Optimizing eDiscovery With AI

You’re looking for the smoking gun and have tens of thousands of documents to review. Experts Lee Neubecker and ZyLAB’s eDiscovery Director, Jeffrey Wolff say Optimize with AI and make your review easier!

Optimize with AI! Lee Neubecker sets out on a quest to find out what’s happening with Artificial Intelligence as it relates to the eDiscovery review process. Lee visits eDiscovery Director, Jeffrey Wolff from ZyLAB and together they examine how new AI algorithms are coded for priority review and can rank documents for relevance, saving countless hours and dollars for the client.

Part 2 of 3 Part Series on Smarter Solutions eDiscovery

Part 2 of our 3 Part Series on Smarter Solutions in eDiscovery

Optimizing eDiscovery with AI Video Transcript Follows

Lee Neubecker (LN): Hi, I have Jeff Wolff back on the show again from ZyLAB. Jeff, thanks for coming back.

Jeff Wolff (JW): Thank you.

LN: And today we’re going to talk a little bit more about trends in Artificial Intelligence as it relates to eDiscovery and the review process that comes along with that. Jeff, what do you see happening right now with Artificial Intelligence as it relates to the eDiscovery review process?

JW: So what we’ve noticed over time is that, traditionally, Artificial Intelligence was always deemed to be only valid in cases where you had hundreds of thousands or millions of documents. And one of the changes that have happened over the last few years is that the Artificial Intelligence models have gotten so much better than you can now use them for much smaller data sets, and so we evangelize the use of Artificial Intelligence in smaller data sets, even, a thousand documents, you’re going to get a better review, more efficient, and more correct, faster, with AI than you would with a team of reviewers.

LN: So if you have a project and you’re using your platform, let’s say there are a million pages of documents that need to be reviewed. You put a review team on starting that process, and they start categorizing and coding, as they get through the first ten thousand documents, what is your software doing to help make this process more efficient and effective for them?

JW: Sure, so if you’re using traditional, what we call supervised machine learning, that used to be referred to as predictive coding, what our software allows you to do is train a small training batch, so a small sample of the documents, and code them for responsiveness, whether they’re responsive or not responsive. And we’ve made it very easy for users to do that. So, you can create issues, and for each issue, you get two tabs, responsive or not responsive, and you just train, you look through a bunch of training documents and you tag the documents appropriately, and the machine classifier learns, very quickly, what is responsive, what is not responsive. So, maybe after two or at most three training batches, the classifier is now bringing you back almost exclusively responsive documents. It’s already smart enough to do that. And so you only need a few training rounds to get the classifier well over the 80%, typical 80% precision and recall threshold that most attorneys feel is what the human is capable of, but the machine will do 90, 95% precision and recall, so you can be assured, not only are you getting a more efficient and more correct review, but you’re also doing it in a whole lot less time with a whole lot fewer people.

LN: And so, are your algorithms looking for synonyms, and similar phrasing that has equivalent word matches?

JW: It’s a bit of secret sauce. But, yeah, we use a support vector machine-based set of algorithms, kind of the most modern version of machine learning. And it is effective, it understands what our topics that were identified in the document, and what other topics are like them. So that’s how it’s doing an identification. But you’re effectively training in or on that.

LN: So the people using your platform, are they having to necessarily review all of the documents, or are you basically, based on the trained review process, you’re taking that universe of a million, and as they get through it, it’s starting to cluster.

JW: Correct.

LN: There’s a set that, this probably isn’t useful, and you don’t have to look at it, but you can look through it just to see.

JW: Sure.

LN: They have confidence that it’s not excluding relevant stuff, right?

JW: Yeah. What we find from an AI standpoint is that the two primary use cases that attorneys have when they use AI are priority review, so that means hey, I’m going to start teaching the data about, the classifier about my data set, and I’m going to show what responsive documents look like, and then I want it to rank all the remaining documents for me for relevance. And so I’m going to then put eyes on those top-ranking documents. That’s effectively looking for the smoking gun, right? That’s one. But they also use it a lot for QC and this is where I see I’m trying to put a lot more attorneys into utilizing AI, is you’ve already done your tagging, and you had eyes on all of your documents, now go back and use the AI and compare it against what your human reviewers did, and see if you’ve missed things. Because inevitably, your reviewers are not going to be all at the same level. Some people are going to miss-tag documents, and the AI has a really good chance of picking up those mistakes and showing them to you.

LN: So have there been any published studies that document the effectiveness of AI with the review process?

JW: There’s been a bunch of them. I know Law Geeks did one that was pretty interesting. What I’ve read recently is that only about, nationally, about 4% of all cases use Artificial Intelligence officially. But then again, there’s no requirement, in the meet and confer that you identify that you are using Artificial Intelligence in a discovery case. So a lot of attorneys can be used, and just not reporting it. Which is fine, because back when the review was manual, and you went through paper and bankers boxes, you didn’t have to document the process for that review. So why should you have to document the fact that you using a machine to do some of the identification of documents and responsiveness today?

LN: So are there potential problems as a result of using AI for failing to produce relevant documents?

JW: No, I think the case law already demonstrates that AI is an accepted form of using, of identifying reviewed documents, and again, even if you’re just using it for QC purposes, you’re still better off. You’re still less likely to miss things than if you hadn’t used it at all.

LN: Great, well, it’s been great. Thanks a bunch for being on the show.

JW: My pleasure, my pleasure.

View Part 1 of our 3 Part Series on Smarter Solutions in eDiscovery

Part 1 of our 3 Part Series about Smarter Solutions and eDiscovery

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AI Smarter Solutions: eDiscovery

Artificial Intelligence (AI) can be used to vastly improve the eDiscovery document review process. Zylab is one of several eDiscovery vendors offering solutions utilizing AI. Lee Neubecker, Computer Forensic Expert, and President & CEO of Enigma Forensics met with Jeffrey Wolff, Director of eDiscovery Solutions at ZyLAB during his visit to the Legal Tech Conference 2020 in New York. Lee and Jeffrey discuss how AI can be used to conduct more effective eDiscovery.

Artificial Intelligence (AI) technology is everywhere. It’s hard to imagine how it’s being used in the legal industry where legal libraries filled with law books and courts filled with black-robed judges reign. In this formal traditional world, AI is now providing smart solutions for today’s electronically stored information or ESI and is streamlining the way the Legal Industry works.

In this video, Lee Neubecker, Computer Forensic Expert, and President & CEO of Enigma Forensics met with Jeffrey Wolff, Director of eDiscovery Solutions at ZyLAB during his visit to the Legal Tech Conference in New York. Lee and Jeffrey analyze how Artificial Intelligence (AI) develops smarter solutions in the eDiscovery process. Jeffrey shares with Lee that ZyLAB’s mission is to provide automated full-text retrieval using AI, for both on-premise or cloud-based solutions.

Watch Part 1 of a Three-Part Series on Artificial Intelligence (AI) and eDiscovery.

The video transcript of AI Smarter Solutions: eDiscovery follows.

Lee Neubecker: Hi, I have Jeff Wolff, back on the show from ZyLAB. Jeff, thanks for coming back on.

Jeff Wolff: Thank you.

LN: He’s their Director of eDiscovery, and I wanted to ask him some questions as it related to what differentiates ZyLAB from other products out on the market. Some of my clients may want to use this type of artificial intelligence program to help get through their review and see what the results are of using AI verse the traditional e-discovery review process, so.

JW: Sure.

LN: Jeff, could you tell us what sets ZyLAB apart from other competitors in the marketplace.

JW: Sure, sure, so first, I think ZyLAB is uniquely positioned in the fact we understand the corporate space quite well, as well as the law firm space, but we got our start incorporate, or start in information governance. So we are very vested in search and data science, and that’s really where we’ve put a lot of our focus. We have both on-premise solutions, as well as cloud-based, SaaS solutions like every other next-gen provider. But we really push our interface, our user interface and our user experience, as one of the most unique selling points. And that is, that it is not difficult to start using. Anyone, any legal professional can pick up our product in an hour, from start to finish, and understand really how you utilize it. Drag and drop interfaces for getting data into the system, and immediate color-coding and tagging, easy search, and the ability to really visualize your data and understand what’s in the dataset.

LN: Okay. So, what would you say for a company that has to deal with multiple jurisdictions, they’re in Europe, they’re in the US. JW: Sure. LN: There are some unique challenges posed by all the various regulations out there, like GDPR.

JW: Right.

LN: Maybe the have operations in China. How could you help a company that has to deal with various regulatory authorities spanning the globe?

JW: Sure, and that’s another advantage that ZyLAB has, actually, we’re actually a global company, so we’re dual-headquartered in Washington, D.C., here in the US, as well as Amsterdam in the Netherlands, in the EU. And as a result, we have cloud operations in both jurisdictions. So our global customers can actually keep US data in the US, and they can keep the European Union in the EU, and not worry about that issue. But we also have the expertise, consulting expertise, in both environments, both geographic locations. For example, I’m doing a lot of work now with corporations, not so much focused on directly just on e-discovery, because e-discovery is a bit reactive, you know? Or corporations go through peaks and valleys with e-discovery, the litigation, something they have it, sometimes they don’t. What they constantly have though, are internal investigations, regulatory responses, in the highly regulated corporations. And more and more now, data privacy concerns. So, my European colleagues have been dealing with GDPR for a while, we’re now starting to feel it here in the US, with CCPA, the California Consumer Privacy Act. And there are a number of states on the horizon that are going to California’s examples, so corporations need to be able to find, and classify all the data that they have in their organization that has customer information because if those customers request it and they can’t provide it, they’re financially in a lot of trouble.

LN: Do you think that the regulations coming down on companies are going to fundamentally change how companies chose to communicate with their vendors, suppliers, and own employees?

JW: Absolutely. If you look at all the recent data breach situations, it’s typically not the organization that has the problem, and I won’t mention any of the large companies that have recently had data breaches, but it’s typically not the original company that had the issue, it’s one of their suppliers, or one of their vendors that had accesses to the database, and wasn’t protecting it properly, and that’s how the trouble began.

LN: Yeah.

JW: Same thing with data privacy.

LN: The supply chain certainly is a huge point of vulnerability for all types of organizations. The governments, the military,

JW: Yep.

LN: and even corporations.

JW: Yes.

LN: So what do you see happening over the next few years with the adoption of AI platforms?

JW: I think the e-discovery market is going to fundamentally change. There’s still always going to be a need for discovery within corporations and law firms, but what you do you with the data is going to become much more important, so it’s going to be about how you can extract value from the data, not just metadata, which we’ve always been able to do for years now, but now more about looking for entity information. People, places, organizations that are mentioned in documents and emails, and collaborative environments, and being able to visualize those, and quickly drill down to what was going on in your organization. You know, if you got people that are going to the dentist three times a week, they’re not doing to the dentist, they’re doing something else, They’re just writing about going to the dentist.

LN: Yeah.

JW: Software like ours that can identify those references in documents are going to be crucial to the success of organizations.

LN: That’s great. So it seems that there’s continued e-discovery service provider consolidation out there.

JW: Mmhmm.

LN: The companies that are using tools that are more of a channel partner tool to resell.

JW: Yes.

LN: But as those companies consolidate, do you think that there’s going to be a movement away from those providers where, the company, the firms, directly do their own e-discovery?

JW: Oh, yes. Yeah, very much so. We’ve been seeing that over the last few years. A lot of companies, even small companies that tend to have, in the past, just used outside vendors for e-discovery, are now deciding that they prefer to control, not just the cost, but also their data. They don’t want their data outside of the organization for reasons we’ve already talked about. So they’re purchasing in-house tools that they can use themselves, and then they can invite outside counsel in to make use of, that way they control their costs, they control the efficiency, and they control the data.

LN: Well, this has been great. Thanks a bunch for being on the show.

Lee Neubecker: Thank you again.

LN: Take care.

JW: Bye bye.

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View ZyLAB’s for more information on (AI) Smart Solutions: eDiscovery

https://www.zylab.com/en/product/artificial-intelligence

View Law Technology Today’s article on Artificial Intelligence (AI)

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