Latest Grossman and Cormack Study Proves Folly of Using Random Search For Machine Training – Part Four

August 3, 2014

This is the conclusion of my four part blog: Latest Grossman and Cormack Study Proves Folly of Using Random Search For Machine Training – Part One and Part Two and Part Three.

Cormack and Grossman’s Conclusions

Maura-and-Gordon_Aug2014Gordon Cormack and Maura Grossman have obviously put a tremendous amount of time and effort into this study. In their well written conclusion they explain why they did it, as well as provide a good summary of their findings

Because SPL can be ineffective and inefficient, particularly with the low-prevalence collections that are common in ediscovery, disappointment with such tools may lead lawyers to be reluctant to embrace the use of all TAR. Moreover, a number of myths and misconceptions about TAR appear to be closely associated with SPL; notably, that seed and training sets must be randomly selected to avoid “biasing” the learning algorithm.

This study lends no support to the proposition that seed or training sets must be random; to the contrary, keyword seeding, uncertainty sampling, and, in particular, relevance feedback – all non-random methods – improve significantly (P < 0:01) upon random sampling.

While active-learning protocols employing uncertainty sampling are clearly more effective than passive-learning protocols, they tend to focus the reviewer’s attention on marginal rather than legally significant documents. In addition, uncertainty sampling shares a fundamental weakness with passive learning: the need to define and detect when stabilization has occurred, so as to know when to stop training. In the legal context, this decision is fraught with risk, as premature stabilization could result in insufficient recall and undermine an attorney’s certification of having conducted a reasonable search under (U.S.) Federal Rule of Civil Procedure 26(g)(1)(B).

This study highlights an alternative approach – continuous active learning with relevance feedback – that demonstrates superior performance, while avoiding certain problems associated with uncertainty sampling and passive learning. CAL also offers the reviewer the opportunity to quickly identify legally significant documents that can guide litigation strategy, and can readily adapt when new documents are added to the collection, or new issues or interpretations of relevance arise.

Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic DiscoverySIGIR’14, July 6–11, 2014, at pg. 9.

The insights and conclusions of Cormack and Grossman are perfectly in accord with my own experience and practice with predictive coding search efforts, both with messy real world projects, and the four controlled scientific tests I have done over the last several years (only two of which have to date been reported, and the fourth is still in progress). I agree that a relevancy approach that emphasizes high ranked documents for training is one of the most powerful search tools we now have. So too is uncertainty training (mid ranked) when used judiciously, as well as keywords, and a number of other methods. All the many tools we have to find both relevant and irrelevant documents for training should be used, depending on the circumstances, including even some random searches.

In my view, we should never use just one method to select documents for machine training, and ignore the rest, even when it is a good method like Cormack and Grossman have shown CAL to be. When the one method selected is the worst of all possible methods, as random search has now been shown to be, then the monomodal approach is a recipe for ineffective, over-priced review.

Why All the Foolishness with Random Search?

random samplingAs shown in Part One of this article, it is only common sense to use what you know to find training documents, and not rely on a so-called easy way of rolling dice. A random chance approach is essentially a fool’s method of search. The search for evidence to do justice is too important to leave to chance. Cormack and Grossman did the legal profession a favor by taking the time to prove the obvious in their study. They showed that even very simplistic mutlimodal search protocols, CAL and SAL, do better at machine training than monomodal random only.

scientist on simpsonInformation scientists already knew this rather obvious truism, that multimodal is better, that the roulette wheel is not an effective search tool, that random chance just slows things down and is ineffective as a machine training tool. Yet Cormack and Grossman took the time to prove the obvious because the legal profession is being led astray. Many are actually using chance as if it that were a valid search method, although perhaps not in the way they describe. As Cormack and Grossman explained in their report:

While it is perhaps no surprise to the information retrieval community that active learning generally outperforms random training [22], this result has not previously been demonstrated for the TAR Problem, and is neither well known nor well accepted within the legal community.

Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic DiscoverySIGIR’14, July 6–11, 2014 at pg. 8.

As this quoted comment suggests, everyone in the information science search community knew this already, that the random only approach to search is inartful. So do most lawyers, especially the ones with years of hands-on experience in search for relevant ESI. So why in the world is random search only still promoted by some software companies and their customers? Is it really to address the so called problem of “not knowing what you don’t know.” That is the alleged inherent bias of using knowledge to program the AI. The total-random approach is also supposed to prevent overt, intentional bias, where lawyers might try to mis-train the AI searcher algorithm on purpose. These may be the stated reasons by vendors, but there are other reasons. There must be, because these excuses do not hold water. This was addressed in Part One of this article.

This bias-avoidance claim must just be an excuse because there are many better ways to counter myopic effects of search driven too narrowly. There are many methods and software enhancements that can be used to avoid overlooking important, not yet discovered types of relevant documents. For instance, allow machine selection of uncertain documents, as was done here with the SAL protocol. You could also include some random document selection into the mix, and not just make the whole thing random. It is not all or nothing, not logically at least, but perhaps it is as a practical matter for some software.

My preferred solution to the problem of “not knowing what you don’t know” is to use a combination of all those methods, buttressed by a human searcher that is aware of the limits of knowledge. In mean, really! The whole premise behind using random as the only way to avoid a self-looping trap of “not knowing what you don’t know” assumes that the lawyer searcher is a naive boob or dishonest scoundrel. It assumes lawyers are unaware that they don’t know what they don’t know. Please, we know that perfectly well. All experienced searchers know that. This insight is not just the exclusive knowledge of engineers and scientists. Very few attorneys are that arrogant and self absorbed, or that naive and simplistic in their approach to search.

No, this whole you must use random only search to avoid prejudice is just a smoke screen to hide real reason a vendor sells software that only works that way. The real reason is that poor software design decisions were made in a rush to get predictive coding software to market. Software was designed to only use random search because it was easy and quick to build software like that. It allowed for quick implementation of machine training. Such simplistic types of AI software may work better than poorly designed keyword searches, but it is still far inferior to more complex machine training system, as Cormack and Grossman have now proven. It is inferior to a multimodal approach.

The software vendors with random only training need to move on. They need to invest in their software to adopt a multimodal approach. In fact, it appears that many have already done so, or are in the process. Yes, such software enhancements take time and money to implement. But we need software search tools for adults. Stop all of the talk about easy buttons. Lawyers are not simpletons. We embrace hard work. We are masters of complexity. Give us choices. Empower the software so that more than one method can be used. Do not force us to use only random selection.

We need software tools that respect the ability of attorneys to perform effective searches for evidence. This is our sand box. That is what we attorneys do, we search for evidence. The software companies are just here to give us tools, not to tell us how to search. Let us stop the arguments and move on to discuss more sophisticated search methods and tools that empower complex methods.

Attorneys want software with the capacity to integrate all search functions, including random, into a mulitmodal search process. We do not want software with only one type of machine training ability, be it CAL, SAL or SPL. We do not want software that can only do one thing, and then have the vendor build a false ideology around their one capacity that says their method is the best and only way. These are legal issues, not software issues.

Attorneys do not just want one search tool, we want a whole tool chest. The marketplace will sort out whose tools are best, so will science. For vendors to remain competitive they need to sell the biggest tool chest possible, and make sure the tools are well built and perform as advertised. Do not just sell us a screwdriver and tell us we do not need a hammer and pliers too.

Leave the legal arguments as to reasonability and rules to lawyers. Just give us the tools and we lawyers will find the evidence we need. We are experts at evidence detection. It is in our blood. It is part of our proud heritage, our tradition.

King_Solomon_JudgeFinding evidence is what lawyers do. The law has been doing this for millennia. Think back to story of the judicial decision of King Solomon. He decided to award the child to the woman he saw cry in response to his sham decision to cut the baby in half. He based his decision on the facts, not ideology. He found the truth in clever ways built around facts, around evidence.

Lawyers always search to find evidence so that justice can be done. The facts matter. It has always been an essential part of what we do. Lawyers always adapt with the times. We always demand and use the best tools available to do our job. Just think of Abraham Lincoln who readily used telegraphs, the great new high-tech invention of his day. When you want to know the truth of what happened in an event that took place in the recent past, you hire a lawyer, not an engineer nor scientist. That is what we are trained to do. We separate the truth from the lies. With great tools we can and will do an even better job.

Many multimodal based software vendors already understand all of this. They build software that empowers attorneys to leverage their knowledge and skills. That is why we use their tools. Empowerment of attorneys with the latest AI tools empowers our entire system of justice. That is why the latest Cormack Grossman study is so important. That is why I am so passionate about this. Join with us in this. Demand diversity and many capacities in your search software, not just one.

Vendor Wake Up Call and Plea for Change

Ralph_x-mas_2013My basic message to all manufacturers of predictive coding software who use only one type of machine training protocol is to change your ways. I mean no animosity at all. Many of you have great software already, it is just the monomondal method built into your predictive coding features that I challenge. This is a plea for change, for diversity. Sell us a whole tool chest, not just a single, super-simple tool.

Yes, upgrading software takes time and money. But all software companies need to do that anyway to continue to supply tools to lawyers in the Twenty-First Century. Take this message as both a wake up call and a respectful plea for change.

Dear software designers: please stop trying to make the legal profession look only under the random lamp. Treat your attorney customers like mature professionals who are capable of complex analysis and skills. Do not just assume that we do not know how to perform sophisticated searches. I am not the only attorney with multimodal search skills. I am just the only one with a blog who is passionate about it. There are many out there with very sophisticated skills and knowledge. They may not be as old (I prefer to say experienced) and loud mouthed (I prefer to say outspoken) as I am, but they are just as skilled. They are just as talented. More importantly, their numbers are growing rapidly. It is a generation thing too, you know. Your next generation of lawyer customers are just as comfortable with computers and big data as I am, maybe more so. Do you really doubt that Adam Losey and his generation will not surpass our accomplishments with legal search. I don’t.

Dear software designers: please upgrade your software and get with the multi-feature program. Then you will have many new customers, and they will be empowered customers. Do not have the money to do that? Show your CEO this article. Lawyers are not stupid. They are catching on, and they are catching on fast. Moreover, these scientific experiments and reports will keep on too. The truth will come out. Do you want to be survive the inevitable vendor closures and consolidation? Then you need to invest in more sophisticated, fully featured software. Your competitors are.

Dear software designers: please abandon the single feature approach, then you will be welcome in the legal search sandbox. I know that the limited functionality software that some of you have created is really very good. It already has many other search capacities. It just needs to be better integrated with predictive coding. Apparently some single feature software already produces decent results, even with the handicap of random-only. Continue to enhance and build upon your software. Invest in the improvements needed to allow for full multimodal, active, judgmental search.

Conclusion

Flashlights_taticalrandom only search method for predictive coding training documents is ineffective. The same applies to any other training method if it is applied to the exclusion of all others. Any experienced searcher knows this. Software that relies solely on a random only method should be enhanced and modified to allow attorneys to search where they know. All types of training techniques should be built into AI based software, not just random. Random may be easy, but is it foolish to only search under the lamp post. It is foolish to turn a blind eye to what you know. Attorneys, insist on having your own flashlight that empowers you to look wherever you want. Shine your light wherever you think appropriate. Use your knowledge. Equip yourself with a full tool chest that allows you to do that.




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