Optimize 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 eDiscovery 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. Utilizing new AI will optimize your current eDiscovery process.

Part 2 of 3 Part Series on Smarter Solutions eDiscovery

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

Optimize 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 Trends in the Legal Industry

AI trends in the Legal Industry is revolutionizing data, and whittling down the amount of paperwork involved in legal practice. Lee Neubecker and DISCO’s Cat Casey discuss trends in the legal industry.

Paper death! Legal professionals get buried in a mountain of paperwork. Artificial Intelligence (AI) replaces that mountain of paper with cloud-based apps and whittles down costs. What’s new in Artificial Intelligence (AI) as it relates to the legal industry? Check out this video as Forensic Expert Lee Neubecker and DISCO’s Information Officer Catherine “Cat” Casey talk through AI trends in the legal industry.

View Part 2 of our 3 Part Series on Artificial Intelligence (AI) in the Legal Industry

Artificial Intelligence (AI) in the Legal Industry

The video transcript AI Trends in the Legal Industry follows:

Lee Neubecker: Hi, I’m back here again with Cat Casey from CS Disco. Thanks for coming back again.

Cat Casey: My total pleasure.

LN: We’re going to continue our conversation in this multipart series. This time, we’re talking about artificial intelligence and the trends impacting the legal industry and the whole eDiscovery industry as well.

CC: Absolutely, so in my role at Disco, I’m chief innovation officer, and one of the things I’m tasked with doing, both now and in my prior roles, is going out and figuring out what’s going on in the market, and what we’re seeing is AI written everywhere. Sometimes it’s true AI, sometimes it’s not, but what we are seeing is people want to find evidence faster. People want to eliminate those low-hanging tasks that aren’t the practice of law. And so, we’re seeing a lot of tools that are driving efficiency both in practice management and litigation management and in finding evidence.

LN: So where do you see we’ve gone in the last few years with AI in terms of advancements and providing products for the review process?

CC: When we first, I think, announced AI about 2006, seven, eight, nine, I was working as a channel partner with the company that patented the word predictive coding. That was the first AI model in eDiscovery and people liked it. They didn’t really want to use it. They were nervous. What I’ve seen is not only has the process improved instead of TAR 1.0, where you have a sample, you make decisions, and then, the algorithm might learn, we have continual models. So the tools got better, but the appetite to use them has increased dramatically, I think, in the last 18 months, because data’s getting very big, very complicated, and no amount of money or time is enough to actually get through it without using this sort of technology.

LN: So are you seeing that other messaging platforms are starting to become more a part of this process, like Slack?

CC: Oh, yeah.

LN: You’ve got all kinds of other messaging platforms, WhatsApp.

CC: Weird data is the new normal and I noticed it starting, I’ve been at Disco about a year, so starting my last 18 months at Gibson Dunn, where it used to be, okay, email, maybe text. That’s all I got to worry about. No, no, no, now I’m dealing with ephemeral messaging, which is self-destructing text messages. I’m dealing with collaboration tools like Slack and Messenger and Teams and each one of these tools has a challenge in terms of formatting the data, being able to review it, and relating it. Think of a given day. This morning, I was on Slack, then I was answering text messages, then I had a phone call, then I sent an email, then I went back to my Slack channel. That was before I got out of bed and if you want to recreate kind of this digital footprint of what people are doing, you need to have all of that info. And so, finding tools and partners that can deal with it is paramount.

LN: So does your platform at Disco, does it have APIs and import specs that match upon those alternate data streams?

CC: We do to a degree. We also do kind of a middleware layer of parsing and creating a new visualization, like say from a JSON file for Slack, we recreate that in our ecosystem and render it the way you would’ve seen it in the Slack dialogue box. And so, we’re developing more of those direct APIs of a 365 box, but we’ve worked on the visualization and ensuring that the data we receive is reviewable, usable, and easily rendered, so.

LN: Now, it’s interesting when we’ve collected cellphone data, we’ve used some of the popular tools on the market and the output of the data isn’t necessarily always easy for the attorneys to review. And what we’ve done is we’ve often taken the spreadsheet output of text.

CC: Oh yeah, yeah.

LN: So what are some of the challenges you see facing AI and its adoption over the next few years?

CC: Like with everything, it’s fear and desire. People desire the outcome of finding stuff faster, being able to practice law, but no attorney went to law school to play with relational databases and lambda calculus. I didn’t. And so, what ends up happening is there’s a fear of the unknown and a fear of explaining something to a judge who maybe didn’t even use a laptop when he was going to law school, probably didn’t. So there is a fear of using technology that folks don’t understand, a fear of explaining it, and that’s when having the right partner, the right person to testify, the right person to navigate you through this becomes so important.

LN: Have you seen much, part of my practice deals with patient electronic medical records?

CC: Oh yeah, yeah.

LN: And patient audit trails of EMR, electronic medical records.

CC: Oh, yeah.

LN: Usually, those records aren’t quite like an email thread. They’re more cryptic. They’re more accustomed to the specific platform the hospital’s use. Have you seen many of those cases come in where they’re pulling in the charts and various transcripts from the physicians and whatnot?

CC: I haven’t run into that as much at Disco, but when I was at PWC, we were doing very complex multilayer investigations, and so, we would have, sometimes, medical charts. Sometimes we would have trade databases and so, marrying and creating a story between that structured data and the unstructured data was always very challenging and very bespoke, and there’s some tech that’s beginning to create a unified place to do that. We’re looking in to do that as well, but it’s very hard to take that weirdly formatted data and render it in a way that then ties to what the humans are saying and then, help you get those facts to build your case.

LN: That’s great. Well, this has been great. In our next segment, we’ll be talking a little bit more about artificial intelligence and some of the potential challenges and impacts for organizations that don’t get on board. So thanks for coming on again.

CC: My pleasure.

View Part 1 of our 3 Part Series on Artificial Intelligence (AI) in the Legal Industry

Part 1 in our Three-Part Series about Artificial Intelligence (AI) in the Legal Industry

View Other related blogs from Enigma Forensics.com

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Artificial Intelligence (AI) in Hospitals
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View DISCO’s website and receive a free demo

https://www.csdisco.com/

View Law Technology Today LTT as it reviews AI trends in the Legal Industry

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