FYI – in each round of training, including the first, document reviewers are only looking at a few hundred, to at most, a few thousand documents. A typical complex project takes about ten rounds of machine training to complete by finding all of the relevant documents required.
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Lawyers Duties, Metadata, Review, Search, Technology, VENDORS | Tagged: computer assisted review, legal search, machine learning, predictive coding, technology, vendors | 
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Posted by Ralph Losey
The gears in this diagram show the mechanics of a complex document review project circa 2018. The role I play in such projects, and other search specialists like me, is that of the AI Trainer (the dark blue gear with my picture in it).
AI Trainers Need Only Look at Metadata
As you can see from the diagram, in the role of AI Trainer I do not actually look at any documents myself, just the ESI metadata, such as number of documents with certain keywords or documents that already have relevance classifications. The metadata is all the information that I need in order to search, make machine training selection decisions and select documents for reviewers to actually look at and classify (multimodal review). These are steps four, five and six of my open-sourced eight-step process for document review using predictive coding. (Classes 10-16 of the TARcourse.com) This new Key Players diagram is another way of describing the iterative process that makes up the core of AI-enhanced document review today.
I could not complete my role in this process with my own limited human capacities and intelligence. I rely heavily on the input of the machine intelligence, the ranking metadata created by the AI (step-five in the iterated steps 4, 5 and 6 shown above). This is a Hybrid process, Man and Machine, working together using a variety of search techniques. Predictive Coding 4.0 Hybrid Multimodal IST Method (Classes 10-16 of the TARcourse.com). The entire eight-step work-flow for a predictive coding review project is shown in the diagram below.
The highest ranking documents are almost always included in the documents that I select to batch out to reviewers to examine and code. (Step One in the Key Players work flow.) This ranking is part of the metadata that the AI adds to the ESI. (Step Four in the Key Players work flow.)
Infinity Loop
The iterative training workflow when described in terms of the Key Players forms a figure-eight, an infinity loop, as shown in blue in the diagram below. Step One in the Key Players work flow is my work of multimodal search and choosing ESI for the Review Attorneys to read and code. Step Two is the work of the Review Attorneys to code the documents. This changes the metadata by adding classifications to the documents they review. In Step Three I study the classification metadata created by the Reviewers, and other metadata, and use this information to choose the ESI to Train the AI. In Step Four the AI re-ranks the ESI again, changing the metadata again. That brings us back to Step One again and my work to study the new metadata, create searches and batch out more documents for the Document Reviewers. The highest ranking documents are almost always included in the documents that I select to batch out to reviewers to examine and code.
This circle eight workflow keeps repeating until all of the responsive documents (ESI) required by the project have been found. That should include all Highly Relevant documents and most, if not close to all, of the merely relevant. The iterative infinity loop comes to an end when I determine reasonable, proportional efforts have been made and make a Stop Decision. Then we move onto testing that decision with a random sample, which is part of Step Seven, Zero Error Numerics (ZEN) Quality Control.
Limiting the Time of High Billing Rate Attorneys
The metadata, and my monitoring of all communications in a project, provides all of the information that I need to help supervise first-pass quality control. (I rarely get involved in second pass, final production work. The project managers have that well under control.) Since I do not spend my time looking at documents, my time on a project, which is at a relatively high billing rate, is very limited. Clients like that. So do I, since I just get to do what I enjoy the most.
With the help of my document review attorneys, most of whom are contract attorneys who specialize in review, I can complete an entire project without ever reading a single document! They do it for me and do a great job at it. (I only use the best.) The bulk of the work in complex projects like this is now performed by these document reviewers, either contract review attorneys or Junior SME (Subject Matter Expert) attorneys. They are the ones that put in the hours. The senior SMEs do not have to spend much time on the review at all, which is good, because most of them do not like this kind of work.
My time on the high-level meta-functions is also constrained and very limited compared to the reviewers. But I like what I do a lot; I find it challenging, even fun. WHY I LOVE PREDICTIVE CODING: Making Document Review Fun Again with Mr. EDR and Predictive Coding 4.0. After having done this since 2012, when our Da Silva Moore case with Judge Peck kicked off the predictive coding frenzy, I am able to do this pretty fast.
Better Recall and Precision
If I do my job right, and the AI probability ranking algorithm in the software works correctly, then the amount of time needed by the reviewers to do theirs will be far less. That is primarily because they will have to look at far fewer irrelevant documents. Moreover, we will be able to find far more of the relevant documents by using the AI-enhanced, iterated figure-eight methods.
Our tests, both formal and ad hoc, have shown that the number of relevant documents that we miss is far less in complex projects when we have the help of machine learning. In other words, our recall is higher when we use machine learning search features to help us to find documents. See e-Discovery Team’s 2016 TREC Report: Once Again Proving the Effectiveness of Our Standard Method of Predictive Coding; and, official TREC report for 2015, published on February 20, 2016, found on the NIST website at http://trec.nist.gov/pubs/trec24/papers/eDiscoveryTeam-TR.pdf. We can do without it in simple and small projects, but when the going gets tough, we need the help of machine learning. It is a game changer. Our team, and many others before us, proved this at TREC in both 2015 and again 2016. We proved it in 2015 when we followed TREC into searches of the dark web and BlackHat World. We proved it again in 2016 when we searched the considerably different world of Jeb Bush email. We prove it again every day in our legal practice.
Better, faster, cheaper. This is not a myth or idle vendor promise. If you tried it before and it did not work for you, your software might have been poor or your methods wrong. If you used a method with large random samples at the start and secret control sets, your method was wrong. If you used a method where you trained and then reviewed, instead of continuously training, your method was wrong too. Try it again with the latest methods and software with actual active machine learning features, not just passive analytics. Active machine learning is what makes the difference. Software quality is important. So too are the proper methods of using the technology.
The Use of Active Machine Learning to Find Documents is Not New
There are two sponsors of TREC, the U.S. Department of Commerce through the National Institute of Standards and Technology (NIST), and the Department of Defense. The power of this kind of specialized artificial intelligence to help find documents is an everyday reality for most document search experts. All text retrieval scientists in the field, many of whom we met at TREC, know this from decades of work in academia and for government agencies they cannot talk about. Active machine learning for text retrieval has been used by libraries and information gathering government agencies around the world for decades. It is a classic signal in the noise type of problem; find the important information from trillion of bits of useless noise. (Why do you think the NSA collects email metadata, and does not care so much about actual contents?)
Although the term Predictive Coding is somewhat new, and the methods discussed here for efficiently using this technology in legal document review projects are brand new, the machine learning technology itself is not new. It is well established. The 2016 TREC we participated in was the twenty-fifth anniversary of TREC. Active machine learning for purposes of text retrieval is far more powerful then mere passive analytics. It is truly a breakthrough for legal technology, and a win win for the legal profession.
Using Active Machine learning to Lower Costs and Improve Recall
With this technology and these methods it is now possible to have both improved precision and better recall. It is possible to find more relevant documents at a lower cost, even considering the relatively high billing rates of the AI-Trainers and Senior SMEs.
The system shown in the Key Player diagrams allows us to limit the time of these two key players. The Senior SMEs just supervise the work of their junior counterparts and the skilled legal searchers, people like me, just look at metadata, not the documents. Using this method I can supervise and serve as AI-Trainer in multiple projects at the same time.
AI Trainers Need Not Be Involved in SME Issues
As you can see from the gears work flow, as an AI Trainer I do not get involved in subject matter expert issues, such as scope of relevance/responsiveness. Although I occasionally still do this, and unlike most search experts, have several decades of experience as a Senior SME under my belt, it is not necessary. Most of the time I am not involved. The senior trial attorney and their number-two know a lot more about the subject and case than me. I am not concerned about the quality of their expertise, nor the good faith of the execution. See TARcourse.com, Ninth Class: 7th, 8th and 9th Insights – GIGO, QC, SME, Method, Software (discusses GIGO training issues and the problem of the negligent or corrupt SME).
The SME Team
On the question of competent, good faith activities, the SMEs I work with are usually my law partners. I have no reason to be concerned about their skills or bad faith malfeasance. Even if they were not, any negligent or intentional twisting of relevance to hide evidence would be revealed by the metadata. It would also be exposed by the AI and the Review Attorneys. In other words, the AI would notice, the AI Trainer would notice and so would the Review Attorneys. If an unscrupulous attorney were to attempt to hide evidence, it would have to be done before the review by excluding the ESI from the collection. (That is one reason I always like to be involved in collection and insist on bulk collections.) If the ESI is not in the review database to begin with, then it will not be found. But even then, tell-tale traces of the omitted documents may be noticed, such as gaps in email chains. Bottom line, the team approach described here makes the kind of corrupt practices described in Waymo v Uber far more difficult. Waymo v. Uber, Hide-the-Ball Ethics and the Special Master Report of December 15, 2017.
It would be much easier for a corrupt attorney to get away with hiding evidence in an older system, without AI Trainers and document review specialists. Unethical behavior thrives in the dark. It is near impossible to pull off in open group team work. Everyone would have to be in on it. For that reason, I am quite comfortable leaving most relevance decisions to the SMEs, even when they are not my attorneys, and focusing my time on multimodal search, machine training and quality controls.
SME in e-Discovery
At this point in my career, I am an e-discovery specialist. I do not attempt to stay current in other substantive areas of the law. It is hard enough to stay current with e-discovery, both case law and new technology. Assuming a project has good communications (and I help out with that), there is no reason for me to know much more than the basics about a case. Also, as discussed in the Ninth Class of the TARcourse.com, we have multiple built-in safeguards for quality control. They catch and help correct mistakes and inconsistencies in relevance judgment. Such mistakes are inevitably in any complex project. The understanding of relevance naturally evolves as more ESI is reviewed. That is the main reason the first methods of predictive coding often worked poorly. They used large, random secret control sets that incorrectly assumed that relevance was fixed. We have fixed and stopped using control sets long ago. TARcourse.com – First Class: Background and History of Predictive Coding.
Under our current methods, the document reviewers themselves (and the AI that I train) are the ones who have to closely understand and follow the SMEs, not me. That means I can work on almost any type of document review project without personal expertise in the type of law involved, nor the relevance rules that develop. It is more important that I know the Reviewers and type of ESI involved. It is especially important for me to understand the metadata created by the reviewers as they code the documents and the metadata created by the software algorithm, the AI, as it ranks the documents. It is more important for me to know the software than the subject matter of the case.
Conclusion
In a forthcoming blog I will provide a video with a more detailed explanation of how the Key Players flow-chart works. I will also add this new Key Players explanation to the TARcourse.com. In addition, look for me to be speaking about this at forthcoming webinars, including one for Bloomberg on January 18, 2018, 12:00-1:00 EST on Building Effective Discovery Teams through Relationship Management, and at Legal Tech in 2018. I also plan to talk about this on Twitter. If you are not already following me on Twitter, and do not mind my sometimes ill-controlled anti-Trump rage, suggest you follow me there at @ralphLosey.
Happy New Year!
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Posted by Ralph Losey
Playing games is a great way to learn. That’s one reason I’ve devised a game concerning the interesting and fairly complex issues involved in trying to determine what e-discovery activities are proportional and appropriate in various sized cases. Specifically, what should you do to prepare for federal court 26(f) conferences in small and medium sized cases, versus large, complicated cases? Small, Medium or Large? is kind of a Goldilocks game of proportionality.
I have had to give these proportionality questions a lot of thought as part of my practice as a lawyer supervising hundreds of e-discovery projects at a time, projects of all different sizes. I could simply give you my answer, but after five books, I’ve already been there and done that. So I thought I’d try something new and make this learning into a game where you consider and vote on what activities you think are appropriate for a Small, Medium or Large case.
The format of this proportionality game is loosely based on the ideas of Hive Mind, a pretty cool new insight into collective intelligence, which, as Wikipedia puts it, is a “shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.” See: Is Collective Intelligence a safer form of A.I?
The Hive Mind is different from Crowdsourcing, but related. To do properly, a Hive Mind requires Swarming, which we cannot really do properly in this game using polls. , Super-Intelligence and the virtues of a “Hive Mind” (Singularity, 2/10/16). One Silicon Valley startup Unanimous A.I., is developing technologies that enable sophisticated online human swarming and thus better collective intelligence. I might try their free product for social research, UNO, if I further investigate the power of the Hive Mind. Maybe at NY LegalTech? (Email me if you’re interested.) In the meantime we are going to use simple polling for the e-Discovery Hive Mind Game: Small, Medium or Large?
Let’s play the game and see what collective intelligence emerges from many individuals giving their opinion about proportionality and e-discovery. I am playing this game now with all of the litigation associates and paralegals in my law firm, which is a pretty large swarm by itself. Your responses will join in the swarm, the collective intelligence.
In January I’ll share the results of all the polling and opine away as to how well the Hive Mind performed. You can win the game in one of two ways, either by matching the most popular Hive Mind responses or by matching my responses. (I assume there will be a difference because not enough experts will be playing, but who knows, maybe not. In theory, with enough experts swarming, the group, the Hive Mind, will always have the best answer.)
Background To Play the Game
In order to play the Small, Medium or Large? game, you first need to be familiar with the checklist of the Southern District Court of Florida of all of the things that you should do to prepare for e-discovery in a large case. It is a pretty good list and I have written about it before. Good New 33-Point e-Discovery Checklist From Miami (e-Discovery Team, October 1, 2017) (a must read to fully prepare for this game). My article contains comments and explanations about all checklist items, which is the beginning of a kind of swarm interaction, that is, if you take time to ponder the signals. The Court’s checklist incorporates the new provisions in the rules on relevance and proportionality (Rule 26(b)(1)) and on specific objections (Rule 34(b)(2)).
It is not a perfect list, but it is the best one now out there with a court pedigree. It is not too long and complex, like the older lists of some courts that are very detailed, and not too short, like the easy-peasy list that Bill Hamilton and I created for the Middle District Court of Florida many years ago. (Attorneys still complained about how burdensome it was!) In sum, the 33-Point Checklist out of Miami is a good list for legal practitioners all over the country to use to prepare for e-discovery, which means it is a good basis for our Small, Medium or Large? Hive Mind Game. Come play along.
Rules of the Game
The goal of our Hive Mind Game is to determine which of the thirty-three points on the checklist are applicable to big cases only, which are applicable to medium size cases and which to small cases. You are to assume that all thirty-three points apply to large cases, but that they are not all applicable to medium and small size cases. The Hive Mind voting is used to allow the swarm – that’s you – to identify which of the thirty-three only apply to small cases, and which only apply to medium size cases. If a checklist item applies to a medium size case, it automatically also applies to a small size case.
In other words, the game is to sort the thirty-three into three piles, Small, Medium or Large? Simple, eh? Well, maybe not. This is a matter of opinion and things are pretty vague. For instance, I’m not going to define the difference between large, small and medium size case. That is part of the Hive Mind.
The game is important because proportionality in the law is important. You do not prepare for a big case the same way you prepare for a small case. You just don’t. You could, but it would be a waste of your clients money to do so. So the real trick in e-discovery, like in all other aspects of the litigation, is to determine what you should do in any size case to prepare, including even the small cases. For instance, outside of e-discovery, most people would agree that you should take the parties depositions as a minimum to prepare for a trial of any size case, including small ones.
What are the equivalent items in the 33-point checklist? Which of them should be applied to all cases, even the small ones? Which of them are too complicated and expensive to apply in a small case, but not a medium sized case? Which too complicated and expensive to apply in a small or medium sized case, but not a large one? That is where the real skill and knowledge come in. That is the essence of the game.
Assume All 33 Items Apply to Big Cases
This Small, Medium or Large? Hive Game requires you to assume that all thirty-three items on the Court’s checklist apply to big cases, but not to all cases, that there are certain checklist items that only apply to medium size cases, and others, a smaller list, that only apply to small cases. You may question the reality of this assumption. After all, the Court does not say that. It does not say, here’s a checklist we made to guide your e-discovery, but you can ignore many of the items on this list if you have a small case, or even a medium size case. Still, that’s what they mean, but they do not go on to say what’s what. They know that the Bar will figure it out themselves in due time, meaning the next several years. And right they are. But why wait? Let’s figure it out ourselves now with this Small, Medium or Large? Hive Game, and condense years to weeks.
The Hive Game will allow us to fill in the blanks of what the Court did not say. But first, lets focus again on what the Court did say. It said that the checklist may be used by members of the Bar to guide the Rule 26(f) e-discovery conferences and Case Management Reports. It did not say shall be used. It is a suggestion, not a requirement. Still, as every long-term member of the court knows, what they mean is that you damn well should follow the checklist in a big case! Woe unto the lawyers who come before the judges in a big case with an e-discovery issue where they never even bothered to go through the checklist. If your issue is on that list, and chances are it will be, then dear slacker, prepare for a Miami style bench-slap. Ouch! It is going to hurt. Not only you and your reputation, but also your client.
I feel confident in making the game assumption that all thirty-three checklist items apply to big cases. I am also confident they do not all apply to medium and small size cases and that is the real reason for the court’s use of may instead of shall.
With that background we are almost ready to start playing the game and opine away as to which of the 33 are small and medium size only. But, there is still one more thing I have found very helpful when you try to really dig into the checklist and play the game, you need to have the numbers 1-33 added to the list. The one mistake the Court made on this list was in using headings and bullet points instead of numbers. I fix that in the list that follows, so that you can, if nothing else, more easily play the game.
Preservation
Liaison
Informal Discovery About Location and Types of Systems
Proportionality and Costs
Search
Phasing
Production
Privilege
One Example Before the Games Begin
We are almost ready to play the e-Discovery Small, Medium or Large? Hive Mind Game. We will do so with thirty-two polls that are presented to the player in the same order as the Court’s checklist. To make sure the rules are clear (this is, after all, a game for lawyers, not kids) we start with an example, the first of the thirty-three items on the checklist. The court’s first item is to suggest that you Determine the range of creation or receipt dates for any ESI to be preserved.
The “right answer” to this first item is that this should be done in every case, even the small ones. You should always determine the date range of data to be preserved. In most cases that is very easy to do, and, as every lawyer should know, when in doubt, when it comes to preservation, always err on the side of inclusion. That means you should check the Small Case answer as shown in the “dummy poll” graphic below.
Checklist 1
We have set these polls up so that you cannot see the results, but you can leave private comments. We may do this again later and experiment with what happens when you can see the results. We will share the results (and some comments) when the game ends on January 1, 2019.
Now for the live polls and game proper. Note that several of the checklist items, including number two and three, which are the first two polls shown below, are so long that we had to paraphrase and shorten them to fit in the space allocated in the polling software. To see the original of all thirty-three items on the checklist, go to my prior blog explaining the list (highly recommended) or the court’s page.
Let the Games Begin!
We are now ready to begin playing the e-Discovery Hive Mind Game. So get ready to plug-in. Select an answer to each of the thirty-two polls that follow. After you vote, you also have a chance to leave a private comment to each poll, but that is optional and will not impact your score.
Checklist 2
Checklist 3
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Checklist 4
Checklist 5
Checklist 6
Checklist 7
Checklist 8
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Checklist 9
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Checklist 10
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Checklist 11
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Checklist 12
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Checklist 13
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Checklist 14
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Checklist 15
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Checklist 16
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Checklist 17
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Checklist 18
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Checklist 19
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Checklist 20
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Checklist 21
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Checklist 22
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Checklist 23
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Checklist 24
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Checklist 25
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Checklist 26
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Checklist 27
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Checklist 28
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Checklist 29
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Checklist 30
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Checklist 31
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Checklist 32
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Checklist 33
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Congratulations! You have finished the Game and made your contribution to the e-Discovery Hive Mind. Look for results sometime in early 2018. You can then determine how your answers compared with the collective Hive Mind.
I will also let you know how the Hive Mind answers compared with my own. So you will have two chances to win. Anyone who matches all of my answers wins a free lunch with me in Orlando. Other prizes have yet to be determined. Vendors care to contribute some goodies? Perhaps Elon will donate a free trip to Mars, where I for one hope we don’t run into any Borg cubes, I don’t care how good their Hive Mind is.
In the meantime, please encourage your e-discovery friends and colleagues to join in the game. Teachers and Partners are invited to require their students and associates, paralegals to play too. Resistance is futile! Digging deep into this checklist is a great way to expand your knowledge and expertise of electronic discovery law and practice.
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Posted by Ralph Losey
Our addiction to smart phone and social media has led to unintended negative consequences. We are in danger of becoming vapid carbon-based consumption machines. We are already extremely vulnerable to foreign power social manipulation, to propagandists and hostile mind-meddlers of all kinds. We are living in very dangerous times where information floods and confuses us all. Examining the 12 Predictions Made in 2015 in “Information → Knowledge → Wisdom” (2017). We need to cut down on our addiction and pressure the big-tech companies into positive action to protect our rights. We need to unplug more often to make sure we maintain our humanity. Science is wrong, we are not mere information processing machines. We are wise stardust with capacities beyond the imagination of most technocrats. Reverse the mantra of the sixties: Turn off, tune out and drop in. Look up. Look around. Resist. Act. See eg. Metz, Smartphones Are Weapons of Mass Manipulation (MIT Technology Review, 10/19/17).
Metablog
This is a rare blog where I write about someone else’s article – a blog about a blog. To make this more bizarre, the blog I’m moved to write about is a blog about a book, which I have been meaning to write about myself. So yes, this is a blog about a blog about a book that was almost a blog. But I get ahead of myself with this useless metadata. The blog that caught my interest is called: We’re Drowning in Data But Starved for Wisdom: We’re more than just intelligent machines (Thrive Global, 9-12-17). It is by a famous person whom I follow on Twitter, but have never written about before, Arianna Huffington. I used to think of her as a politically astute rich media person (Huffington Post) who was concerned about sleep. The Sleep Revolution: Transforming Your Life, One Night at a Time (Harmony, 2016).
Deep Thoughts About Technology
Arianna has apparently gotten enough sleep now and is thinking deep thoughts about technology. Well, at least her thoughts seem profound to me, although my reasoning is narcissisticly suspect. That’s because Arianna’s thoughts are much like my own. Although I may have been writing about the future of technology longer than Arianna, I have to admit that her short blog does a better job than me at expressing many of my core concerns. AI-Ethics.com.
The inspiration of Arianna’s blog is the latest popular book in AI, one that I am also quite enamored by, Life 3.0: Being Human in the Age of Artificial Intelligence. It is written by MIT professor Max Tegmark. I mentioned him and this book in my blog last month, More Additions to AI-Ethics.com: Offer to Host a No-Press Conference to Mediate the Current Disputes on AI Ethics, Report on the Asilomar Conference and Report on Cyborg Law. Arianna’s blog also mentions the debate I offered to mediate in a dialogue based conference. Arianna says:
What’s fascinating about the debate about artificial intelligence is that it isn’t just about the threat AI potentially represents to humanity, but – a much more interesting and consequential debate – about what it actually means to be human.
That’s a deep thought. One that I hope a conference someday will explore and discuss (not debate). Arianna, if you are listening, you have an open invitation to participate in this conference.
Max Tegmark, the founder of the Future of Life Institute, whom I also follow on Twitter, had this tweet to say recommending Arianna’s blog about his book:
I agree with @ariannahuff about tech: let’s aspire to be more than vapid carbon-based consumption machines.
That is as good a Twitter summary of her article as any. Among other things, Arianna replies to a now popular thought among AI scientists that human consciousness is just an epiphenomena of brain chemistry; thus when computers and general AI become advanced enough, people can be downloaded into and merged with computers. Many scientists see no fundamental difference between intelligence that can be replicated on computers and human consciousness. Here is Arianna’s response.
Of course, there are some who believe we are nothing but machines, and that to even bring up the idea that there’s something unique or sacred about humans or human consciousness is somehow anti-science. But science and qualities like awe and wonder – which have often gone hand-in-hand with scientific discovery – aren’t antithetical. They have co-existed for millennia. Here is how the astrophysicist Neil deGrasse Tyson described it: “When I say spiritual I am referring to a feeling you would have that connects you to the universe in a way that it may defy simple vocabulary,” he said. “We think of spirituality as an intellectual playground but the moment you learn something that touches an emotion rather than just something intellectual, I would call that a spiritual encounter with the universe.”
Although I am somewhat of a Kurzweilian, I think there is more to human consciousness than intelligence. I do not see us ever creating a being by creation of intelligence alone. Many share my skepticism. Again to quote Arianna:
If humans were simply intelligent machines, they could be seamlessly blended with the most intelligent of artificial intelligence with nothing essential lost. But if there is something unique and ineffable about being human, if there is such a thing as a soul, an inner essence, a consciousness beyond our minds, becoming more and more connected with that self – which is also what truly connects us with others – is what gives meaning to life. And it’s also what ultimately determines why technological progress decoupled from wisdom is so dangerous to our humanity. …
So AI is – or should be – forcing us to think seriously about what it is to be human. And then to take steps to protect our humanity from the onslaught of technology in every aspect [of] our lives as we’re becoming increasingly addicted to our smartphones and all our ubiquitous screens.
Danger of Too Much Information
This danger of information without knowledge or wisdom is one of the themes in my theory of the impact of technology on society. Information → Knowledge → Wisdom: Progression of Society in the Age of Computers (2015); Examining the 12 Predictions Made in 2015 in “Information → Knowledge → Wisdom” (2017). I think it is the key theme of our historical era.
We are now dangerously awash in information, including misinformation and propaganda. Much of this is fueled by our increasing addiction to our smartphones, to our social media, including Facebook, Twitter and yes, even blogs like this. Foreign powers are exploiting our obsession with information. They are exploiting the gullibility and weak-mindedness of some to interfere with our democratic processes. It remains to be seen how this will all play out, but one thing is already clear, we have to be careful about the impact of AI on all of this. As Vladimir Putin said recently: Whoever leads in Artificial Intelligence will rule the world.
Our 2016 Election Was Hacked
I touched on this danger previously in my blog, New Draft Principles of AI Ethics Proposed by the Allen Institute for Artificial Intelligence and the Problem of Election Hijacking by Secret AIs Posing as Real People. There I cited the scholarly paper with the latest research by Oxford University on what happened in the 2016 U.S. election: Samuel C. Woolley and Douglas R. Guilbeault, Computational Propaganda in the United States of America: Manufacturing Consensus Online (Oxford, UK: Project on Computational Propaganda). The research proved that AI, primarily in the form of bots on Twitter, are probably responsible for tipping this close election to Trump:
Taken altogether, our mixed methods approach points to the possibility that bots were a key player in allowing social media activity to influence the election in Trump’s favour. Our qualitative analysis situates these results in their broader political context, where it is unknown exactly who is responsible for bot manipulation – Russian hackers, rogue campaigners, everyday citizens, or some complex conspiracy among these potential actors. …
The report exposes one of the possible reasons why we have not seen greater action taken towards bots on behalf of companies: it puts their bottom line at risk. Several company representatives fear that notifying users of bot threats will deter people from using their services, given the growing ubiquity of bot threats and the nuisance such alerts would cause. … We hope that the empirical evidence in this working paper – provided through both qualitative and quantitative investigation – can help to raise awareness and support the expanding body of evidence needed to begin managing political bots and the rising culture of computational propaganda.
As I said before, and feel compelled to say again, this is a serious issue that requires immediate action, if not voluntarily by social media providers, such as Facebook and Twitter, then by law. AI Ethics Work Should Begin Now. Etzioni’s proposed second rule of AI ethics should be adopted immediately: An A.I. system must clearly disclose that it is not human. How to Regulate Artificial Intelligence (NYT, 9/1/17); New Draft Principles of AI Ethics Proposed by the Allen Institute for Artificial Intelligence and the Problem of Election Hijacking by Secret AIs Posing as Real People. AI controlled bots posing as people is an immediate threat and must be outlawed now. This is fraud.
We cannot afford to have another election hijacked by secret AIs posing as real people, either on Twitter, Facebook or elsewhere. Legal changes under the current Administration are unlikely, so too are real changes by big corporate America. That means it is up to us, everyday citizens, to be vigilant against the AI enhanced propaganda. Do not count on big government to save you. Count on the Constitution and our legal system. That branch of the government is still functional and honest. Count on yourself, your inner wisdom. Self reliance is the American way. Trust that smart, skeptical Americans are a majority and will ultimately prevail over all would be dictators, imbeciles and geniuses alike. But verify. Exercise your rights, including especially your rights of free speech, assembly and to vote. Never give up and sink into mere vapid carbon-based consumption machines.
Arianna, who is herself a master of social influence and media, understands this danger all too well. Again, to quote her article:
Part of our wish list for our lives and our future should be disentangling wisdom from intelligence. In our era of Big Data and algorithms, they’re easy to conflate. But the truth is that we’re drowning in data and starved for wisdom. As Harari put it, “in the past censorship worked by blocking the flow of information. In the twenty-first century, censorship works by flooding people with irrelevant information. . .in ancient times having power meant having access to data. Today, having power means knowing what to ignore.”
e-Discovery, Truth, AI and the Law
Flooding people with irrelevant information is a situation very familiar to everyone in e-discovery. We have developed predictive coding and other AI based technologies to sort though the junk to find the key evidence. All of life has now become a document dump, one that is often covertly controlled by BIG BROTHER. AI can be used for the good to lead us out of this mess, not only for justice in the Law, but also to protect our democratic institutions. Alternatively, AI without ethics will not only mean a loss of justice, but also a loss of free elections, of the democratic processes at the heart of our way of life. We cannot and will not allow this to happen.
The Founding Fathers enacted the Bill of Rights to protect U.S. citizens from our own government. Distrust of government is hardwired into our Constitution. Follow the Constitution. Fight oppression. If you have sworn an oath to uphold the Constitution, including the all-important Bill of Rights, as I have, then be true to your oath. Trust no one. Including especially the government and its political leaders. That is the wisdom of our Founding Fathers. The same applies in spades to what foreign governments say or do, especially the dictatorships that we see in Russia and China, to name just a few. Do not count on the FBI to protect you. Be skeptical. Verify everything. Demand proof. Get to the truth. The same distrust should apply to all big organizations, including the new technology companies. Do not click away your soul. You are more than a machine. Fight addiction to social media.
Conclusion
The problem is ours to solve, both by AI Ethics principles and law, but also by our everyday conduct. Again, to quote the concluding words to Arianna’s article We’re Drowning in Data But Starved for Wisdom: We’re more than just intelligent machines:
The way to ensure a safe, beneficial and healthy relationship with technology is to begin by taking control of that relationship right now, when the technology is much more manageable. Or, as Tegmark put it, “one of the best ways for you to improve the future of life is to improve tomorrow.” We can be role models, he says, but we have to choose which sort of role model we want to be: “Do you want to be someone who interrupts all their conversations by checking their smartphone or someone who feels empowered by using technology in a planned or deliberate way? Do you want to own your technology or do you want your technology to own you? What do you want it to mean to be human in the age of AI?”
He urges us to have this discussion with everyone around us: “Our future isn’t written in stone and just waiting to happen to us. It’s ours to create. Let’s create an inspiring one together!”
5 Comments | AI Ethics, informaton, knowledge, Metadata, Technology, wisdom | Tagged: ethics, junk science, justice, law, legal profession, machine learning, predictive coding, science, technology, too much information, truth, wisdom | Permalink
Posted by Ralph Losey
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Ralph has long been a leader of the world's tech lawyers. He has presented at hundreds of legal conferences and CLEs around the world. Ralph has written over two million words on e-discovery and tech-law subjects, including seven books. He is also the founder of Electronic Discovery Best Practices, and e-Discovery Team Training, an online education program that arose out of his five years as an adjunct professor teaching e-Discovery and Evidence at the UF School of Law. Ralph is also publisher and principle author of this blog and many other instructional websites.
Ralph is a specialist who has limited his legal practice to electronic discovery and tech law since 2006. He has a special interest in software and the search and review of electronic evidence using artificial intelligence, and also in general AI Ethics. issues. Ralph was the only private lawyer to participate in the 2015 and 2016 TREC Recall Track of the National Institute of Standards and Technology and prior to that competed successfully in the EDI Oracle research.
Ralph has been involved with computers, software, legal hacking and the law since 1980. Ralph has the highest peer AV rating as a lawyer and was selected as a Best Lawyer in America in four categories: Commercial Litigation; E-Discovery and Information Management Law; Information Technology Law; and, Employment Law - Management. Ralph also received the "Most Trusted Legal Advisor" industry award for 2016-17 by the Masters Conference. His full biography may be found at RalphLosey.com.
Ralph is the proud father of two children, Eva Losey Grossman, and Adam Losey, a lawyer with cyber expertise (married to another cyber expert lawyer, Catherine Losey), and best of all, husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.
