Team’s TAR Course has been Updated and Expanded

May 21, 2017

I was in Manhattan at the Marriott on Times Square this week presenting on advanced TAR with Jim Sullivan. We provided an overview on the Team’s latest methods, Hybrid Multimodal IST Predictive Coding 4.0. Presentations like this allow you to interact with students and refine your approach. It was a good group, as is typical in NYC, of lawyers and litigation support experts.

I left N.Y. on Wednesday night, well before the madman drove his car into Times Square on Thursday. Jim stayed overnight and walked out of Times Square just minutes before this horrible massacre. My condolences to the family of the nineteen year old girl who was killed and the twenty-two other pedestrians who were injured.

When I got home from NYC I condensed our eighty-five minute presentation into a thirty-seven minute video. It now serves as the core video introduction to the e-Discovery Team’s free TAR Course. It is found in the first of the sixteen classes in the Course. I also revised and improved the wording in the Welcome Page of the course and made it a stand alone entry point. To have a little more fun with all of this I also created a new graphic, shown below. It provides a visualization of the core content of the TAR Course. Click on it to see a larger view.

This TAR Course welcome page now has its own written and video content. That material used to be combined with the first class. So the net result is an expansion of the TAR Course from sixteen to seventeen modules. At this point the first eleven classes have “Homework Assignments” at the end with suggested supplemental readings and analytic challenges. We will be adding homework to the last five classes in the next month.

If you have already studied the first class, I urge you to go back and reread and re-view the writings and new three-part video. I also reproduce the same new video below. I call it an overview video, a first tell, but it contains advanced materials and some of my latest thinking.

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Go to the TAR Course.


Lawyers’ Job Security in a Near Future World of AI, the Law’s “Reasonable Man Myth” and “Bagley Two” – Part One

January 15, 2017

bad-robotDoes the inevitable triumph of AI robots over human reason and logic mean that the legal profession is doomed? Will Watson be the next generation’s lawyer of choice? I do no think so and have written many articles on why, including two last year: Scientific Proof of Law’s Overreliance On Reason: The “Reasonable Man” is Dead and the Holistic Lawyer is Born; and 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. In the Reasonable Man article I discussed how reasonability is the basis of the law, but that it is not objective. It depends on many subjective factors, on psychology. In the Scientific Proof article I continued the argument and argued:

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.

Scientific Proof Article

brain_gears_NOTo help make my argument in the Scientific Proof article I relied on the analysis of Thomas H. Davenport and Julia Kirby in Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (Harper 2016) and on the scientific work of Dan Ariely, a Professor of Psychology and Behavioral Economics at Duke University.

I cite to Only Humans Need Apply: Winners and Losers in the Age of Smart Machines to support my thesis:

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.

Also see Dean Gonsowski, A Clear View or a Short Distance? AI and the Legal Industry; and, Gonsowski, A Changing World: Ralph Losey on “Stepping In” for e-Discovery, (Relativity Blog).

Professor Ariely has found from many experiments that We’re All Predictably Irrational. In my article, Scientific ProofI point my readers to his many easily accessible video talks on the subject. I consider the implication of Professor Ariely’s research on the law:

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. …

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

My Scientific Proof article included a call to action, the response to which has been positive:

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.

I go on to make some specific suggestions, just to start the dialogue, and then closed with the following:

We must move away from over-reliance on reason alone. Our enlightened self-interest in continued employment in the rapidly advancing world of AI demands 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.

Reasonable Man Article

Reasonable_man_cloudTo help make my argument in the earlier blog, 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, I quoted extensively from an Order Denying Defendant’s Motion for Protective Order. The order arose out of a routine employment discrimination case. Bagely v. Yale, Civil Action No. 3:13-CV-1890 (CSH) (Doc. 108) (order dated April 27, 2015). The Order examined the “reasonability” of ESI accessibility under Rule 26(b)(2)(B) and the “reasonable” efforts requirements under Rule 26(b). I used language of that Bagley Order to help support my argument that there is far more to The Law than mere reason and logic. I also argued that this is a very good thing, for otherwise lawyers could easily be replaced by robots.

Another e-discovery order was entered in Bagley on December 22, 2016. Ruling On Plaintiff’s Motion To Compel. Bagely v. Yale, Civil Action No. 3:13-CV-1890 (CSH). Bagley Two again provokes me to write on this key topic. This second order, like the first, was written by Senior District Judge Charles S. Haight, Jr.. The eighty-six year old Judge Haight is becoming one of my favorite legal scholars because of his excellent analysis and his witty, fairly transparent writing style. This double Yale graduate has a way with words, especially when issuing rulings adverse to his alma mater. He is also one of the few judges that I have been unable to locate an online photo of, so use your imagination, which, by the way, is another powerful tool that separates us from AI juiced robots.

Lady JusticeI pointed out in the Reasonable Man article, and it bears repetition, that I am no enemy of reason and rationality. It is a powerful tool in legal practice, but it is hardly our only tool. It is one of many. The “Reasonable Man” is one of the most important ideas of Law, symbolized by the balance scales, but it is not the only idea. In fact, it is not even the most important idea for the Law. That honor goes to Justice. Lady Justice holding the scales of reason is the symbol of the Law, not the scales alone. She is usually depicted with a blindfold on, symbolizing the impartiality of justice, not dependent on the social status or position of the litigants.

My view is that lawyer reasoning should continue in all future law, but should augmented by artificial intelligence. With machines helping to rid us of hidden biases in all human reason, and making that part of our evaluation easier and more accurate, we are free to put more emphasis on our other lawyer skills, on the other factors that go into our evaluation of the case. These include our empathy, intuition, emotional intelligence, feelings, humor, perception (including lie detection), imagination, inventiveness and sense of fairness and justice. Reason is only one of many human capacities involved in legal decision making.

In Reasonable Man article I analyzed the first Bagley Order to help prove that point:

Bagley shows that the dividing line between what is reasonable and thus acceptable efforts, and what is not, can often be difficult to determine. It depends on a careful evaluation of the facts, to be sure, but this evaluation in turn depends on many subjective factors, including whether one side or another was trying to cooperate. These factors include all kinds of prevailing social norms, not just cooperativeness. It also includes personal values, prejudices, education, intelligence, and even how the mind itself works, the hidden psychological influences. They all influence a judge’s evaluation in any particular case as to which side of the acceptable behavior line a particular course of conduct falls.

In close questions the subjectivity inherent in determinations of reasonability is obvious. This is especially true for the attorneys involved, the ones paid to be independent analysts and objective advisors. People can, and often do, disagree on what is reasonable and what is not. They disagree on what is negligent and what is not. On what is acceptable and what is not.

All trial lawyers know that certain tricks of argument and appeals to emotion can have a profound effect on a judge’s resolution of these supposedly reason-based disagreements. They can have an even more profound affect on a jury’s decision. (That is the primary reason that there are so many rules on what can and cannot be said to a jury.)

lady_justice_not_blindIn spite of practical knowledge by the experienced, the myth continues in our profession that reasonability exists in some sort of objective, platonic plane of ideas, above all subjective influences. The just decision can be reached by deep, impartial reasoning. It is an article of faith in the legal profession, even though experienced trial lawyers and judges know that it is total nonsense, or nearly so. They know full well the importance of psychology and social norms. They know the impact of cognitive biases of all kinds, including, for example, hindsight biasSee Roitblat, The Schlemiel and the Schlimazel and the Psychology of Reasonableness (Jan. 10, 2014, LTN) (link is to republication by a vendor without attribution) (“tendency to see events that have already occurred as being more predictable than they were before they actually took place“); Also see Rimkus v Cammarata, 688 F. Supp. 2d 598 (S.D. Tex. 2010) (J. Rosenthal) (“It can be difficult to draw bright-line distinctions between acceptable and unacceptable conduct in preserving information and in conducting discovery, either prospectively or with the benefit (and distortion) of hindsight.” emphasis added); Pension Committee of the University of Montreal Pension Plan, et al. v. Banc of America Securities, LLC, et al., 685 F. Supp. 2d 456 (S.D.N.Y. Jan. 15, 2010 as amended May 28, 2010) at pgs. 463-464 (J. Scheindlin) (‘That is a judgment call that must be made by a court reviewing the conduct through the backward lens known as hindsight.” emphasis added).

In my conclusion to Reasonable Man article I summarized my thoughts and tried to kick off further discussion of this topic:

The myth of objectivity and the “Reasonable Man” in the law should be exposed. Many naive people still put all of their faith in legal rules and the operation of objective, unemotional logic. The system does not really work that way. Outsiders trying to automate the law are misguided. The Law is far more than logic and reason. It is more than the facts, the surrounding circumstances. It is more than evidence. It is about people and by people. It is about emotion and empathy too. It is about fairness and equity. It’s prime directive is justice, not reason.

That is the key reason why AI cannot automate law, nor legal decision making. Judge Charles (“Terry”) Haight could be augmented and enhanced by smart machines, by AI, but never replaced. The role of AI in the Law is to improve our reasoning, minimize our schlemiel biases. But the robots will never replace lawyers and judges. In spite of the myth of the Reasonable Man, there is far more to law then reason and facts. I for one am glad about that. If it were otherwise the legal profession would be doomed to be replaced by robots.

Bagley Two

Now let us see how Judge Haight once again helps prove the Reasonable Man points by his opinion in Bagley Two. Ruling On Plaintiff’s Motion To Compel (December 22, 2016), Bagely v. Yale, Civil Action No. 3:13-CV-1890 (CSH). In this opinion the reasonability of defendant Yale’s preservation efforts was considered in the context of a motion to compel discovery. His order again reveals the complexity and inherent subjectivity of all human reason. It shows that there are always multiple factors at work in any judge’s decision beyond just thought and reason, including an instinct born out of long experience for fairness and justice. Once again I will rely primarily on Judge Haight’s own words. I do so because I like the way he writes and because you need to read his original words to appreciate what I am talking about. But first, let me set the stage.

Reasonable_guageYale sent written preservation notices to sixty-five different people, which I know from thousands of matters is a very large number of custodians to put on hold in a single-plaintiff discrimination case. But Yale did so in stages, starting on March 1, 2013 and ending on August 7, 2014. Eight different times over this period they kept adding people to their hold list. The notices were sent by Jonathan Clune, a senior associate general counsel of Yale University. The plaintiff argued that they were too late in adding some of the custodians and otherwise attacked the reasonability of Yale’s efforts.

The plaintiff was not seeking sanctions yet for the suspected unreasonable efforts, they were seeking discovery from Yale as to details of these efforts. Specifically they sought production of: (1) the actual litigation hold notices; (2) the completed document preservation computer survey forms that were required to be returned to the Office of General Counsel by each Litigation Hold Recipient; and, (3) an affidavit detailing the retention and production for all non-ESI documents collected from each of the Litigation hold Recipients.

Yale opposed this discovery claiming any more information as to its preservation efforts was protected from discovery under the attorney-client privilege and attorney work product protection.  Yale also argued that even if the privileges did not apply here, the discovery should still be denied because to obtain such information a party must first provide convincing proof that spoliation in fact occurred. Yale asserted that the plaintiff failed to provide sufficient proof, or even any proof, that spoliation had in fact occurred.

Here is the start of Judge Haight’s evaluation of the respective positions:

Mr. Clune’s litigation hold notices stressed that a recipient’s failure to preserve pertinent documents could “lead to legal sanctions” against Yale. Clune was concerned about a possible sanction against Yale for spoliation of evidence. While Clune’s notices did not use the term, “spoliation” is a cardinal litigation vice, known by that name to trial lawyers and judges, perhaps unfamiliar to academics unable to claim either of those distinctions. Clune’s notices made manifest his concern that a trial court might sanction Yale for spoliation of evidence relevant to the University SOM’s decision not to reappoint Bagley to its faculty.

skull_bones_yaleNote the jab at academics. By the way, in my experience his observation is correct about the cluelessness of most law professors when it comes to e-discovery. But why does Judge Haight take the time here to point that out? This case did not involve the Law School. It involved the business school professors and staff (as you would expect). It is important to know that Judge Haight is a double Yale graduate, both undergraduate and law school. He graduated from Yale Law in 1955. He was even a member of Yale’s infamous of Skull and Bones society. (What does 322 really mean? Eulogia?) Perhaps there are some underlying emotions here? Judge Haight does seem to enjoy poking Yale, but he may do that in all his cases with Yale out of an eccentric kind of good humor, like a friendly shoulder punch. But I doubt it.

To be continued … 



Document Review and Predictive Coding: Video Talks – Part Four

March 11, 2016

predictive_coding_Step-3This is the fourth of seven informal video talks on document review and predictive coding. The first video explained why this is important to the future of the Law. The second talked about ESI Communications. The third about Multimodal Search Review. This video talks about the third step of the e-Discovery Team’s eight-step work flow, shown above, Random Baseline Sample.

coin_flipAlthough this text intro is overly long, the video itself is short, under eight minutes, as there is really not that much to this step. You simply take a random sample at or near the beginning of the project. Again, this step can be used in any document review project, not just ones with predictive coding. You do this to get some sense of the prevalence of  relevant documents in the data collection. That just means the sample will give you an idea as to the total number of relevant documents. You do not take the sample to set up a secret control set, a practice that has been thoroughly discredited by our Team and others. See Predictive Coding 3.0.

thumb_ruleIf you understand sampling statistics you know that sampling like this produces a range, not an exact number. If your sample size is small, then the range will be very high. If you want to reduce your range in half, which is a function in statistics known as a confidence interval, you have to quadruple your sample size. This is a general rule of thumb that I explained in tedious mathematical detail several years ago in Random Sample Calculations And My Prediction That 300,000 Lawyers Will Be Using Random Sampling By 2022. Our Team likes to use a fairly large sample size of about 1,533 documents that creates a confidence interval of plus or minus 2.5%, subject to a confidence level of 95% (meaning the true value will lie within that range 95 times out of 100). More information on sample size is summarized in the graph below. Id.

random_size_graph

The picture below this paragraph illustrates a data cloud where the yellow dots are the sampled documents from the grey dot total, and the hard to see red dots are the relevant documents found in that sample. Although this illustration is from a real project we had, it shows a dataset that is unusual in legal search because the prevalence here was high, between 22.5% and 27.5%. In most data collections searched in the law today, where the custodian data has not been filtered by keywords, the prevalence is far less than that, typically less than 5%, maybe even less that 0.5%. The low prevalence increases the range size, the uncertainties, and requires a binomial calculation adjustment to determine the statistically valid confidence interval, and thus the true document range.

data-visual_RANDOM_2

For example, in a typical legal project with a few percent prevalence range, it would be common to see a range between 20,000 and 60,000 relevant documents in a 1,000,000 collection. Still, even with this very large range, we find it useful to at least have some idea of the number of documents they are looking for. That is what the Baseline Step can provide to you, nothing more nor less.

95 Percent Confidence Level with Normal Distribution 1.96If you are unsure of how to do sampling for prevalence estimates, your vendor can probably help. Just do not let them tell you that it is one exact number. That is simply a point projection near the middle of a range. The one number point projection is just the top of the typical probability bell curve shown above, which illustrates a 95% confidence level distribution. The top is just one possibility, albeit slightly more likely than either end points. The true value could be anywhere in the blue range.

To repeat, the Step Three prevalence baseline number is always a range, never just one number. Going back to the relatively high prevalence example, the below bell cure shows a point projection of 25% prevalence, with a range of 22.2% and 27.5%, creating a range of relevant documents of from between 225,000 and 275,000. This is shown below.

25_bell-curve-Standard_deviation_diagram

confidence interval graph showing standard distribution and 50% prevalenceThe important point that many vendors and other “experts” often forget to mention, is that you can never know exactly where within that range the true value may lie. Plus, there is always a small possibility, 5% when using a sample size based on a 95% confidence level, that the true value may fall outside of that range. It may, for example, only have 200,000 relevant documents. This means that even with a high prevalence project with datasets that approach the Normal Distribution of 50% (here meaning half of the documents are relevant), you can never know that there are exactly 250,000 documents, just because it is the mid-point or point projection. You can only know that there are between 225,000 and 275,000 relevant documents, and even that range may be wrong 5% of the time. Those uncertainties are inherent limitations to random sampling.

Shame on the vendors who still perpetuate that myth of certainty. Lawyers can handle the truth. We are used to dealing with uncertainties. All trial lawyers talk in terms of probable results at trial, and risks of loss, and often calculate a case’s settlement value based on such risk estimates. Do not insult our intelligence by a simplification of statistics that is plain wrong. Reliance on such erroneous point projections alone can lead to incorrect estimates as to the level of recall that we have attained in a project. We do not need to know the math, but we do need to know the truth.

The short video that follows will briefly explain the Random Baseline step, but does not go into the technical details of the math or statistics, such as the use of the binomial calculator for low prevalence. I have previously written extensively on this subject. See for instance:

Byte and Switch

If you prefer to learn stuff like this by watching cute animated robots, then you might like: Robots From The Not-Too-Distant Future Explain How They Use Random Sampling For Artificial Intelligence Based Evidence Search. But be careful, their view is version 1.0 as to control sets.

Thanks again to William Webber and other scientists in this field who helped me out over the years to understand the Bayesian nature of statistics (and reality).

For details on all eight steps, including this third step, see Predictive Coding 3.0More information on document review and predictive coding can be found in the fifty-six articles published here.

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