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Revolutionizing Legal Predictions

How AI is Transforming Litigation Risk Assessment and Prediction

Pre/Dicta takes a radically different approach to predictive analytics than others

Victor Li, an assistant managing editor, joined the ABA Journal staff in 2013 as a legal affairs writer. He manages the magazine’s Business of Law section. Victor previously reported for Law Technology News, the American Lawyer magazine, and Litigation Daily in New York City. He is the author of the book “Nixon in New York: How Wall Street Helped Richard Nixon Win the White House.” A former prosecutor in the Bronx, Victor earned his JD from Tulane, an MS from Columbia University School of Journalism, and a BA in history from Amherst College.

Victor’s extensive background in both journalism and law provides him with a unique perspective on the intersection of these fields. His work covers a wide range of topics, from courtroom technology to the future of diversity, equity, and inclusion programs in the legal industry. His expertise and insightful analysis make him a respected voice in legal journalism.

Podcast Summary

Victor Li of the ABA Journal Legal Rebels Podcast discusses with Dan Rabinowitz, CEO and co-founder of Pre/Dicta, about how their AI-powered judicial analytics tool is setting new standards in litigation prediction. Unlike traditional analytics that rely solely on past rulings, Pre/Dicta incorporates judges’ political affiliations, net worth, career backgrounds, and other personal data to predict case outcomes with approximately 85% accuracy. This innovative approach allows lawyers to better assess the potential costs and strategies for their cases, providing a significant advantage in litigation planning and client advising.

Rabinowitz explains that Pre/Dicta’s model transcends simple statistics by leveraging big data and machine learning to understand non-obvious patterns in judicial behavior. This forward-thinking methodology not only enriches legal data analysis but also addresses the hesitancy lawyers may have in adopting analytics tools. As the legal industry grapples with the integration of technology, Rabinowitz’s insights emphasize the importance of predictive analytics in enhancing the precision and efficiency of legal practice.

ABA Journal – Legal Rebels Podcast Link: https://www.abajournal.com/legalrebels/article/rebels-podcast-episode-088

  • Legal analytics and its future with a focus on predicting judges’ decisions. 0:02
    • Dan Rabinowitz, CEO of Pre/Dicta, discusses AI-powered legal analytics tool that considers judge’s personal data to predict rulings.
    • Speaker 2 discusses judicial analytics, using data beyond opinions to predict future judge decisions.
  • Legal analytics adoption and limitations. 3:22
    • Speaker 1: How widespread are legal analytics tools within the industry? (0:03:22)
    • Speaker 2: Judicial analytics tools have been adopted by many firms, but adoption is limited (0:05:45)
    • Speaker 1 suggests lawyers may be hesitant to use judicial analytics tools due to concerns about relying on past performance for future decisions.
    • Speaker 2 agrees, citing limitations of the current system and the need for more comprehensive data to make accurate predictions.
  • Predicting judges’ decisions using data and AI. 7:59
    • Speaker 2 highlights limitations of other predictive analytics platforms in the legal field, including reliance on historic statistics and lack of prediction capabilities.
    • Speaker 2’s platform uses advanced technology to predict legal outcomes with a high degree of accuracy, differing from other platforms that rely solely on historic data.
    • Speaker 2 explains that predicting a judge’s ruling is complex, requiring multiple data points and machine learning to identify non-obvious patterns.
    • The approach is similar to how Google provides targeted ads based on a person’s historic buying patterns and demographic information.
  • Predicting judicial decisions using data-driven approach. 13:30
    • Speaker 1 wonders how to balance data points, precedent, and judge’s personal experiences.
    • Speaker 2 prioritizes parties and attorneys over legal theories and precedent in predicting judges’ decisions.
    • Speaker 2 predicts judicial decisions with 85% accuracy using algorithmic models.
    • Speaker 2 takes a different approach to the current polarized and political state of the judiciary.
  • Judicial decisions, political polarization, and appointment vs. election of judges. 17:57
    • Speaker 2 argues that most judges’ decisions are not driven by politics, despite political polarization.
    • Data analysis of state courts is needed to determine if appointed or elected judges make different decisions.
    • Speaker 2: Data shows unexpected patterns in judicial appointments, such as women appointed by Obama ruling in favor of corporations at same rate as Republicans.
    • Speaker 2: Analysis of presidential appointments reveals differences in selection process and decision-making, leading to varied outcomes.
  • Predictive analytics for judicial decisions. 22:46
    • The company’s predictive model was accurate nearly 85% of the time for motions not included in the model training.
    • The model lost accuracy for newly appointed judges, but still performed well overall due to a two-pronged approach.
    • Speaker 1: Judicial analytics will become more widespread amongst lawyers in the field.
    • Speaker 2: Predictive models will see greater adoption, but leave data science to experts.
    • Dan at PreDicta discusses the importance of lawyers understanding predictive analytics for their clients.


Victor Li, Dan Rabinowitz


Victor Li  00:02

Welcome to the ABA journal legal rebels podcast, where we talk to men and women who are remaking the legal profession, changing the way the law is practiced and setting standards that will guide us into the future. There are plenty of traditional analytics and litigation prediction tools on the market. They may have differences in execution or focus. But the general rule of thumb is that they look at a judge’s past rulings and opinions to predict how that judge might rule on a similar motion or case in the future. For instance, you can look up how judge so and so roll on prior motions to dismiss on certain employment discrimination cases, to get an idea how they might want a similar one currently pending in their courtroom. That knowledge can be important for lawyers not only does let them evaluate their case, and determine whether it’s worthwhile to go to trial or settle. They can also provide the client with some certainty as to how much it might cost them based on what they’ve charged in the past for similar matters. But that litigation analytics tool Pre/Dicta launched in 2022, with a different approach. In addition to looking at their rulings and jurisprudence Pre/Dicta also examines the judges political affiliation, net worth, area of residence, career, and other personal and demographic data. My name is Victor Lee, and I’m assistant managing editor of the ABA journal. Joining me on today’s episode of the ABA journal legal rebels podcast is Dan Rabinowitz, CEO and co founder of Pre/Dicta. He’s here to talk about what sets Pre/Dicta apart from its competitors, as well as discuss the field of traditional analytics and where it’s heading. Welcome to the showdown.


Dan Rabinowitz  01:31

Thank you for having me.


Victor Li  01:32

So tell me a little bit a little bit about yourself in your background, what made you decide to become a lawyer?


Dan Rabinowitz  01:37

What maybe he’s beside to become a lawyer? That’s a That’s a tough one. It’s I always was fascinated by the idea of argument and just going through the debate process, and also fascinated by history. And much of at least what I did when I was a litigator. Well, it’s not, you know, history in terms of, you know, a particular country, it’s really looking at, at the history of what the judges or or judge has, has discussed in the past what, what reasons and rationales that they’ve articulated, and how you might craft them and frame them for the current context.


Victor Li  02:18

Gotcha. Well, like any, any assets better than I saw it on TV, I thought it’d be cool to do right. So let’s talk a little bit about the future of litigation analytics. So how would you describe it for someone who might not have much understanding of what it is what it does and what it doesn’t do? Sure. So


Dan Rabinowitz  02:31

judicial analytics, at least the way that I think about it, is the ability to look at data specific to the judge, but not limited to their opinions, judges write very, very few opinions, as compared to the number of cases and motions that that actually come before them. So there are other data points that that you can use in order to assess and attempt to predict what the judge will do in the future. And then when it comes to analytics, at least the way that it’s currently used in the commercial space, setting aside the law for a moment, it’s generally a focus on the use of big data, which then typically implicates machine learning. And then finally, some sort of algorithmic or AI based functionality.


Victor Li  03:19

Gotcha. So I mean, these tools have been around for a little bit now. How widespread would you say these tools are within the legal industry? Like, is there is there a lot of adoption? Or do you think there’s still some resistance to it? So


Dan Rabinowitz  03:29

I think that we have to break out the different tools. So there’s some types of tools that are research based that that are designed to provide either more targeted arguments or getting to particular arguments quicker. So those are, for example, the ones that look at context, or look at a language that the judge has employed in the past, and try to identify those or identify patterns that that might make research easier or more pointed, or more convincing for for the judge. So that’s research analytics. And most of the major platforms have some form of that. And, you know, I imagined that that attorneys are using those to some degree. The other type of analytics is the judge analytics. And those are different in the sense that most of those, at least not ours, are statistically better or or use statistics in order to tease out judicial ruling patterns. Those are what I’ve seen, while they they have been adopted by many firms. At the same time, in terms of their adoption within the firm, it’s generally fairly limited. Sometimes that sits within legal ops or within what was the traditionally like the librarian function. And then there are a handful, if you’re talking about a large firm of attorneys that that will use those tools themselves. Think again, most of those, you know, are looking at numbers. They’re looking at percentages, for the example that you gave out there. At the beginning, you know that you’re looking at employment cases, and the percentage of time that a judge has granted that. So that ultimately is very different than what attorneys spend their days doing, which is, you know, parsing language, doing fact investigation, and generally not dealing with numbers. And we had a course in law school, accounting for lawyers, which, you know, is minimally different than accounting for Dummies, which was pretty much the intent there. And lawyers generally want to stay away from numbers. So that’s why I think while there is general adoption, when you try to get to the individual level, there’s still a large portions of the legal profession that are that are not necessarily using those judicial analytics tools on a daily basis.


Victor Li  05:45

I took accounting for lawyers when I was in law school, and I ever thought that was little too advanced for me, I probably couldn’t use the dummies class. But, but yeah, like, what have I accrual? I was like, Okay, I don’t even understand this. But But let me ask you. So do you think part of the reason why lawyers might be or lawyers might not might not be willing to embrace at least the judicial analytics tools is that they’re worried about, you know, they might rely on them to make an important decision regarding strategy or billing or budgeting or whatever, and then get bit when the judge surprises them? Like, do you think that that might be part of it, the idea of like the lawyers just not really not wanting, not wanting to take that chance would be the first one kind of like to the kind of like the first one through the first one through the glass gets the guests the cuts kind of thing? Like, do you think that that’s kind of that’s kind of what’s going on?


Dan Rabinowitz  06:26

I certainly think that, that that’s part of it. And there’s a very good reason for that hesitancy. If you’re only using past performance, if you’re only looking at the stats of what a judge has done in the past, that is really a poor indicator of what the judge is going to do in the future. So really, to place any sort of reliance on that, in terms of budgeting in terms of assessing likelihood of success. I mean, the, if you think about the the process of litigation, and where people and the parties and the lawyers and how much those factors influence the outcome, simply looking at what a judge has done, you know, previously is not really going to give you a lot of comfort when you’re going to your client and you’re saying, Well, this is going to cost 5 million or 12 million. And you’re relying on what the judge did in the past that that’s a really, really big leap. And the and as I said, the main reason for that, though, is because of the limitations of the current system. So because in the employment again, just to go back to your example, in the employment case, where you’re looking at a judge’s rulings on a motion to dismiss, let’s say, in employment cases, if most of those employment cases were involved single plaintiffs against very small companies, with regional counsel, that’s very different when you have a massive class action for a fortune 500 company with large law firms on either side. So to extrapolate from that data, is is is not going to really provide an effective prediction.


Victor Li  07:59

Gotcha. Well, we’ll talk a little bit more about your approach to the field of traditional analytics. But before we continue, let’s take a quick break for a word from our sponsor.



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Victor Li  09:20

And we’re back. So let’s talk specifically about predictive. So you talked a little bit about it before the break, but what was it about other products on the market and why you decided to take a different approach?


Dan Rabinowitz  09:29

Well, I think it comes down to the two aspects that we were going back to before that is first numbers. So numbers again, lawyers generally just like working with them. And the other platforms provide a lot of numbers. They provide, you know many statistics about various aspects of cases, whether it be motions, whether it be settlement, whether it be damages, but it’s still numbers. And then the second limitation of the other platforms is simply that they do not provide a prediction They do not claim to. And in fact, there is no way using those high level numbers that they could provide an inaccurate prediction. So limiting yourself to this, just the historic statistics doesn’t really get you very far. And when it comes to predictive technology, outside of the legal profession, we have gotten very, very far, we are able to predict with a high degree of accuracy, many aspects of daily life, many repeatable tasks can be predicted with certainty. If you just think about the way that Google treats us, and you ends up getting those ads that are eerily like creepy that they were potentially listening in, and you start getting an ad about a vacation that you’ve only begun that you’ve only thought about. The reason that they’re able to do that targeted advertising is probably not because they’re listening in, but it’s probably because there’s a combination of two elements that that they’re looking at. One is your historic buying pattern, what have you done in the past? When have you taken a vacation? Where have you gone? And then the second piece is who you are. So Google knows where we live, who our neighbors are, where we went to school, what profession we are, and the combination of both of those elements allows Google to provide highly targeted predictive ads. And I thought that the same approach could be applied to the law, simply that the judge is the equivalent of the person looking to buy something. And if we can predict what the judge will buy, why can’t we predict how the judge is going to rule?


Victor Li  11:35

So my question is, so why so like, when you focus on this kind of information, like, you know, personal information, demographic information, and political affiliation and stuff like that? Have you found that it makes a difference and how, like, when you put that stuff in, as opposed to if you leave that stuff out, have you found that it makes a difference in being able to predict which way a judge will roll or as it was just a matter of degrees?


Dan Rabinowitz  11:56

No. So you have to approach that carefully. If you’re just going to look at three or four data points, then it’s not very helpful. It’s, you know, to simply say, because the judges, you know, a Democrat or appointed by a Democrat or Republican, and consequently, you’re able to say with certainty, they’re going to rule in in favor or against a corporate or in favor of corporate interests. That does that the data doesn’t bear that out. And the reason is, is because that’s very reductive, we are not simply the product of our, our political views, you know, where he went to school. And and those limited data points. Instead, if you want to do Google, you have to know much, much more about a person, you can’t simply say, Well, I know one thing, and therefore I know everything else, you really have to understand that human beings are highly complex people or beings, and that you have to account for that complexity. And looking at multiple data points, dozens of data points, and some of them seemingly have no relationship to a decision. So like net worth, or which stocks a person owns, why would that necessarily affect you know, how a judge rules. So it’s correct standing alone, it doesn’t. But the beauty of big data is that you’re able to take large amounts of data that seemingly have no relationship to the task at hand, hat using machine learning to process it, and then AI to understand non obvious patterns, you can then arrive at a highly accurate prediction, but just looking at political affiliation, or something else that doesn’t get you where if you’re looking to actually predict something. Okay,


Victor Li  13:29

I guess one thing that not puzzles me, but I guess kind of intrigues me about this. And one of the reasons why I want to talk to you about this is so I mean, if you’re looking at all these data points, these things that maybe don’t necessarily have anything to do with the actual written law or the precedents, so in the jurisprudence or whatnot. So how do you balance that within what the law says? Or what the opinions say? Or what the case law says or whatnot? I mean, because ultimately, yeah, I mean, judges are, like I said, a product of many, many different many, there are many, many different data points, many, many different experiences, many different, you know, parts of their life and whatnot. But, you know, up to this idea today, the Socialist judges is this also about the like way, way precedent in a certain way that is supposed to like have some sort of respect for starry decisis and whatnot, or what the law says or whatnot? I mean, so how do you kind of balance those two, or more than two kind of kind of kind of factors. So


Dan Rabinowitz  14:15

we are entirely uninterested in precedent, starry decisis, the latest legal theories or what the judge did, or what they wrote, in their opinion yesterday, or even which law is applicable. Instead, we are looking at other factors that are more influential in terms of creating a prediction. And those factors are, who the parties are, and who the attorneys are. Because that’s ultimately what a case hinges on. It hinges on whether or not it’s a large party, a small party who the attorneys that are representing each one, because that’s ultimately what really influences a judge’s decision. Everyone’s gonna write, you know, they’re gonna make arguments about the law. But what changes it or what potentially affects a decision is who is in front of the judge? Does the judge fail? For single plaintiffs against large corporation or or is more skeptical of those, it doesn’t matter if the lawyer went to Harvard, does the judge do them as an elitist or views them as someone more competent than counsel on the other side? So we have found those factors are the most important ones in terms of creating a prediction. So our system or our technology works simply by entering the case number, we do not ingest briefs, we do not look at the law, we do not look at the facts, you don’t have to upload a memo, it simply requires putting in the case number. And then based on the algorithmic models that we’ve built, we then predict with an almost 85% degree of accuracy, how the judge will rule in that particular case.


Victor Li  15:45

And this might be kind of a like, a yes, Virginia, is there a Santa Claus kind of thing for me, but like, Have you have you gotten any kind of pushback or kind of, you know, any kind of criticism for that approach? Because obviously, you know, like, I mean, for those of us maybe that might be more on the, you know, oh, judges are supposed to do this judge is supposed to do that and kind of remove their personal their personal biases, and leanings and that kind of stuff from from the equation? Like, have you gotten any kind of pushback from people like that, who kind of work kind of like, well, that’s not what, that’s not what jurisprudence. So that’s not what that’s not what judges are supposed to do and stuff like that. We’ve elected


Dan Rabinowitz  16:17

to approach the judicial system by putting human beings at at the top of it, and human beings come to have highly different decisions. And there’s nothing wrong or right with that. But that’s the system that we’re in, you can put the exact same set of facts, the exact same law in front of two people, and they will come out into two and they will arrive entirely opposite opinions. That doesn’t mean that one is right or one is wrong. It’s simply the reality of humans and that we all think and act differently. That doesn’t make us bad people. That’s simply who humans are. We are highly again, we’re highly complex, beings that approach things differently, and different things influence everyone differently. Interesting.


Victor Li  17:01

Alright, so we’ll talk a little bit more about that. But first, let’s take another commercial break for a word from our sponsor.



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Victor Li  17:42

And we’re back. So let’s talk generally about the judiciary, have you felt like things have become more polarized and more political in this time? And is that one of the reasons why you decided to go in this direction with Pre/Dicta? Actually, I


Dan Rabinowitz  17:53

take a slightly different approach to the current state of things. Well, it is certainly true that some decisions might be driven by particular politics. The day to day, I don’t believe is actually driven by politics or those things. Instead, if you think about sure, like abortion, probably politics, or at the very least we can assign particular approaches to particular political affiliations. But that’s not the majority of what most lawyers handle. Most lawyers do not go before the Supreme Court about abortion. Most lawyers spend most of their time on commercial matters, on torts, on other, you know, if you will, day to day life. And that is not something that politics necessarily answers, or at least, that certainly isn’t the only factor that we can ascribe a particular decision to. So there, there’s certainly a lot of political polarization. I’m not a social historian or anything else. So I really can’t give a deep answer on, you know, what that is and why but as it relates to the judiciary, and once you remove some of the the more well known cases, if you just think about everything a judge does every day, like how many cases are coming before her, and even just looking at the civil cases and their docket? Most of those are not cases that we would think that politics really enters the picture. Now, at the same time, well, it doesn’t necessarily enter the picture that, of course, is a part of who they are. So we can’t ignore that. But I don’t believe that those decisions can be reduced to simply politics or or certainly polarization. Gotcha.


Victor Li  19:33

And this is just just out of my curiosity, have you found like a difference as far as like elected judges versus appointed judges? Like, have you noticed, like any kind of like our electorate is more likely to be influenced by certain factors versus like federal judges appointed judges? Or is that not really borne out by the data?


Dan Rabinowitz  19:48

So the difference between appointed and elected judges is only something that we’ve begun to start analyzing. We recently acquired a company and the purpose of which was as to obtain large amounts of state court data of state judicial data. When we initially launched last year, we were wholly focused on on the federal courts. And with that, with the acquisition now, we also are turning our attention to the state courts. Now, I can’t necessarily, you know, provide now whether or not there’s a distinction between the two. But just to give you an example of something that we have analyzed, so we have looked at to go back to the political question, you know, political presidential appointments, and whether or not we can discern any pro or anti business bias. And when you go through that exercise, what is actually borne out by the data, I think, is somewhat surprising to at least some people. And that is, so if we go ahead and look at not just political pointing, but also gender, and we throw that into the mix. So women that were appointed by President Obama ruled in favor of corporations at the same rate as Republicans overall. Whereas women that were appointed by President Trump are the least favorable demographic when it comes to corporations,


Victor Li  21:08

as parties. So I wouldn’t I wouldn’t have guessed that. Yeah,


Dan Rabinowitz  21:11

right. And I understand what like why we want to guess that. But at the same time, if you start thinking about like a more nuanced view of these people who were being picked, where were they coming from, what law schools, they were coming from, what what career they had prior to be appointed to the judiciary, those are very different people. And the whole, right, they’re sourced from different places, they’ve had a different career history, and therefore, it will be manifested in different ways. So each of those data points, you know, gender in this instance, and political affiliation are incredibly important. But even going one level deeper, where you can’t just say, it’s a Democrat or Republican, you also have to understand who the President was at that appointed them. Because if you look at presidential appointments, even if they’re Democrats or Republicans, who they decide to appoint, and how they go through that selection process is very different. Who was in the White House Counsel’s Office, right, who is at the Department of Justice, who was making those decisions, who was creating the pools of judges to go ahead and and look at, and each of those ultimately affect who goes on the bench. And then that will then manifest itself, through their decisions, if you decide to look at them not singularly, let’s say just about abortion, but you’re trying to look at a much, much more significant pattern, like anti business or pro business, then I think it yields very different results than what our assumptions might be, but not what our our analysis would be if we actually took the time to sit down and run through the data. That’s


Victor Li  22:45

fascinating. So how do you know that you’re accurate with your predictions? I mean, I think you mentioned 85%, earlier, how do you know that?


Dan Rabinowitz  22:52

Yeah, so what we wanted to do before we rolled this out, was to make sure that that we weren’t selling a product that that didn’t provide high degree of comfort to our clients. And the way that therefore we analyze our predictive capability was we left out of our dataset. So when we were building our models, you know, we ingested massive amounts of data from around 20 years of Federal Court history and judges. But we excluded around 50,000 or so cases and motions. So they were not included in the model there. And what we then did, because the model hadn’t seen those, so it’s essentially looking at those blind, we then ran those cases through our system, looked at the output, and then compare that to the real world outcome of those cases. And over 50,000 motions, we, as I said, we were correct nearly 85% of the time. And again, the only information that was fed to the system was the case number, and nothing beyond that. Because 85%,


Victor Li  23:51

obviously nothing’s going to be 100%. But like, was there a certain thing that like, you know, maybe caused the prediction model to not be as accurate? Like, was there a certain type of case or a certain type of law or certain type of motion? So generally,


Dan Rabinowitz  24:02

no, there is one exception to that. And that is newly appointed judges. So newly appointed judges, of course, you don’t have this stark data. However, because our approach uses both historic data as well as the personal attributes, what we do with new newly appointed judges, while we can’t match their history, we can match their biography was sitting judges, and that enables us to use those sitting judges as proxies for what that Judge will do throughout their career. And in that particular category, we lose a small percentage, so it knocks us down to around 81%. But overall, not a very significant loss because of the two pronged approach and, and the approach that to build predictive models. You have to know more than just what the judge did in the past.


Victor Li  24:48

So what’s next for you guys? Like what are you working on that you can talk about here? Sure. So


Dan Rabinowitz  24:52

the most near term is as I said, we are applying our philosophy or our approach to judicial predictions moving from federal court into state courts, we have substantial coverage across the United States and all the major jurisdictions. And we’re currently working with a dataset that that that, that we acquired as well as creating our judicial biographies and the profiles that we set up for every judge. And that’s the near term that I think that I would be most comfortable talking about now. And we certainly have other projects in the pipeline, we we focus right now on motions to dismiss, there are a whole host of other motions that we’re working on building predictive models for. But that’s a little bit further down the road. Gotcha.


Victor Li  25:37

I’m just talking about judicial analytics in general, do you think they’ll become more widespread amongst lawyers in the field in the immediate future? And I guess why or why not? So


Dan Rabinowitz  25:46

I think that, again, that we have to distinguish which type of judicial analytics, we’re, we’re, we’re talking about the predictive analytics, right. So if we’re just talking about like more sophisticated statistics, or more statistics about particular aspects of cases, then I think there will always be the barrier of entry that law is not a numbers game. But to the extent that we’re talking about actually creating predictive models, whether it be in terms of predicting what the judge would do, or creating models that would enable us to better estimate ediscovery costs or time for that, or other elements like that, where we’re actually bringing in the predictive element that is, you are outsourcing all the statistics, you’re outsourcing all the data science to someone other than a lawyer or a law librarian. We should leave that to the experts, which is, which is what we do we use our experts and our expertise in order to provide that prediction. So so long as the Visual Analytics moves, to providing answers rather than numbers, then there’s no doubt it will see greater adoption, it’s just a question of then deciding where to turn one’s attention to. But certainly, if you think about the way that analytics is used outside of the law, there isn’t a major corporation that doesn’t have data analytics function within it. And while the CEO is not going through the numbers, what they are doing is that they’re getting a report about where they see the market going, or where they’re where they need to deploy assets through that analytic function. And it has to be that lawyers are representing those clients. Lawyers can’t be ignoring the technology that all of their clients have adopted to make major decisions on a day to day basis.


Victor Li  27:35

Gotcha. And finally, if our listeners want to reach out to you and ask you about, you know, Pre/Dicta or prediction analytics in general, what’s the best way to do that?


Dan Rabinowitz  27:42

They can either go to the website and request a demo, or I’m happy if they would contact me directly, Dan at pre hyphen dicta calm. Great.


Victor Li  27:54

Thanks for joining us today, Dan. I really appreciate it. Oh, thank you for taking the time. If you enjoyed this podcast and would like to hear more, please go to your favorite app and check out some other titles from legal talk. In the meantime, I’m Victor Lee, and I’ll see you next time on the ABA journal legal rebels podcast. If you’d like more information about today’s show, please visit legal rebels.com legal talk network.com And subscribe via iTunes and RSS. Find both the ABA journal and legal talk network on Twitter, Facebook and LinkedIn or download the free apps from ABA journal and legal talk network in Google Play and iTunes. The views expressed by the participants of this program are their own and do not represent the views of nor are they endorsed by legal talk network is officers, directors, employees, agents, representatives, shareholders and subsidiaries. None of the content should be considered legal advice. As always, consult a lawyer


Exploring How AI and Data Science are Transforming Judicial Analytics


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