am also happy to provide you with higher resolution graphics if needed for presentation or publication purposes.

The diagrams above and following will be explained in detail throughout the rest of this multipart blog, as will the restated 8-Step Workflow shown at the top of the page. These are not new concepts. I have discussed most of these here before. I am confident that all readers will be able to follow along as I set forth the new nuances we learned.

Although these concepts are all familiar, some of our deepened understanding of these concepts may surprise you. Some were surprising to us. These insights include several changes in thinking on our part. Some of the research results we saw were unexpected. But we follow the data. Our opinions are always held lightly. I have argued both sides of a legal issue too many times as a lawyer to fall into that trap. Our thinking follows the evidence, not our preconceptions. That is, after all, the whole point of research. Schedule permitting, we are also happy to provide in-person or online presentations that explain these concept-summary diagrams. If retained, you can also see it in action.

Although these insights and experiments were derived using Kroll Ontrack EDR software, they are essentially vendor neutral. The methods will work on any full-featured document review platform, but especially those that includes bona fide active machine learning abilities, aka Predictive Coding. As all experts in this field know, many of the most popular document review platforms do not have these features, even those stating they use Analytics. Active Machine Learning is very different, and far more advanced than Analytics, the early forms of which were called Concept Search. This type of machine learning is passive and clearly is not predictive coding. It has its place in any multimodal system such as ours, and can be a powerful feature to improve search and review. But such software is incomplete and cannot meet the standards and capability of software that includes active machine learning. Only full featured document review platforms with active machine learning abilities can use all of the Predictive Coding 4.0 methods described here.

truth-to-powerSorry dear start-up vendors, and others, but that’s the truth. Consumers, you get what you pay for. You know that. Not sure? Get the help of an independent expert advisor before you make substantial investments in e-discovery software or choose a vendor for a major project. Also, if you have tried predictive coding, or what you were told was advanced TAR, whatever the hell that is, and it did not work well, do not blame yourself. It could be the software. Or if not the software, then the antiquated version 1.0 or 2.0 methods used. There is a lot of bullshit out there. Excuse my French. There always has been when it comes to new technology. It does, however, seems especially prevalent in the legal technology field. Perhaps they think we lawyers are naive and technologically gullible. Do not be fooled. Again, look to an independent consultant if you get confused by all the vendor claims.

ralph_and_lexieContrary to what some vendors will tell you (typically the ones without bona fide predictive coding features), predictive coding 3.0, and now 4.0, methods are not rocket science. You do not have to be a TAR-whisperer or do nothing but search, like my A-team for TREC. With good software it is not really that hard at all. These methods do, however, require an attorney knowledgable in e-discovery and comfortable with software. This is not for novices. But every law firm should anyway have attorneys with special training and experience in technology and e-discovery. For instance, if you practice in the Northern District of California, an e-discovery liaison with such expertise is required in most cases. See Guidelines for the Discovery of Electronically Stored Information. Almost half of the Bar Associations in the U.S. require basic technology competence as an ethical imperative. See eg. ABA Model Rule 1.1, Comment [8] and Robert Ambrogi’s list of 23 states, and counting, that now require such competence. (My own law firm has had an e-discovery liaison program in place since 2010, which I lead and train. I am proud to say that after six years of work it is now a great success.) So no, you do not have to be a full-time specialist, like the members of my TREC e-Discovery team, to successfully use AI-enhanced review, which we call Hybrid Multimodal. This is especially true when you work with vendors like Kroll Ontrack, Catalyst and others that have teams of special consultants to guide you. You just have to pick your vendors wisely.

To be continued …


Scientific Proof of Law’s Overreliance On Reason: The “Reasonable Man” is Dead and the Holistic Lawyer is Born

July 17, 2016

brain_gears_NOLast month I wrote about the place of reason in the law. The Law’s “Reasonable Man,” Judge Haight, Love, Truth, Justice, “Go Fish” and Why the Legal Profession Is Not Doomed to be Replaced by Robots. That article discussed how reasonability is the basis of the law, but that it is not objective. It depends on many subjective factors, on psychology. This article elaborates on this key point. The Law’s Reasonable Man is a fiction. He or she does not exist. Never has, never will. All humans, including us lawyers, are much more complex than that. We need to recognize this. We need to replace the Law’s reliance on reason alone with a more realistic multidimensional holistic approach.

Although logic and reasoning are important, we have many other important capacities, including empathy, intuition and imagination. All of these capacities are required for the practice of law. That is why lawyers cannot be replaced by robots. The fact the Reasonable Man is a fiction is something we lawyers should celebrate, not sweep under the carpet.

Most human decisions are not even based on reason. Quaint notions to the contrary are derived from the 18th Century Age of Reason. They are completely out of touch with reality. They are contrary to what science today is telling us about how humans process information and reach decisions.

frozenbrainsScientific research shows that the cornerstone of the Law – Reasonability – is not solid granite many had thought. There are no hard gears in our head, just soft, gelatinous, pinkish-beige matter. (Our brain is only soft grey matter when dead.) The ratiocination abilities of the brain are just one small part of its many incredible capacities. (For example, MIT scientists have shown that we can identify images seen for as little as 13 milliseconds, 13/1,000ths of one second.) We are far more than just rational, and that is a good thing.

Going Beyond the Age of Enlightenment Into the Modern Era of Science

Only_Humans_Need_ApplyThis article will offer proof that the Law’s Reasonable Man is dead. This is a cause for optimism because, as noted, if we were just reason-based workers, then our functions would soon be automated. We would soon all be out of work. See eg. Thomas H. Davenport, Julia Kirby, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (Harper 2016). Although most lawyers in the profession do not know it yet, the non-reasoning aspects of the Law are its most important parts. The reasoning aspects of legal work can be augmented. That is certain. So will other aspects, like reading comprehension. But the other aspects of our work, the aspects that require more than mere reason, are what makes the Law a human profession. These job functions will survive the surge of AI.

If you want to remain a winner in future Law, grow these aspects. Only losers will hold fast to reason. Letting go of the grip of the Reasonable Man, by which many lawyers are now strangled, will make you a better lawyer and, at the same time, improve your job security.

In today’s AI driven economy we all need to change our work to include more of our human capacities than mere reason. This is transforming all work, not just legal. We are far more than a thinking machine. We must open our eyes and see the truth. That is the true meaning and ultimate conclusion of the Age of Enlightenment.

Schrodinger's CatScience based on reason and the experimental method has taken Man beyond the rational, has shown the limitations of reason. Just as the evidence from physics experiments forced scientists to go beyond Newtonian Causality, and required them to embrace the seemingly irrational truth of Relativity and Quantum Mechanics, so too must the Law now evolve its thinking and procedures. As proof for this proposition in this chapter I will proffer the testimony of one expert witness, a noted MIT and Duke University Psychologist and Behavioral Economist.

The Legal Profession Must Awaken from the Daydream of Rationality

Reasonable_guageMy last blog, The Law’s “Reasonable Man”, laid the foundation for the introduction of this evidence. I noted how the law is based on the assumption that people make reasoned decisions and are capable of acting in a reasonable manner. I offered preliminary evidence that this assumption is contrary to the findings of research psychologists. I referred to a recent article by one such psychologist, Herb Roitblat: The Schlemiel and the Schlimazel and the Psychology of Reasonableness (Jan. 10, 2014, LTN) (link is to republication by a vendor without attribution).

I will now offer further, more detailed proof that humans do not act solely out of reason. Some just delude themselves into thinking so. I will then argue that these findings require us to make fundamental reforms to our system of justice. These reforms will both improve the our justice system and ensure the survival of the legal profession. Lawyers will remain, but they will look and work much differently than they do today. They will be augmented by AI, but not automated and replaced by AI. Productivity and efficiency will go through the roof. Our system of justice will vastly improve. To get there the profession will first have to awaken from the daydream of rationality. This article is designed as a wake up call.

This evidence of logic’s limitations is abundant. With only a little search I am sure you will find much more proof than I will now proffer. Many of you already know this from long experience in the courtrooms and law offices of the world, but may now have heard of the scientific proof. The evidence proves that the old assumptions on human reasonability, assumptions built centuries ago when the Age of Reason first began, are false. The evidence shows that the Reasonable Man is a legal fiction.

As Exhibit “A” to the assumption busting proposition I rely on the work of Dan Ariely, a Professor of Psychology and Behavioral Economics at Duke University. As an introduction to his work I ask readers to stop and take a few minutes, right now, to watch the TED video by Professor Ariely, Are We In Control of Our Own Decisions? He refers to his many scientific experiments at MIT and Duke that show we are not in control of many of our own decisions, even seemingly simple ones. These experiments prove my point.

Predictable Irrationality and Swearing on Bibles

Need more proof? Then please consider additional testimony from Professor Ariely on predictable irrationality. It is on another video called We’re All Predictably Irrational. This discourse even mentions every e-discovery lawyer’s favorite company, Enron, and examines our basic moral code, our personal fudge factor. Dan has conducted many experiments on the all too human tendency to cheat and lie, if only just a little, and the moving grey line between acceptable and unacceptable behavior. This is the line that the Law is constantly asked to draw, and to evaluate. These psychological insights are important to all lawyers, especially discovery lawyers, of the “e” only type like me, or not. Again, please listen carefully and consider the implications of these findings on the Law.

One interesting finding from Professor Ariely’s scientific experiments on cheating, one that you can easily miss in the predictable irrationality video, is that asking people to swear on a Bible significantly reduces cheating. This even works for atheists! I kid you not. Perhaps we should bring back the old tradition of requiring all witnesses to swear on a bible before beginning their testimony?

Ralph_swearing_oath_bibleI have done this myself long ago when I was out taking depositions as a young lawyer. In the early eighties many court reporters in rural counties of Florida would still pull out a Bible before a deposition began (they all used to carry them around for that purpose, and yes, that was way before they started carrying around computers). The court reporter would then ask the deponent to raise their right hand and put their left hand on the Bible. All the witnesses I saw instantly complied, thinking erroneously that this was a legal requirement. They placed their hand on the Bible, some nervously, and some like they did that all the time. Then they were asked to solemnly swear on the Bible that they would tell the truth, the whole truth and nothing but the truth so help me God. They did as asked by the serious court reporter, and some seemed pretty impressed by the whole ceremony. I recall that overall the testimony from these witnesses was pretty good, meaning less lies than usual.

I only saw this done a few times, and, as a typical arrogant big city lawyer (yes, out in the rural areas where they were still doing this, they all thought of Orlando as a big city), I dismissed it as a quaint old custom. But now science shows that it works.

What are the implications of these findings about human behavior? Maybe we should bring back Bibles into the courtrooms? Or at least bring back a bunch of solemn oaths? If we do not require swearing on or to a Bible, due to Church and State, or whatever, then perhaps we should ask people giving testimony to swear on something else. Most anything seems to work, even if it does not really exist. Dan Ariely’s experiments found that it even worked to have MIT students swear on an honor code that didn’t exist. Maybe asking lawyers to swear on their ethics codes would work too? Maybe that is the next reform in the procedural rules we should push for. Maybe we should update Rule 603 of the Federal Rules of Evidence:

Before testifying, a witness must give an oath or affirmation to testify truthfully. It must be in a form designed to impress that duty on the witness’s conscience.

prisoner_ralph_chainsWe need to work on forms designed to impress today’s savvy witnesses. Maybe bringing back Bibles will work for some, or something custom-fit to the particular witnesses. Who knows, for a chemist, it might be the periodic table. For others it might be a picture of their mother. Maybe the oath should be administered by prisoners in chains and mention the penalties of imprisonment for perjury. I think that would be pretty effective. Have you ever seen prisoners in chains up close in the courtroom? A few judges I know used to handcuff and shackle fathers who were delinquent in child support payments before their hearings. I am told it had a very sobering effect. Some experiments with this should be conducted because our current systems are not working very well. We rarely impress witnesses enough to awaken their latent conscience, much less their lawyers.

Maybe we should also amend Rule 26(g) to add swearing and a reference to ethics codes? Maybe stronger, more impressive oaths by lawyers signing 26(g) discovery requests and responses would work. Perhaps that would magically make more all too human lawyers start taking the requirements of the rules more seriously.

Lord Phillips 2009Maybe we should follow the British and make our judges wear fancier robes and make our lawyers and judges wear wigs? (One of Ariely’s experiments found clothing had an impact on honesty.) Let us build even more impressive courtrooms while we are at it, and let’s not only say Your Honor, but how about Your Lordship too? Or Your Grace? Maybe all lawyers should start adding courtly formalities to their 26(f) conferences? I can just imagine defense attorneys beginning every one of their responsive statements with things like: “The right honorable attorney representing the plaintiffs in this proceeding has made a point with some validity, but …” Maybe that would motivate lawyer conduct that would in fact please the court?

Of course I jest, but Ariely’s work shows that irrational approaches have a better chance of success than appeals to abstract knowledge alone. Forget about using reason to appeal to lawyers to cooperate, we have all seen how far that gets us.

Doing the Right Things for the Wrong Reasons

Are you a die-hard rationalist and demand more proof that the Reasonable Man is a myth? More evidence? Then listen to Dan Ariely’s Doing The Right Things for The Wrong Reasons.

Professor Ariely talks about more of his experiments. They show how immediate, tangible, emotions and concrete facts are a much more powerful motivator than all abstract knowledge. This means that one sanctions case invoking fear will do much more to encourage cooperation than a thousand law review articles. In my experience judges that threaten harsh punishment, that are known not to tolerate discovery misconduct, tend to have fewer disputes. Now we know why. Fear is a more powerful motivator than reason. As he shows in the video, for some people a good glass of wine is a powerful motivator too.

Dan_Ariely_toastProfessor Ariely’s testimony in this video examines the big gap between everyone’s knowledge of what they should be doing, and what they actually are doing. The truth is, we often do not act reasonably. There are many other more powerful forces at work. One of the most important is environment, and thus my earlier comments on impressive courtrooms, wigs, courtly conduct, and the like. Brand names and price have the same kind of impact. Many clients are still impressed by the big-firm, fancy reception room syndrome. People tend to think about fine wine and lawyers in the same way.

In the second half of this testimony Dan Ariely started to share some of the solutions he has come up with to these problems, ways to trick yourself and others into doing the right thing. One such motivator is public recognition, pride. Remember his discussion regard Prius owners. So how about Cooperation awards of lawyers? Proportionality awards for judges, etc. Let’s award a whole lot of gold, silver and especially bronze medals. I am serious about this awards and recognition proposal. If you have any interest in funding such awards, or otherwise being involved, please let me know. This would be a good opportunity for vendors in the legal space, especially e-discovery vendors.

Mere intellectual appeals to change behavior are almost useless. You have to persuade the whole human, and that requires addressing emotions and many other subconscious factors. That requires far more than abstract, knowledge-based writings.

The Power of Emotions and the Myth of Reasoned Behavior

Judge_Paul_GrimmThe power of emotions, and immediate gratification, should never be underestimated. This includes the positive motivators, like praise and recognition. An active judiciary can do much more to impact reasonable, ethical conduct than all appeals to reason. Judges need to be in your face, with both criticism and praise, stick and carrot. The motivations need to be immediate and real, not abstract and future oriented. See eg. Victor Stanley and the impact of Judge Grimm’s threats of immediate imprisonment of Pappas, the ultimate hide-the-ball litigant. Only that last jail contempt order in that case slowed the games. Victor Stanley, Inc. v. Creative Pipe, Inc., 269 F.R.D. 497, 506 (D. Md. 2010) (the attempts to collect the many fines and judgment entered in that case are still ongoing).

This all reminds me of Judge Waxse’s well-known quip that lawyers are like elementary particles, they change when observed (by judges). He has found that lawyers are more inclined to cooperate simply by including a possibility that a judge might someday watch a video of their behavior. Maybe we should require that all lawyer-to-lawyer communications be taped? Maybe we should triple the number of judges and give them all sensitivity training? Who knows? But the research shows that all manner of alternatives like that would be more successful than mere appeals to reason alone.

Waxse_Losey

This all makes me wonder why I even bother to continue to write, but then again, you may have noticed that I try to include non-rational appeals in my writing, such as images, videos and the great irrational motivator of humor. Humor is an elusive emotion to reach, but well worth the effort. It is difficult to resist the ideas of anyone who makes you laugh. Personally, I refuse to emulate anyone who does not at least make me smile. If they make me laugh out loud, well, I will dig in deep to try to understand them and their ideas. (This is on reason I highly recommend the new self-help book by Scott Adams, the creator of Dilbert – How to Fail at Almost Everything and Still Win Big: Kind of the Story of My Life.)

Law is Like Economics: Both Are Still Based on an Irrational Reliance on Reason

Predictable_IrrationalAs you have seen from the videos, Dan Ariely is not only witty, but also a psychologist and an economist. He has one PhD in Psychology and another in Business Administration. He is also an author of a number of books that explain his works to the general reader, including the best seller: Predictably Irrational: The Hidden Forces That Shape Our Decisions.

Dan evaluates the implications of his irrationality findings in Psychology on the field of Economics. So too are many other pundits in the field. See eg. Post-Rational Economic Manand Exploring the Post-Rational 21st CenturyAriely and others have amassed a growing body of evidence that humans are not rational machines. Yet most economists, much like most lawyers, do not believe that. They still believe that people make rational decisions. For instance, that purchases are based on reason alone. See Rational Choice Theory. That is the basis of classic economic theory, and since that presumption is wrong, so is the theory. Economics is now struggling with the development of new theories based on the way people really act. Dan is a leader of that movement, which he calls Behavioral Economics.

Learning a little about Dan’s insights and proposals to reform economic theories, and make them more realistic, and empirically based, can provide insights into the Law and reforms we should make. Surely we can do better than propose more videotapes of lawyers, in your face judges, bibles and oaths, solemn court reporters, and British style ceremonial conduct. But these are a start.

Reasonable_guageMore fundamentally, we need to consider how we should speak of legal negligence in the future. We need to stop referring to whether an act is reasonable, and instead speak of acceptability, with reason just one of several factors to consider in evaluating acceptable behavior. That is what I call, for lack of a better term, Holistic Jurisprudence. More on that later. Perhaps some law professors and judges are already thinking and writing about this, and I am not aware of their writings. If not, then what are we waiting for? The evidence of innate irrationally based, yet acceptable, behavior, is strong. That is our everyday reality. So why do we use a measure of acceptable conduct that does not mirror reality? Legal theory needs to change as much as economic theory, and so too does legal practice.

Robots and Neuroscience?

Facc_RobotI know what some of you are thinking. Maybe the answer is simply to turn our justice system over to robots programmed to make rational decisions. They will not suffer from innate irrationality like our judges do. (Yes, even judges are human and thus even judges suffer from the same cognitive disorders, same irrational drivers, that other humans do). Rational machines could also be programmed to fairly consider the innate irrationality of humans. We could create super robojudges by using active machine learning. They could receive training in just-decision-making by our top judges. Imagine, for instance, the wisdom and wit of retired Judge Facciola programmed into an AI entity. The input from our top judges would thereby, in theory at least, live forever. The experience and intelligence of our best judges would then be available to all litigants, not just the lucky few who appear before them. This puts a while new positive spin onto the Ghost in the Machine image.

The AI enhanced robojudges would, of course, be far more than mere rational machines. They would be trained by our legal experts to render judgments based on the Whole Man, one that actually exists, and not the legal fiction of the Reasonable Man. They would be programmed in a post-rational manner following models of real human behavior of acceptable conduct. (Our best human judges and lawyers already do that anyway, even if the jurisprudence theory says otherwise.) The day may come when many litigants will prefer smart, well-trained robots to serve as judges to evaluate acceptable conduct, especially when there are good human appeals judges to oversee the process. That day is, however, still in the remote future.

ex_machinaOf course, if Ray Kurzweil is right about the Singularity coming soon, then all bets are off. But Kurzweil is probably wrong about how fast AI will advance, and so I do not see this anything like this happening this century. Moreover, the live forever proponents are, in my view, seriously deluded. (Fear of death tends to do that to people. Just look at the many wacky beliefs people have.) There is far more to the human soul than logic can ever replicate. We are more than a set of synapses that can be replicated with on off switches. Still, I could be wrong.

Automation by full human replication is not The answer (at least not in this century). The use of AI enhanced tools in the law, such as predictive coding for document review, is more realistic. It will continue and expand into many other legal activities. Very soon many more types of lawyers, in addition to contract review lawyers, will need to retool in order to stay employed. The simple logic and reason tasks of lawyers will be automated. All mere logic workers will have to change or be out of work. But, at the same time, new employment positions will open for those involved in the new technologies. The jobs that open up will require greater technology skills, intellect, empathy, leadership, creativity and imagination. They will require uniquely human attributes that are way beyond the programming of any robots, now and perhaps forever. Again see Only Humans Need Apply for a good review of this subject in the context of the economy.

I cannot imagine exactly how this will all play out, but, new advanced technologies will have to be part of all future legal reforms. Many of the technologies are probably still unknown and thus impossible to project. But some will be based on existing technologies, just significantly improved.

Facciola_computerPerhaps that will include active machine learning and AI based law clerks for judges. It is not hard to imagine a judge’s consideration of an AI enhanced suggested view of the case. After all, they already do this based on their clerk’s views. I suspect judicial clerks will be replaced way before the judges themselves. Judges need to be enhanced with better computers, not replaced by them. They need to be augmented, not automated.

To take a more mundane example than robots and AI, I suspect that lie detection technologies will soon advance enough to be of greater assistance to the Law. How about acceptably intrusive truth-compelling technologies? I can easily imagine neural nets with electronic brain monitors built into “truth hats.” Witnesses would be required to wear the truth-indicating hats and give the attorneys, judges and juries more and better insights into their testimony. Not only intentional lies could be revealed, but strength of recollection, areas of brain accessed, etc. This would not have to be dispositive, but suggestive. This could provide us with something more to evaluate credibility than raw instinct and intuition, as important as these faculties are.

Meet-the-Parents-lie-detector with Harry Potter twist

We should be looking for all kinds of ways to bring the recent incredible advances in Neuroscience into the justice system. This is not futuristic science fiction, nor my over-active imagination. It is already happening. Many neuroscientists are looking into lie detection and other possible neuroscience applications in the Law. See eg Harvard’s Center for Law, Brain and Behavior and its program on Lie Detection & the Neuroscience of Deception.

Final Word From Dan Ariely 

Dan-Ariely_WSJGetting back to Dan, in addition to teaching and running very clever experiments at MIT and Duke, Dan is the founder of an organization with a name that seems both funny and ironic, The Center for Advanced Hindsight. He is also a prolific writer and video maker, both activities I admire. See for instance his informative page at MIT, his blog at DanAriely.com, his several books, and his videos, and even though its slightly boring, see his web page at Duke.

As a final piece of evidence on over reliance on reason I offer more testimony by Professor Ariely’s via another video, one which is not at all boring, I swear. It is called The Honest Truth About Dishonesty.

The video concludes with a subject near and dear to all lawyers, conflicts of interest. The non-rational impact of such conflicts turns out to be very strong and the law is wise to guard against them. Perhaps we should even step up our efforts in this area? 

Cornerstone Made of Pudding

CornerstoneThe scientific experiments of Dan Ariely and others show that the cornerstone of the Law – reasonability – is not made of granite as we had thought, it is made of pudding. You can hide your head in the sand, if you wish, and continue to believe otherwise. We humans are quite good at self-delusion. But that will not change the truth. That will not change quicksand into granite.

Our legal house needs a new and better foundation than reason. We must follow the physicists of a century ago. We must transcend Newtonian causality and embrace the more complex, more profound truth that science has revealed. The Reasonable Man is a myth that has outlived its usefulness. We need to accept the evidence, and move on. We need to develop new theories and propositions of law that confirm to the new facts at hand. Reason is just one part of who we are. There is much more to us then that: emotion, empathy, creativity, aesthetics, intuition, love, strength, courage, imagination, determination – to name just a few of our many qualities. These things are what make us uniquely human; they are what separate us from AI. Logic and reason may end up being the least of our abilities, although they are still qualities that I personally cherish.

Science has shown that our current reason-only-based system of justice is on shaky grounds. It is now up to us to do something about it. No big brother government, or super think-tank guru is going to fix this for us. Certainly not scientists either, but they should be able to help, along with technologists, programmers and engineers. This fix will have to come from within the legal profession itself. No one else knows our system of justice well enough to do this for us, certainly not scientists nor engineers. I know this from hard personal experience.

homer-simpson-brain-scanWhat are the implications of the findings of unreliable mental processes on the Law and our ability to reach just decisions? We should ask these questions concerning the Law, just like Professor Ariely is asking concerning Economics. Our fundamental legal assumption that all people can act out of reason and logic alone is false. Decisions made with these faculties alone are the exception, not the rule. There are a number of other contributing factors, including emotions, intuition, and environment. What does this mean to negligence law? To sanctions law? Now that the Reasonable Man is dead, who shall replace him?

Just as classical economic theory has had it all wrong, so too has classical legal theory. People are not built like reasonable machines. That includes lawyers, judges, and everyone else in the justice system, especially the litigants themselves.

If Not Reason, Then What?

Davinci_whole_manSince human reason is now known to be so unreliable, and is only a contributing factor to our decisions, on what should we base our legal jurisprudence? I believe that the Reasonable Man, now that he is known to be an impossible dream, should be replaced by the Whole Man. Our jurisprudence should be based on the reality that we are not robots, not mere thinking machines. We have many other faculties and capabilities beyond just logic and reason. We are more than math. We are living beings. Reason is just one of our many abilities.

So I propose a new, holistic model for the law. It would still include reason, but add our other faculties. It would incorporate our total self, all human attributes. We would include more than logic and reason to judge whether behavior is acceptable or not, to consider whether a resolution of a dispute is fair or not. Equity would regain equal importance.

A new schemata for a holistic jurisprudence would thus include not just human logic, but also human emotions, our feelings of fairness, our intuitions of what is right and just, and multiple environmental and perceptual factors. I suggest a new model start simple and use a four-fold structure like this, and please note I keep Reason on top, as I still strongly believe in its importance to the Law.

4-levels-Holistic_Law_pyramid

Some readers may notice that this model is similar to that of Carl Jung’s four personality types and the popular Myers Briggs personality tests. I am not advocating adoption of any of their ideologies, or personality theories, but I have over the years found their reference models to be useful. The above model, which is proposed only as a starting point for further discussion, is an extrapolation of these psychological models.

Call For Action

The legal profession needs to take action now to reduce our over-reliance on the Myth of the Reasonable Man. We should put the foundations of our legal system on something else, something more solid, more real than that. We need to put our house in order before it starts collapsing around us. That is the reasonable thing to do, but for that very reason we will not start to do it until we have better motivation than that. You cannot get people to act on reason alone, even lawyers. So let us engage the other more powerful motivators, including the emotions of fear and greed. For if we do not evolve our work to focus on far more than reason, then we will surely be replaced.

cyborg-lawyer

AI can think better and faster, and ultimately at a far lower cost. But can AI reassure a client? Can it tell what a client really wants and needs. Can AI think out of the box to come up with new, creative solutions. Can AI sense what is fair? Beyond application of the rules, can it attain the wisdom of justice. Does it know when rules should be bent and how far? Does it know, like any experienced judge knows, when rules should be broken entirely to attain a just result? Doubtful.

To get specific on the reforms needed now, we should bring back equity, and down play law. This was common in the first half of the Twentieth Century. At that time it was common to have Courts of Law and separate Courts of Equity. By the middle of the last century, Courts of Law won out in most states except Delaware, Mississippi, New Jersey, South Carolina, and Tennessee. Separate Equity Courts were closed down in favor of Courts of Law. Maybe we got it backwards. Maybe we were all led astray by our false confidence in reason.

Perhaps most courts should be Courts of Equity and Courts of Law become the exception. How has it worked out for the states that kept equity courts? Have Chancellors truly been able to side-step strict rules of law when they felt it was equitable to do so? If so, how has that worked out? Has power been abused? Or has justice been attained more often? What can we learn from chancery courts that might help us build a more holistic court of the future? We should apply analytics to study these questions to help us to reshape the law in a more human, holistic manner. Law Professors need to study this and help guide the profession.

A Few More Specific Suggestions of Reform

The AI enhancements already moving the law will continue to expand. That much is certain. Predictive coding, my speciality, is currently the prime example, but there will soon be many others.  They will enhance and improve our abilities. They will help us be more efficient. They will also help us to stay fair and honest. They could help end, or at least mitigate, human bias, stereotypes and prejudice.

Maybe timely reminders of ethics codes and serious under penalties of perjury type threats will also help? Maybe new, improved, and customized oaths will help? Oaths have been shown to be effective by Ariely’s research, so we should modify the rules accordingly. Let’s consider an update Rule 603 of the Federal Rules of Evidence.

Electrodes_EEG_RalphMaybe new truth recognition technologies should be used? Could a truth hat with built-in neural net be that far off? How about Google Glasses type apps that provide reliable new feedback of all kinds on the people you watch testifying? That cannot be too far off.  (The lie detection apps already on the market for iPhones, etc., all look bogus to me, which is not unexpected based on the limited biofeedback the phone sensors can provide.) Even if the information is not admissible as evidence, it could still be quite valuable to lawyers. Perhaps some of the recent discoveries in neuroscience could begin to be used in the justice system in all types of unexpected ways?

trophy_LawMaybe public recognition and awards to lawyers and judges who get it right will help? Ariely’s research suggests it will. And awards to litigants who do the right thing too, even if they lose the case? How about a discretionary set-off for defendants like that? How about the converse? Shame can be a powerful motivator too. Some judges already do this in subliminal manner. Let us encourage a more open application of emotion and creativity in judicial activity.

Maybe we should change the conditions and environments of places where witnesses are questioned, where mediations and trials are conducted? Maybe we should provide special training to court reporters on oath giving? Maybe we should have trials again, and not just settlements?

We need to look for all kinds of motivators. Knowledge and reason alone are not a solid foundation for justice. We also need wisdom. See eg. Losey, R., Information → Knowledge → Wisdom: Progression of Society in the Age of Computers.

Conclusion

Ralph_mosiac_7-16All social structures today are experiencing disruptive change, including the Law. Technology is driving these transformations. We need courage to face the reality of rapid change, instead of fearful avoidance. We need to shape the changes in the Law, not be overridden  by them. We need to be proactive, creative. We need to be guided by truth, not tradition. That means paying attention to analytics and psychology, and not just digging deeper into the Law alone for answers. The insights we need will come from a multidisciplinary approach, but one that is led by legal professionals. Only we truly understand legal practice. But our efforts to shape the change in our profession must include knowledge from all fields, including science, engineering and art. It must also include input from all other participants in the legal system, especially clients, litigants, plaintiffs, defendants. Legal practitioners, judges and scholars alone cannot provide a holistic view.

We must move away from over-reliance on reason alone. Our enlightened self-interest in continued employment in the rapidly advancing world of AI demand this. So too does our quest to improve our system of justice, to keep it current with the rapid changes in society.

Where we must still rely on reason, we should at the same time realize its limitations. We should look for new technology based methods to impose more checks and balances on reason than we already have. We should create new systems that will detect and correct the inevitable errors in reason that all humans make – lawyers, judges and witnesses alike. Bias and prejudice must be overcome in all areas of life, but especially in the justice system.

Computers, especially AI, should be able to help with this and also make the whole process more efficient. We need to start focusing on this, to make it a priority. It demands more than talk and thinking. It demands action. We cannot just think our way out of a prison of thought. We need to use all of our faculties, especially our imagination, creativity, intuition, empathy and good faith.


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