Tag: Artificial Intelligence and Legal Technology

Pitfalls in AI?

Artificial Intelligence (AI) is the fastest-growing eDiscovery solution in the Legal Industry. Just like in Henry Ford’s day, it’s the keen cutting edge shaving away costs by reducing time spent from evidence to production. Use AI and don’t land in the pitfall.

“Competition is the keen cutting edge of business, always shaving away at costs”…Henry Ford

Is there a pitfall if you use AI? Computer Forensic Experts Lee Neubecker interviews Chief Innovation Office with DISCO, Cat Casey both agree the largest pitfall in AI is NOT embracing AI! Artificial Intelligence (AI) is the fastest-growing eDiscovery solution in the Legal Industry. Just like in Henry Ford’s day, it’s the keen cutting edge shaving away costs by reducing time spent from evidence to production.

Cat explains DISCO was born out of the firm’s frustration with conventional eDiscovery tools that were slow and difficult for lawyers to use. Instead of being forced to adapt our work methods to technology, we wanted to invent technology that works the way lawyers work. DISCO was the result, and today we are the fastest-growing eDiscovery solution in North America. Both experts agree implementing AI will help companies gain a competitive edge. Watch this video to hear examples of how AI helps sharpen that edge!

Final Part of our 3-Part Series in Artificial Intelligence: Pitfalls in AI

The Video Transcript Pitfalls in AI Follows.

Lee Neubecker (LN): Hi and welcome back again Cat. Thanks for being on the show again.

Cat Casey (CC): My pleasure.

LN: Cat Casey from CS Disco. She’s a Chief Product Innovation Officer. Did I say that right?

CC: Chief Innovation Officer.

LN: Okay.

CC: Products too, though. It’s fine.

LN: They call her chief.

CC: They should.

LN: So we’re going to talk now, in this last part of our series on artificial intelligence, about some of the challenges of organizations that don’t adapt and don’t get on board. So, what do you see the potential risks and pitfalls for law firms that don’t begin to embrace so sort the form of a technology-assisted review or artificial intelligence to help speed up the review process?

CC: Well, at a very basic level, clients are getting smarter. We’ve got CLOC https://cloc.org/, we’ve got clients talking to each other more, and they’ve raised their expectations of how their firms are going to be competitive. And it used to be if you were big law firm A you would always have this corporate client for every anti-trust case they would always go to you. But now I was getting dozens of RFPs where they’re asking me what technology are you using? How are you driving innovation? How are you driving efficiency? Because there is a higher expectation of competition between outside counsel. That, maybe, wasn’t there a few years ago. And so, the client expectation is driving this appetite to investigate eDiscovery and Artificial Innovation (AI) based innovation in a way that wasn’t here a few years ago.

LN: Has there been any industry research that has attempted to benchmark the cost of a case using an AI platform to speed up review versus not, to your knowledge?

CC: You know. I can speak from Disco, and we see about a 60% reduction in time to evidence to production. And that translated to dollars. And so, I mean, 60% savings on the 80% of a case that is reviewed is substantial. The thing that I think is most important is cost-savings big, but getting evidence quicker.

LN: Yeah. Time is of the essence.

CC: That is the thing that is paramount because of a lot of these companies… I worked at a company that had very big budgets, but no amount of money, no amount of people, was going to be enough to get these insights I needed before the meet and confer. Or before I had a critical filing with a government investigator. And so, getting evidence quicker so I can start building my case, was the differentiator.

LN: Yeah, certainly if you’re working for a company facing a DOJ inquiry.

CC: Yep.

LN: Knowing the good, the bad, the ugly.

CC: Yep.

LN: As soon as possible can help you make better decisions for your clients. Which might involve, you know, settlement. settling. Yeah, yeah. There have been many recent settlements, recently, from big companies that didn’t want to get tied down at least.

CC: Well I’ve had cases where… One of my favorite ones I used tons of different AI and analytic tools. I had a big bank that had been fined billions of dollars and another big bank was, they had hired on people in that same group, and they were wondering if they would be subject to the same investigation. So, I did some social network analysis. Who was talking to who, with what frequency? I parsed Bloomberg’s chat. I parsed audio logs. And I used everything to keep triangulating down until I was able to identify the bad actors, saying the bad things, and the map of the structured data to show they didn’t do the bad things. And my company wasn’t on the front page of the Wall Street Journal. My company wasn’t fined. So it ends up being very compelling, even early in investigations.

LN: Yeah. Certainly responding quickly is important now. Have you seen any success stories as it relates to companies embroiled with data breach incidences, that have used your platform to help get ahead of what was going on?

CC: 100%. I mean PII, so personally identifiable information, is something that you’re going to have to notify if there is a breach. So if someone, say your Equifax, not that I’m naming them, but say you’re a big company with a lot of personally identifiable or health information. You need to identify it quickly, notify these people in their specific timelines. Tools, like Disco’s, help you use algorithms to find that quickly and act upon it. Otherwise, if you’re looking at 100 million records, there’s no amount of humans that could go through that, in a timely manner, where you’re going to comply with time obligations. And so, it’s majorly impactful.

LN: That certainly is. Well, are there any other things you want to say on the show before we wrap up?

CC: You know, adapt. The reality is no one wants to be the buggy whip maker in a Tesla world. The time to start investigating and vetting and ensuring that the tech you’re looking at isn’t hype is now. Because in a year, or three years, or four years, you might be behind the curve. So, find your resident dork, ask questions, dig into the tech. Now is the time.

LN: And it’s probably worthwhile, you know, without being biased towards Lit Funder, why not take a case try out Disco, try out another offering to see what really works. I mean you had the benefit of…

CC: Yeah.

LN: You were on the other side working for the law firm, shopping for vendors.

CC: I did a 55 vendor RFP. I’ve seen everyone. I’ve looked under every hood. I mean there’s a reason I went to Disco. But there are other tools good out there. I think you want a toolbox with lots of different tools. If you’re a hammer, everything looks like a nail. Let’s be honest, litigation is always bespoke, so you want lots of tools that can help you address it.

LN: Great. Well, thanks again for being on the show.

CC: Yeah, my pleasure.

LN: This was great.

Watch the Entire Series on Artificial Intelligence (AI)

Part 1 of our 3-Part Series on AI

Re-inventing Legal Technology: Artificial Intelligence (AI)
Part 1 in our 3-Part Series on AI

Part 2 of our 3-Part Series on AI

AI Trends in the Legal Industry
Part 2 on our 3-Part Series

Other Related Articles

AI Smarter Solutions: eDiscovery
Optimize eDiscovery With AI

DISCO’s website

https://www.csdisco.com/about-us

The Association for the Advancement of Artificial Intelligence

http://www.aaai.org/

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

AI Smarter Solutions: 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

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

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Re-inventing Legal Technology: Artificial Intelligence (AI)

Forensic Experts Lee Neubecker and Cat Casey from DISCO discuss Artificial Intelligence (AI) as it relates to improving Legal technology.

Artificial Intelligence (AI) thinks, learns and problem solves more efficiently than humans. AI is all around us and in almost everything we touch, it is an algorithm that is designed to make our lives easier and is sometimes referred to as machine learning.

In the case of litigation, it can save time and money by streamlining the process of document review, eDiscovery, and preparation for forensic cases. Computer Forensic Expert, Lee Neubecker and Catherine “Cat” Casey who is the Chief Innovation Officer for DISCO discuss how AI works to improve legal technology.

DISCO is a leader in legal technology is a developer of a cloud-native eDiscovery software for law firms designed to automate and simplify error-prone tasks. They provide a myriad of different types of analytics that will supercharge searching data dramatically reducing time and money.

Part 1 of our Three-Part Series on Artificial Intelligence (AI)

Artificial Intelligence (AI) Re-Inventing Legal Technology

The Video Transcript Follows.

Lee Neubecker (LN): Hi, I’m here today with Cat Casey from CS DISCO. Thanks for being on the show.

Cat Casey (CC): My pleasure.

LN: We’re going to talk a little about artificial intelligence as it relates to eDiscovery and document review. Cat, can you tell us just a little bit about what your firm does to help speed up the review process and lower costs for clients.

CC: Absolutely, we’re a cloud-native AI-powered eDiscovery company. And what that means is we’ve got vast amounts of elastic computational power that we can use to run a myriad of different types of analytics on data to supercharge your searching and dramatically reduce the amount of time it takes you to get to that key actionable evidence. So, we’ve kind of flipped everything on its head. Instead of being a question of how quickly can I read through all of this data, it’s how laparoscopically can I surgically find all of that key information. The results that we’re seeing are pretty resounding. Up to 60% reduction in time to get to that key evidence. Freeing up attorneys to get back to what they went to school for, the practice of law. It’s pretty compelling. We’ve had some pretty interesting additions, including even today, we just announced, I think, the first true AI in eDiscovery with AI model sharing. Basically, with each iteration, with each type of case that you conduct with DISCO, our algorithms are getting smarter. We’re extracting insights and building in more robust taxonomy and analytic structure to parse data, which is going to yield better and better results for our clients. It’s truly exciting.

LN: So we’ve come a long way from the early days when the attorneys wanted everything printed and Bates-labeled before they looked at it. To now, moving ahead using TAR, technology-assisted review, like artificial intelligence, which fits into that, correct?

CC: 100%, we have a continual active learning model, so it’s more reinforcement learning than a standard supervised learning model. Basically, from the coding of document one, our algorithm’s getting smarter and making recommendations on highly likely to be similar documents. We battle test the algorithm on an ongoing basis. Whether it is an affirmative or a negative for a suggested document, the algorithm learns more, and because of that, we prioritize the most relevant information quickly and people are able to then accelerate their review speeds by up to, I think we’ve had over 180 docs per hour. So, it’s pretty compelling and this is just the beginning.

LN: So your platform’s all in the cloud, correct? So companies or law firms, they need no infrastructure other than a browser?

CC: 100%, the nice thing, in my prior life, I ran a global discovery program, and I spent hundreds of thousands of dollars a year just to keep pace, just to have storage, just to have basic replication and back up, and all of that. Now, even a small firm, all the way up to an Am Law One firm or a massive Fortune One company, they can have the same robust technology without having to set up a data center, without having to invest a ton of money. It lets everyone level up and has a better experience throughout the discovery process.

LN: One of the challenges a lot of my clients always have is they have a need to understand what the costs are going to be and to be able to communicate to their clients those expectations so they’re not throwing their clients on the eDiscovery rollercoaster of non-controllable bills. How does DISCO help to address those concerns?

CC: Transparency is a major pain point. One of the banes of my existence used to be trying to normalize this pricing model versus this, versus this service provider, versus this technology. We just throw that all out. We charge one flat amount per gig. It includes analytics. It includes processing. It includes everything, and we work with you to get the volume of data that is being applied to that one flat cost per gig down. It eliminates that hide the ball gotcha moment and it gives a lot of transparency. And of course, if someone wants a different model, we’re happy to accommodate that. But in general, straight, simple, honest. It’s really rewarding for our clients.

LN: So, what cases, what types of litigation case matters do you see as having some of the best benefits of being migrated into your platform?

CC: Yeah, I think any case can. If you’re a tiny company, it helps you be David versus Goliath. Even on a small data volume case, you can start getting insights and reduce the amount of time you’re having to spend doing something maybe you can’t chargeback for. For a big massive case, because we are an AWS and we were built on kind of convolutional neural networking, we’re moving, and we have such a robust computational lift, even we’ve had 150 million documents with hundreds of users and we still have sub one second page to page. We are still lightning fast. And so, whether it’s a big case, a simple case, a complex case, there is a value proposition for almost anyone.

LN: In terms of the types of law firms that are using your platform, do you see many smaller, medium-size firms using your–

CC: Tons, actually tons. That was where we got our teeth. Boutique, we started as a boutique law firm. We actually were a bunch of attorneys that were frustrated that all the tools were terrible, and so they built their own. And so, the foundation of DISCO, we had a family of tons of boutique law firms that we were supporting, we still do to this day. The tool we built though, had a longer vision. It was built to be much bigger and more scalable, and as a result, that’s why you’re seeing us with major, the WilmerHales of the world, very large firms and very large corporations because the tool itself can scale up so much.

LN: Great, what are some of the challenges of working, that law firms find that already have entrenched solutions? There are other review products out there and if they really want to make the benefit of your platform, don’t they have to kind of fully use it for the case?

CC: I would say you probably don’t want to split the baby with a case. If you’re processing with another tool, you’re not going to get the same benefit as working with DISCO. But you don’t have to move your entire litigation portfolio to DISCO day one. We’re seeing a lot of people that are sunsetting Legacy Product and Legacy Platforms moving towards DISCO, but it’s not, “I’m going to move every single case today.” It’s going forward, we’re going to start bringing in new cases. There tends to be such an improved experience and improved UI for the attorneys that they start to not want to use the other technology as much.

LN: I know as a computer forensic expert, oftentimes we’re going out initially collecting and forensically preserving the data. But your product sounds like it would be right for a firm that does forensics that needs to collect different data from computers, possibly harvest just an email. Filter the dates and times of the email to a PST and then they can take those PSTs and upload it into your platform, correct?

CC: 100% and we also, we’ve productized some advanced ECA, where we charge a much, much lower rate. So, you get three months no cost hosting. It’s half the usual rate, and you can do ECA for up to three months. And the goal of that is to let’s whittle down to the most surgical, teeny, tiny, laparoscopic piece of data set that you can have. An example was we had a 20 million document case and we were able to run the ECA, get it down to about 5.6 million documents. Run more coaling, run our analytics, get it down to about 200,000 documents. And usually, that would be when you have to review every single one, but we were able to, with our workflow, with CAL, get it down to 140,000 documents. And so, if you think 50 bucks an hour, an attorney can only do 50 docs an hour, the cost savings is monumental.

LN: So as someone uses your platform and they start to tag and prioritize certain documents, your software learns based on that taking. It helps find related concepts to those conversations and what not?

CC: 100%, 100%.

LN: So really, the more that are reviewed as responsive, similar concepts and whatnot so that important links aren’t missed.

CC: 100% and because we do automatic batching, is every new batch of documents a person gets because we’ve applied this artificial intelligence and continual active learning model, it is a more relevant subset of data and people are able to go through it more faster. And sometimes, they will get to a point where they can say, “I’ve hit all my relevant information. “The rest is not relevant. “I’m going to sample it and statistically determine “I don’t have to review those last 100,000 documents “that maybe aren’t relevant,” and it’s pretty cool.

LN: In our next segment, we’re going to be talking What the trends are in the industry impacting law and eDiscovery. And then finally, we’ll talk about some of the pitfalls of what companies, organizations, and law firms face if they don’t embrace artificial intelligence to help make their review process more efficient. Well, thanks for being on the show.

CC: My pleasure.

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