Welcome to the beta launch of the MIT Computational Law Report, which has been about six months and coming. Next. My name is Dazza Greenwood. I'm a researcher here in the Human Dynamics Lab. Sandy Pentland runs and a fellow in the Connection Science Group. And my name is Brian Wilson. I am the editor-in-chief for the Computational Law Report, and I am a fellow in MIT Connection Science. And my role for the Computational Law Report is executive producer. So why do we have an editor-in-chief and an executive producer? What is it? Is it a law journal? Is it a TV show? Well, the answer is yes. The idea is that we want this to be not a stodgy academic publication, but something that takes the best of articles and rigorous academic work and also brings to it some of what we excel at here, especially in the media lab, which in fact is media, rich media. So podcasts, video, sort of infographics where you can explore data. And data is so much of this. A lot of what happens in the Human Dynamics Group here is, in fact, analysis of data to gain new insights and opportunities to do things with impact socially and economically. And so, too, we want to head on, work in the legal context to make data available as a true critical asset. So one of some of the ways we're doing that, which we'll talk more about in a moment, is in addition to the articles and the rich media, a data playground where these data science experiments and computational law and access to justice applications that we hear about, people have an opportunity to upload their data set, their Python notebook or their RStudio script, and allow others to see if they can reproduce those results. We've got a great board of directors, including many of the Titans in the area of computational law and legal informatics. And so we've been fortunate to be able to, at least in the beginning, attract a lot of good data sets and a lot of good applications so that we can try to catalyze this idea of reproducibility and then also allowing people to take that code and extend it.  So MIT doesn't have a law school. And that, we believe, among other things, puts us in a good position to be more of almost a neutral forum where we can do convening with people from the law, from different law schools, from industry, from technology, in order to catalyze and promote the sort of idea flow that is necessary for the profession to evolve. Indeed, and I think to that end, the kind of program that we've set out to run through today reflects a little bit about the kind of goals that we have in reimagining and re-engineering law as computational systems and the different types of forms that DAS was talking about. So here in just a second, we'll have Sandy come up and say a few words and then we'll do kind of a run-through of what the computational law report is in a little bit more detail with the few people coming up and providing their personal insights about that. And then we'll get into advisory and editorial board introduction so that you can hear about all the great people that we've been able to convene as a kind of mechanism for affecting this sort of change from a paper-based legal operating system to one that's more data driven. And then we'll have a bit of a break and then we'll go into the author and invited flash talks where the contributors to the first release of this publication will have a chance to say, to talk about the work that they've been doing in about five minutes, get a little bit of feedback and then move on to the next one. And then we'll look ahead to the future releases of what we're trying to accomplish and talk about some of the open issues that are on our mind as we've gone through this process of setting up something that's a little bit new and a little bit crazy in terms of legal academia. So Brian just promised you some Sandy Pentland and we're going to deliver on that promise. Thanks. Yeah. So they wanted me to talk, so I'll talk a little bit. So you might say, why is computation, why computational law and certainly why at MIT? So the history is that when we set up the media lab, one of the cool things was there was no education department at MIT. The computer science department did not do user interface. Period. It was wimpy, right? Which meant that all of that was intellectually free, right? It was like you could do stuff and there was nobody, the dean or the president would not call you in and say, you know, you need to work with, you know, because there was nobody else to work with, right? So we have freedom in a way that other people don't. So that's actually pretty good. Why computational law? Well, I'll say something that we reacted to, because it's caused you to like pause, right? Which is that law is just algorithms. That's all it is, right? Now some of the algorithms have steps in it like talk to a group of 12 people and decide if they're guilty or something like that, right? So they have, they're not just computer algorithms, it's algorithms for people and decisions and things like that. And I'd be willing to defend that, right? As a reasonable discussion, you might have to stretch the notion of algorithm a little bit. And of course, what's happening is all these elements in law that had nodes that were, you know, now send a person to do this are becoming now ask the computer. And so there's a lot of things becoming automatic. And so it's interesting to explore that whole idea of law as code, code as law, that sort of notion from that lessing popularized. But I think there's more profound things, which is as you think about this as a set of rules, it's an operating system for society, that type of sort of thing. You think, well, what are other things that are operating systems for society? And we have things like traffic systems and delivery systems and financial systems. And increasingly, these are all digital, all controlled in an automatic responsive way. And you have the problem of how do you keep people in the loop so it's doing the right thing? How do you keep it from running off the rails? And there has grown up in the design of this stuff, completely independent from law, a set of design practices that are done around the world in every mode that you want. Air traffic and land traffic, which is essentially an iterative design approach where what you do is you analyze the problem beforehand, statistics, okay, that sounds sort of familiar. And then you do small beta experiments with real people to see how it works. And that's a very new thing in the creation of law. Typically, law is hard enough to create that you do it for the whole country and for eternity and that's the way it works. It isn't thought of as an experimental regime. You get to see a little of that. This is the J-PAL lab here, just got the Nobel Prize. We're basically saying, well, what happens if we change our social support system just a little bit? Let's do an A-B test. That's actually a very simple sort of idea, but the fact that they got a Nobel Prize for that, John is over there. The fact that they got a Nobel Prize for that sort of tells you that a lot of people don't think about this stuff as being susceptible to experiment. And it is. Anything with people susceptible to experiment. Sometimes there are ethics problems, right? Because, you know, if you don't do it, then people die or something like that, but experimental. And so all these systems start with small experiments, scale they're considered as being unfinished. That's another sort of big differences is that it's not seen as the truth or the system. It's seen as version 0.1 or that sort of thing. So it's seen as something that's in progress. Wabi sabi if you were Japanese. And that's another sort of just attitudinal difference. But what it means is that you recognize that the system they've put out together, the algorithms, are going to be refined constantly. And that that refinement should be based on evidence. In other words, continuous experiment, a continuous instrumentation. And we certainly don't do that. So people put, for instance, broken windows in place in New York. Oh, wonderful. But nobody actually looked at whether it worked until like 10 years later when the cracks were really pretty evident. Same thing with opioids. Right?  Nobody really noticed it until the suicide rate, for instance, among middle-aged people from things like, and opioids is just part of it. But this whole sort of disaster where life expectancy decreased in the United States for the first time ever. In the last 20 years, nobody noticed. It's funny, right? When the Soviet Union broke up, we knew that about them, but we didn't look at ourselves. So there's this notion of how can you design a system that's modular and instrumented so that you can update it continuously. Good examples of how not to do law are our health care system or our tax system where you simply can't change anything because it's all connected. So people learned in computer science that's called spaghetti code, right?  So we need to sort of bring those sort of design practices to law is a suggestion. Obviously, this is way above my pay grade, but you know, my job is to ask the inconvenient question and throw sand in the gears and stuff like that. So that's sort of my motivation is that the things that we look at, which is how does technology change society, repeatedly bump into law. Law is often the boundary condition that determines it. It's the implementation of ideas, of course, and so forth, along with computers and everything else. And so I think that we need to explore to what extent we can integrate this more sort of design orientation and continuous evaluation orientation that we see in some areas into the practice of legal creation and law. And I don't want to go on about it, but you know, that's my thing, okay? Irving. Starting again real quick. Previously soft discipline apply the word plot computational and it's the same kind of scientific method we've been applying to hard disciplines. It's the same pattern of thought. It's the pattern that you need to use measurement, which produces data to evaluate hypotheses about what's going on to be able to build better theories. And in the sense of engineering, then to be able to use those theories to design things that are then tested and renovated. Remember to make you develop models to make predictions. If the predictions empirically work, you have a good model. If the predictions don't work, you need to keep up. Yeah. No, no. All of what we're saying is a few hundred years old. What is really new and revolutionary, which you've been saying is because we now have so much data about the behavior of people, organizations, et cetera. We can look at them quite different. So we use the term computational, but it's really, at the heart of it is, apply the tried and true scientific method. Yeah. I mean, so, yeah. Did you want to say, yeah. And get your exercise, Daza. So unlike the beauty you all have in the traditional, you know, in software, computational fields, law and society have been entrenched for hundreds, thousands of plus years.  We are working at best with a brown field, not a green field. So. Okay. It'd be great if we could start from scratch to, you know, use distributed leisure, use automation, use AI to recreate society from scratch in a better model. If you look at telecommunications networks, we're stuck with 10 digit dialing and copper. If we could have started from scratch, we would not have submitted to 10 digit dialing, we would not have submitted to copper. We'd have all fiber networks, all wireless, but, but, but, but we were stuck with sort of adaptation, absolutely existing networks.  Well, how do you. So, let me, let me give two examples. Okay. So, about 25 years ago, I was put as one of the directors of Nissan's Advanced Driving Institute, and we designed, I designed actually the framework for their autonomous vehicle, which is now the largest deployed autonomous vehicle system in the world. And just like Google, we sort of began building these things and trying to amount out people, and we realized that there was no way to avoid the liability question, right? If you have an autonomous vehicle and you switch to a human, there's a several second delay during which you die, right? If you have a complicated situation, the autonomous vehicle is going to run over the cute little kid. And regardless of what the law says, the jury will find you guilty, et cetera, et cetera. What was missing, right, was not just the sort of legal system or the technology, but you have to have some generalized sense of informed consent from the population. The population has to understand that these are the rules. These cars do this, not that.   Why? Because everybody knows you don't stand on a train track. But not everybody knows that about cars. So there is, it's more than just law. It's more than just the technology. It's this whole sort of educational thing. So, if you look at that whole system, what are we going to do? Well, we live for better or for worse at a time when there's a great deal of disruption. And we suddenly have data everywhere and people are running around, oh, my God, there's data everywhere. And there's algorithms going into law. And algorithms are robot overlords. Everyone's very upset about all this. And it's this, you know, never waste a good crisis. Okay, we got a crisis. Let's do it. Okay. And, you know, so what I see is I see very senior people talking about this sort of issue and how we can redo it. And they're looking for guidance. They're looking for that sort of clean concept of what it is the world should do that they can then sell. So, I was at this amusing club weekend before last. So, this is a club. It's called the Club of Madrid. So, when you're the president of a country and you step down, you join this club. So, there's like 120 ex-presidents and premiers in this club.  And I got to visit with them and dance and sing and stuff because they're concerned about what will the new social contract look like. How will we actually do this? So, in their mind, there is a crisis. They see this in the operational people, the people who are currently in office, and they're looking for something that's going to change. Well, it's our opportunity to provide them with that concept. And actually, maybe I'll share something that, I'll put it back there. Wave your hand there. So, sort of a major voice in Vietnam and works with Michael Dukakis and helps support this club of ex-presidents and stuff. So, we're trying to put together a statement about what the new social contract will look like, right? And put it in front of them. And the UN is going to have a whole session about this next year and we can, you know, so it's a time to bring it up. And it's not going to be easy, right? It's got to go with the existing stuff. But I think there's opportunities to do things. Because, you know, you've got a crisis, you can make it. Hopefully that answers you, yeah. And that's how I think about it anyhow. Yeah, I'm sorry for commenting again. But remember, we can ask the same question about how did we become convinced that the Earth rotates around the Sun? And let's not forget that a huge part of what made the scientific revolution, the scientific revolution, is that before, if you saw the Sun coming up and whatever, you said, well, clearly the Sun rotates around the Earth. And Galileo and others started doing observations, started doing experiments, started doing predictions. They were almost burned at the stake by the church. But getting all that analysis models, et cetera, is what changed people's mind. And that happened over and over again in discipline after discipline after discipline. I think what's happened is that is now catching up with the previously solved disciplines because of our tools, our data, but it's the evolution of histories. So an example of something like this is a tax law is a good example. So all the governments in the world are going, oh my God, we don't have enough money. There's all this non-compliance. Could we do something with the implementation of tax law, like make it computational and evidence-based that resulted in a better tax system? So short-term interest for long-term change. Okay. So the World Bank's interested. We're working with EY on this, who's the world's largest law firm, I think, at least as they say that sometimes, because everybody's a lawyer in EY, right? It's all tax law. That's just one example. There's a number of things where the short-term and the long-term interests are aligned, not everywhere. Companies, like you, right? You represent a big law firm, right? People are very interested. You do. You're EY, right? People are very interested in changing the financial structure, right? And they want to do that by changing the nature of law and law creation and regulation creation and compliance checking and things like that, just to make money for net quarter. But to do that, you know, they need to standardize contracts and make them Riccardian and do all these other sort of things that make it interesting to us, right? Tax changes in America. The tax changes in America, for example. Yeah. It's always, at this point, at least in the near-term future, there is no one really speaking to help the disenfranchised in the process and all the tax cuts when you scratch the surface in order to the benefit of the most... So the good point and the answer about that would be, I think, what Irving said is that the current data resources about that are cloudy, right? So the OECD says one thing. If you do transfer payments in there, you get a different result. What's happened really, you know, we should be... That's this process of sort of science reflected on ourselves, right? And that doesn't happen very well today, right? So this sort of more data-driven policy where it is fairly clear data is a dream. It's not a reality. But part of that also means that it needs to be driven by the needs of policy and law to answer questions to set things correctly. And it's not the reflex that people say, well, we need to actually have a clear answer. Okay?  I'm sorry, I'm talking too much. Let me give you an example. Recently, a fellow I know named Raj Chetty tricked the IRS into linking data across generations, right? He did this by offering to build a system for them for zero cost. And because Lois Bitter has to be accepted, he got it, okay? But what he got was the ability to link these things, okay? And ask questions like what social policies, what social conditions result in intergenerational mobility? It turns out there's a fairly clear answer to that, that if we had known that 50 years ago, we'd have a very different society. So we have the data, but we don't have the reflex of looking at the data. And this data, incidentally, is like everybody in the United States that files a tax form. I mean, it's just incredible, right? And maybe it wouldn't resolve it. Maybe people would talk about other sorts of things, but it's incredible insights because it's data about everybody. It's not a sample. It's not an experiment. It's not a policy paper. It's every tax return of every human in the whole United States for the last 30, 40 years, right? And there's children. Okay, that sounds like good data to use, but he had to go through this thing. Several people at the IRS got fired, okay? You know? And I don't see policy people paying sufficient attention to it. It really is sort of the gold standard out there because it's complete. And it's the government's data. It's the best you're likely to do. At least, I mean, I could imagine better, but, you know. Okay. Oh, not so quick. One more reflection. Just stitching together the comments. One of the things that you've talked about over the years that in large part led to the launch of this journal in terms of your vision of computational law is if the law were more, we'll say computational, but let's say data-driven model based in a sense, science, you could have laws, tax law, and others that were written in a way that could be adaptive and data-driven so that after they're enacted, instead of, you know, kind of washing our hands of it until years and years later when it's amended again, we could actually look at how they're performing. We could measure the performance, and that could perhaps adjust parameters. So that's again the sort of iterative continuous design. Nobody in the world that builds like airplanes or transportation or delivery systems builds a system and then walks away and not look at it. It's all heavily instrumented and it's modular so that you can change the things that aren't working and you can update it because otherwise the world changes too fast. You need to do that and we need to do that with government, right? And with interpretation of law also. Yeah, but that's like hundreds of thousands of years and the selection adaptation function is a little violent and unfriendly, yeah. Yeah, unless you have something to say. Yeah, I'm nice. I know. Hello, Sandy. My name is Brendan Maher. I'm an alum from 98 and as Sandy and as I've said, this has been 30 years in the making, really. And what is happening, I think, to answer Jonathan's question is that we're actually starting at a very different place. So to give an analogy, we tend to think of computation as being very linear, right? And that is put in reological statements. What's happening is we're entering into a vastly different way in terms of the way information is computed. Our smart contracts, all of the emerging distributed and autonomous systems and the distributed and autonomous dows that are happening, the decentralized systems. We're starting at a different place. There is going to be a vast amount of complexity, very, very rapidly, because all of these smart contracts are going to come into play. And this is going to provide a much greater and different way of doing analysis. So dynamics. Let me restate that. So this is something we talked about yesterday with the costs of doing transactions, of organizing things and dropping dramatically. So previously you couldn't make, for instance, things that were really personalized and individual. You had to make general rules that are applied in a fairly blind way. And of course that's not optimal because there's always special cases and contacts. And now what you're able to do is you're able to do things in a much more fine grain way, in a more transparent way than is ever before. For instance, we came up with a way of evaluating the country of Columbia's social programs and auditing them in a way where you could compare what actually happens to what they, a gold standard that they very expensively compute. And we discovered that they give benefits to almost a million people that they shouldn't be doing and they don't give benefits to another million people that should be getting benefits. Well, that's crazy. And the reason they couldn't do that was that they had this uniform law. It was paper-based. It was completely non-integrated. So every office followed the rules or not. And there was a paper record of stuff. But they had no way of really knowing what it was doing and no way of adapting. So you can now begin having things that are much more fair. That was the goal of this. And that's actually what they're implementing with help from the Inter-American Development Bank. So that's a simple example, but it's the lives of 2 million people in a country of 40 million. So it probably matters. Okay, I'm not running this. Okay, I am letting you guys run it.  And I'm just like, this is a good thing to do, okay? That's what the North Star looks like. Thanks, Andy. So as we were just discussing, the data-driven model-based algorithmic service types that have transformed other professions and industries. So I think like banking and finance, for example, think, you know, from biplane to aerospace, they're pulling the law, lawyers and legal processes toward the same revolutionary transformation. So yesterday we were talking a little bit about what people were asking, what does it mean computational? Is this the digitization of law? Is it the digital revolution? So this slide represents something that we can distinguish. So we had paper and a lot of what happened in the, well, the 80s and the 90s and still happening. What do you think? Yes, show of hands. Is it digital? It is digital. I mean, it's certainly digital, you know, it goes into a computer. But is it computational? Not really. You know, it's, I would consider it more of a blob, a binary large object. And so what, you know, to distinguish that, what we're really talking about, and we'll break it down a little bit more, especially as more of a speak from our perspectives, is something that's composed of data that is accessible. So if the individual clauses of a contract or regulation or statute, for example, or a complex financial legal instrument, were themselves marked up in a format that was an open format like UTF-8 encoded plain text. If it was structured data like in JSON, it's a JSON object so that you could tell this is clause 172 and it relates to indemnification between these entities. That would be computational. Now we have the fuel necessary to programmatically address the terms. So we can do some simple reasoning. We can do, or even complex reasoning, we can do some analysis. We could do much better, more fine-tuned automation. We could compute the law, which brings us to one of these big questions along the lines of what I think, in a sense, Jonathan and others were where everyone has been talking about, which is, well, how do we re-engineer law? In the early days when people were talking about flying, many people thought, well, what we need to do is model like a bird. Birds fly, so we should come up with a machine that has wings that flap. I don't know if any of you have seen those hilarious and tragically ending usually videos of the contraptions with things that have wings and jump off the hill or the cliff. Well, that's not always what we need. When it's time to cause something to be computational, there's some artifacts and assumptions that may be worked in paper and for paper paradigms that need to be, the word I would use from computer science is refactored as part of the re-engineering. So it turned out if we wanted aerodynamics, fixed wing aircraft, but initially by planes, we're the way to go for aerodynamics. So too with the law, we're part of the process of taking each part of law and each kind of variant of legal process and different types of legal rules are going to require some rethinking. Some of them can be described programmatically and very declaratively in something like Haskell. Other stuff may require something more, a little more fuzzy and AI based. So what does the shift toward computational law look like? Well, as I just said, from paper documents to paper documents and word documents to something more like Markdown and JSON. So imagine going from PDF just at the simplest level to Excel. But is Excel where we want to be? Well, not really. We favor formats that are more like JSON or at worst XML so that we have structured data that can be, can show up as part of an input to software. From natural language to formal programming language. So there's a good example is who here has heard of something called Lexon. A handful of people. It's this very interesting innovation coming out of Berlin from a friend of ours named Henning. And he has this sort of dual editor on the left side. He has a simple natural language just like normal human language clauses, like for a contract or other legal instruments. He has a nice demo now for a UCC nine security filing when you're, when you have collateral for a loan. And those simple phrases on the right side will show you kind of in real time what the software code would be.  And we're working with them now to also basically transform and compile that down to to JavaScript. So it'll be more the lingua franca of the web. And so to one of the things that Sandy had said earlier about law being like a legal algorithm or just an algorithm, I think, you know, when you're in law school, you learn this very formulaic approach to breaking down a problem. You state the issue, you state the rule that applies to that issue. You apply that rule to the issue, and then you conclude with like a pretty dogmatic statement of why you're right. Now, what we're seeing with computational laws, you can actually structure things as data, you can input them into a certain format, you can apply a layer of computational law, and you can generate some sort of output. This is happening already with Turbo tax so Turbo tax has a lot of data about tax violence, for example, user inputs that information into some sort of standardized form. This layer of computation is applied, and an expert system helps calculate these tax liabilities, and the output of that is your state and federal tax returns are generated. So the the apps that lets you sue corporations fairly easily has a lot of data about how to do that. You can input your information about the different services or different products that you've purchased into their system, they automatically compute the amount of money that you can sue for, and then those claims are automatically generated. The LA Piper has a global data protection laws map that does something similar and allows you to compare the privacy laws between different different international jurisdictions, and relativity trace has a tool that monitors emails to effectively evaluate whether or not there's a risk of insider trading about to occur. So with that, I think we're going to hand it over to the panel. Yes. Great thanks Bob, can you introduce yourself and and sort of start us off with computational law and this new endeavor from your perspective, and then we'll go right down the line. Okay. So, how's this. My name is Bob Craig, I am the currently the CIO with law firm Baker hostile or we have approximately 1000 lawyers spread across the US. I like to always say I'm not a lawyer, but I have observed them interacting with technology now for more than 30 years so I've been toiling in that brown field now for 30 years Jonathan. But I do think we're on the on the precipice of a new opportunity and I think one of the observations I'll make is the fact that market forces are in play that I think arguably have the global legal ecosystem on on steady footing. And I think that's a good thing. And that's happened for a whole bunch of reasons. I won't lament them all but a lot of them have to do with the increasing sophistication of the buy side of the whole equation. Our corporate clients have gotten increasingly sophisticated with the application of their own technologies and their own understanding of what, what value we deliver with our legal services. And that alone is a force that has gotten acute attention amongst every global law firm, trying to figure out how our clients think about our value proposition and the degree to which sophisticated technologies need to be employed to validate that value proposition. So, the whole industry is in a state of, I don't know somewhere between disruption and awakening that there's a new way to think about how we have to practice law in order to continue to to get interesting work and to do all the right things for our important clients. I've also observed, especially recently now with so much access to so much information, including through various forms of social media, which obviously has its downsides but the upside is you can start to see who in our industry has passion and conviction around ideas. Some of them are in this room, two of them are right here for sure. And, and that's coming from the tech providers in our industry to provide solutions. I think most refreshingly it's starting to come from some really innovative law schools who are trying to reorient the thinking of young lawyers that the human barriers, which, of course, make up that brownfield that Jonathan referred to. I really like that obviously. That is the ultimate challenge. And so the question in my mind is, is there a new way to kind of catalyze that energy and passion from various sectors of the legal industry. What some more science where you know where I'm going with this Sandy T this up very very well I think the, the participation of MIT in this whole conversation is so fascinating to me almost therapeutic to me. In terms of giving me hope for the future. And I do think as, as different different ideas start to emerge and have impact and those ideas are validated by these increasingly sophisticated corporate buyers that will start to create an energy and a momentum that the best law firms in the world. The best lawyers in the world will be drawn to that to not want to miss that wave to be able to play a part in this new way of what I like to refer to as legal engineering and being part of the very design of new systems. Even if they're social systems, certainly any system that Sandy put collides with law. The lawyers I have observed once they see that becoming a real and present opportunity them applying their intellect and being willing to innovate will be something they, they get on board with. I could go on but I think that's where I'll leave it. Thanks, Bob, and Brian, I want to thank Brian and Thompson Reuters labs for, or we should all thank them for the coffee and and for being a supporting organization for this event. Thanks very much. You're very welcome. Well thanks for having me it's great to be here. My name is Brian you listening and VP of Thompson Reuters labs Americas. And we're very excited to be supporting this new report. And we've been working with Daza for quite a few years and Brian and a lot of the people involved here. And it's really exciting to get this, not only visible within, you know, the confines of MIT, and so on, but to the wider world. So we're very excited about that. And the labs where I come from we're super excited about the potential that we now see in being able to leverage the huge amounts of legal data, you know, the corp, the whole corpus of legal judgments and so on that that we have it at TR, the legal citation and lots of interesting stuff going on with reasoning about overrulings and, you know, split court overrulings and so on. On top of that citation graph and so on. And the, the potentials for deep learning it within the legal space are kind of incredible so one of the things that we've been doing within our lab for example, is taking a service called court wire that maybe some of you are familiar with that we take civil complaints as they're filed before they're docketed. And typically we either retrieve them electronically or send runners to the court someone scans these things sends them to contractors they summarize what the allegations of the case are and what the damages that are being sought. So it's sent out as quickly as possible for legal business development and financial people are interested in who's being sued. And so, you know, we summarize those as quickly as possible that's always been done by humans. What we have shown recently is that some of these deep learning summarization techniques can summarize these cases, as well as human so when we do double blind evaluations trained legal editors can distinguish the human generated summaries from the machine summaries. The summaries can include terms that they've never seen before. So terms that were never in the training vocabulary can appear in the summary. They're more grammatical than the human generated summaries, and they are judged to be excellent only than the human generated summaries. So this is pretty incredible that this, this task that we previously thought only not only humans but specially trained humans could do within the context of the law can now be done equally well by machines, which you know, so being able to say what a legal case is about and why it matters in language that is not just language that's extracted from the legal case, but is sort of abstractively generated on the basis of language models. That means that we can, the summaries could be done in ordinary English, not just legalese, they could be done in some sort of computational language they could be done in a foreign language, as long as there's the training data to do this. So I think we're on the verge of seeing a lot of really interesting opportunities for machines to expand, you know, access to legal services and so on, in ways that we've never seen before and so we're really excited to be part of this journey that's going to be documented and explored within the context of this report. And super excited to be here at the beginning. Thanks. You know, it's almost to do summaries of dense complex legal corp corpora along the lines of pride to you. It's almost as though you'd need a PhD and computational linguistics from MIT to deal with that. That's exactly what Brian has and and so what we are really seeking to do with our partners and with our research affiliates here at MIT is bring the best of computational thinking like a laser onto the law so we can, we can't get there. And in that, thank you Brian and we have a lot of Brian's here. So to distinguish us this is cool Brian in case you want to get his attention. Yes, I want to take one one other moment to announce that we are sponsoring a internship in computational law this summer it's a memorial internship named in honor of Andreas Antonio the CIO of Paul Weiss long term CIO of Paul Weiss who was tragically killed this past summer in an accident. So we're we're offering an internship this coming summer for undergrads or law students who are interested in exploring the frontiers of computational law with us so hopefully someone here knows someone who might be interested so please get in touch. We've had a great relationship over years. Thompson Reuters labs with our MIT undergraduates going there and that's been one of the great ways we've connected to in the spirit of excellence in different fields. We're also really thrilled that Kat Moon is on our team. And one of the big questions, I think for all of us, and that Sandy's been posing for years as well as you know you've got these deep computational systems are very numbers based. What would be the interface what would be the human experience that a legislator could interact with in order to create rules that are computation that lawyers can interact with the business people would interact with. What is the design of that. And so that's really why we wanted to recruit Kat Moon to be to be on our team and why we're so excited that you're with us today so Kat Moon. Thank you. So I'm Kat Moon. I'm teach at Vanderbilt University Law School. I'm the director of innovation design. And I also teach a few technology related courses my passion and really where the intersection of my thought and effort is the intersection of technology and how we humans consume technology and use it. I'm going to pull a quote out from Sandy and I'm going to use it out of context but I think the statement. I haven't tweeted it yet but that might happen. You know Sandy made the statement more than law more than technology it's about education and so I think that this report is a phenomenal opportunity to create access and help create understanding. And really build a community around really this very sophisticated work that's happening in a lot of pockets globally. I have conversations with people around the world and it really the connections that are being built I think are phenomenal, but we need to supercharge those connections and really create a nexus point so people have a place where they can come around these ideas and have really interesting conversation and share and build community but equally so and this is why I'm really interested in in the format reflecting the sophistication and nuance of the content is that the format is really going to be agile and it's going to take different forms and so the content is not going to be delivered solely in the form of frankly a very dry academic paper. I know some people enjoy weeding through those, but it really limits access to very important thought ideas and information. When that is the only way these things are shared and so to create content in much more accessible and to many people, much more interesting and understandable ways. I think is really going to expand the ability of the people involved to share these important ideas and build on them and create a community around them. I think it's critical that we address how we are going to take all of this very complex information and these very important yet complex and sophisticated ideas and translate them in a way so that we are actually educating people who need to understand these things, people who need to be able to use this information, people who create policy, people who create our laws. This is not merely an academic conversation. It has very important real world consequences. And so that design that interface about how we translate it and share and create understanding. I think it's critical. And so that's why I jumped on board because I'm excited. I think it's going to do some new things in some new ways and I'm very excited to be involved. Thank you. My name is Diana. I'm from coming from Lisbon. I'm initially a loss. I did law law and management. Actually, I'm doing engineering public policy so huge shift and work as a fellow as well with engineers most of the times but it's quite interesting to help and have both in this experiment. Hi, everyone. I'm as an editor. Hi, everyone. I'm Robert Mahari. I'm at the Harvard Law School but I'm an MIT alum. I studied chemical engineering here. And I'm kind of pushing from the student side and from the education side. I run the Harvard Law and Technology Society there. We're very proud to have put together probably the largest kind of student run legal tech event in September. And we're we're kind of pushing for for faculty members for our fellow students to realize the changes that are happening and how they can get involved and kind of design and hack the law. So so that's my function. Hi, I'm Elizabeth Rainieres. I'm the founder of a consultancy called the hacky lawyer, which is focused on a service called legal engineering, which you heard a little bit about earlier this morning. I'm also a fellow at the Berkman Klein Center for Internet and Society at Harvard, down the river. I'm focused on basically designing and building better legal frameworks around our data. I've been in house a number of emerging tech startups. And so I have a very technical perspective as a lawyer. I'm really looking to bridge the gap between lawyers and technologists. I am serving on the advisory board, both potentially to be determined. I've been working with a dozen Brian and others here for a while. So I'm looking forward to it. I've been asking, I'm a professor at Brooklyn Law School, and I guess I stumbled into legal tech through circuitous paths and being driven out of other fields. I started as a civil rights civil liberties lawyer, and realized unless I started to understand technology, I was never going to be an effective attorney in the digital age. So I moved into tech law and then I think people conflated the term tech law with legal tech. That by default became a legal tech academic as opposed to just a straight tech law academic. And here I am now. And I am also, and no, I'm in the editorial board, right? Am I supposed to say more? I am an advisor. I'm Bob Craig, and I guess I'm an advisor because I've been in that brown field for 30 years. Still Brian, you listening? I'm an advisor. I'm Matt Moon again. I'm an advisor as well. And a little more context. I practiced for 20 years before I started teaching and represented a number of technology companies. And so I've come to the technology piece, both from my practice. And I'm now evangelist for helping lawyers cross the chasm as well. So I've only been doing it for 20 years, but it feels like a millennium. 20 years in legal tech internet time is, you know, a millennium. So, sound check. Do we have David Horrigan, Shawna Hoffman or anybody else on the line? Can you come off mute and say hello? Okay, I hear Shawna. Hi, Shawna. Hey, welcome. Welcome back. Thank you very much. I've been kind of all over the world over the past month between Taiwan, Spain, and Greece, and it's amazing legal tech in general. And, you know, the ideas of the Ministry of Justice and where they want to move with AI and blockchain is just absolutely fascinating. So I'm excited to join the board.  I know advisory board. I don't know if you have any other thoughts for me. I know we've gone in a couple different directions, but we do want to talk with you eventually. So let me kind of share who I am for those who don't know. I think I know actually pretty much everyone, but I am the head of the cognitive legal practice at IBM. And we work with AI and blockchain and kind of that convergence with, you know, law firms, government entities, various different lawyers worldwide. So it's been pretty fascinating over the past seven years since I moved to IBM from practicing and eat discovery for over a decade. And the change has just been so much fun and so exciting. But I'm excited to take all the things that we've learned worldwide over the past few years, and really bring those into play and join this community so that we can actually make some huge strides forward. Because one of the things that I'm seeing is everyone has the same problems. And so if we can get those problems and solved or at least, you know, put out on the forefront saying here's the top 10 issues and here's ways to solve them. Then I think we are going to make leaps ahead for the attorneys worldwide and really in the end for our citizens and does that I are working on and well and hopefully you all will join us on something with the future society and wanting to move forward with working in modern day slavery. And we're talking about various different offerings and opportunities to really affect that area and make huge insights into bringing to light some of the issues that right now are dark and uncovered. And just to be clear, we're working against human day slavery. And it's no laughing matter.  And you hear it feels like almost weekly of people in forced labor, and well, in so many difficult situations and data can make a difference to uncover and to rescue these these victims and survivors, and to structure things so it's so much harder for people to get into that in the first place. And Brian you listening who's been working on this for some years, and Sean who's also been a real leader have kindly agreed to co chair a kind of a task force is what we're calling it initially to spin up to spin up an initiative in this area. And Robert has kindly introduced us to people in the Department of Justice civil rights division, the chief innovation officer and their chief operations officer where we're an initial but you know fairly well along discussions about a commercial collaboration with them, part of their beat is also on human trafficking. And they're very interested in working on what ways to use data to make sure they can marshal their scarce resources better to make more impact so anyone who's interested in that initiative. You know, let us know. And we have a, we'll have a board meeting after this meeting this afternoon where we'll flesh that out a little more, but you can find out more at law.mit.edu. So, um, thank you, Sean. We're so delighted that you're with us. Next up, I think we've got Sarah Glassmeyer. Are you online. Hi, my name is Sarah Glassmeyer. I am currently with the ABA Center for innovation, although I don't speak for the ABA we're very sensitive about that, especially in recent days. I am a former academic law librarian and technologist and my interests really lie in. I'm on Twitter all the time and I just tweeted this because you're watching the earlier presentations. You know, it seems like, you know, so much of my career has been trying to introduce technology into the delivery of legal services and how to make it more efficient. But I also now starting to feel like we really need to remember the humanity involved and that justice, the delivery of justice isn't a win-loss proposition for some people. Everyone can win. People just want to feel heard. They want to feel, you know, that's one of the things I learned working a reference desk in Kentucky. You know, people don't need to have like actually to win to feel like they have been made whole. So that's kind of my thing that I will bring to the table is humans involved in computational law. And I'll never forget that that law is a human process, although so much of it can be automated so much of it can be made into algorithms and made more efficient. We're here. Thanks, Sarah. And Sarah has been such a shining light in this area for so many years. We're honored to have you on our team. Thanks, Sarah. Oh, happy to be here. Thank you. Yeah. Is Mila on the line from San Paolo, Brazil? Okay. How about David Horgan? Okay, we, we recently had David Horgan on the line from Relativity, and they've also been a supporter from the start. You can see our entire Board of Advisors and Editors at law.mit.edu and on this fancy poster here. We have a little bit of insight about how this is going to play out. We're going to have authors come up and talk about their submission to the first release of the MIT computational law report for about five minutes. And then we're going to have a chance to get a little bit of feedback from the audience and use that feedback as a way to kind of improve the overall quality that we're bringing to this publication and make it more connected between, you know, academia, people in the community, and everybody else who's here, so that it's as robust as it can be. You're here. And do we have perfect. And so we're leaning on Sandy really heavily today. But Sandy Pentland is our first author, actually, doing the anchor article, really laying out the thesis of computational law, Sandy. So I didn't know I was going to do this, but fortunately, I already talked about the article, which is this notion of bringing design thinking to legislation and to the practice of law. And I'm told I'm supposed to talk about personal data. Yes. So, so, so I'll give another example so about 15 years ago. We were building some of the first cell phones before there were cell phones and stuff and we realized that data off of those phones was going to be a huge issue, both for good and for bad because you could see you could track health outcomes and do things that were clearly social good, but it also was something that was like 1984 squared, right. And so I started the discussion at Davos with an article about people should have ownership rights over their data, and you know, I was reading who was member of the was a Justice Commissioner for the you went on to actually create GDPR and stuff. GDPR gives people the rights for people to know what's being done with their data, but also have a copy of their data. Okay, this is getting to be a sort of common thing you see this in HIPAA in this country and things like that. However, having a copy of your data does virtually nothing for you, because you have no political power and a sample of one is not big data, you can't really tell. So recently we've been developing the software and architecture for setting up cooperatives, where the people can as a collective own their data and have power with it. And it turns out that credit unions charted in what the neighboring legislation was 1904 something like that. Allow are empowered to manage personal identity, which of course includes digital identity, and they are member owned, not for profit organizations, or community organizations. They have a bunch of interesting structures like for instance, they have an alliance among them for investment and political things they're democratic rather than run by the senior people. And so what we're trying to do and we're just talking to Jonathan here we want to get an example of a credit union, where all of the members take control of for instance their medical data. And just sort of think for a moment what that would mean. What that would mean is is that I could look at drug interactions. So in this country. Drug companies don't look at that because of liability doctors don't look at it because of HIPAA and because of sort of odd economic incentives. Government doesn't have access to the data. Not really. So this would be something where you could revolutionize health care by just having say 100,000 or 50,000 people in the in one city, all with copies of their data, where they could ask things like these two drugs interact. So is this treatment actually works. We don't know these things we pay 20% of our economy on health care. We have no bloody idea how well it works. Okay, so what you need to do is put it in the power of people. So that they can get a good statistical sample of what's happening and it's in their interest to do that. And then we can push back politically. Okay, so there we are handling data. Yay. I mean, Ed actually had a question stop before and I told me to ask now. Okay, yeah, so that actually you basically address the question which is, you know, I know yesterday that was a theory of kind of data cooperative that you know framework for how that could be shared that you presented and I thought was really interesting. I think in law that model is can is a little bit fraught with other barriers, right, because I think when you talk about health care data that's being shared, people have the right to essentially submit that into a cooperative voluntarily. But when it comes, and then it's sort of like metadata that's that's factory. So the credit unions, you're not giving it to the credit union, its own union is holding it for you. So you have to agree to have it analyzed. But if I say to you, would you like to know how your kids are going to react to a treatment, would you like to know how you know the drugs that you're being given work. And the reality is is almost everyone will say yes to that if they're not giving up rights to their data and not exposing their personal data. So, the case here. I think where that finds metaphor and law would be what are the terms that you're negotiating in these contracts at large like what it let's say residential leases or something like you know what are the terms that are being, you know, that you're entering into as you are in your residential lease what are the terms that you're entering into in terms of your insurance policies or anything else I mean there there are a number of other sort of when you talk about how that could be applied in in law right and I think one of the things that was really kind of eye opening for me yesterday I forget who said it, one of the speakers was essentially saying the growth of kind of AI discovery is a lot less about AI algorithms and it's a lot more about pointing at a new data. And it's almost directly kind of related to access to more data and legal data we're just talking about this during the break is siloed so much right and who owns it and who has access to it, whether it's contract whether it's anything else. You mentioned earlier that like there are examples of like the government having access to huge pools of data because they have visibility into for instance tax, right because they're kind of stakeholders in that. But in an individual level of contracts that or you know other legal obligations that they might be able to share into kind of a data collective into shared ownership I should say, in a data collective. But I think I'm curious to understand whether there are barriers to that model or that framework that are inherent to things like, you know, other other other forces of confidentiality that might apply to people's legal. So, just, I mean I don't want to talk too much about it but but one of the main foci of our research is, how do you get insights across siloed data, without sharing the data, because in many cases there's just Cisco but just internal boundaries and privacy, there's proprietary things. And it turns out that there are a family of algorithms that allow you to answer collective questions without sharing the data from a legal perspective, and without revealing anything that is proprietary or personal. The classic one is something called security party computation we've developed a couple more. There seem to be once people are focused on it it turns out that there's a bunch of things you can do where you can answer questions like, you know, is this tax equitable and this you were talking about the tax reform. You could answer questions like that with great certainty. We're exposing people's individual expenditure records. Right. If, if there was a place that was a uniform place where there was access to everybody's spend or tax records or something like that. And without exposing the individual records provably not exposing them, and without exposing proprietary things of the data holders and say like you could go to into it. But you can do it in a way that doesn't reveal that. So, so that really changes the way you think about data, because if I can answer questions about it without actually holding the data or exposing the data. It's not like science fiction, but it works. And it's even provable and blah, blah, blah, right. And another question that you had was last year I gave a talk at your stat which is the organization that handles all the European data and data standards. I heard the commissioner, the EU commissioner talk about secure multi party computation. Yes, this is, this is like the most senior sort of politician in Europe, talking about this very exotic but powerful computer science technique. That's like, you know, joy. People are getting to the point where they understand that some of the some of them, the mechanism of computational mechanisms do things interact with law and ways that are extremely unexpected and you might not believe were possible. And so we ought to explore that. Here. Thanks so much Sandy. Great, we'll go ahead. Thanks. This is a co article by Elizabeth and as a. And it's about a pretty fun concept. Sure, so I'll start with some. I'll start with some scene setting. So I am trained as a data protection and privacy lawyer I have a very cross border experience of practice in the US and UK in the Middle East. In the last four years or so I've been very focused on blockchain and distributed ledger technology so I have been doing a lot of work at the intersection of these two things. And so this sort of content was born out of my experience at the intersection of data protection and blockchain. So it does and I recognize that blockchain and DLT are typically borderless technologies. Obviously we're talking about sort of open networks here. But the idea that they don't they don't really respect sort of conventional geographic borders in sort of public block chains this. The idea is that pretty much anyone can stand up a node irrespective of where they're physically located, which means that it is a real challenge for laws which are still very much bound by jurisdiction and geographic scope. So we were thinking about some of the hard problems in reconciling existing data protection privacy laws with these types of block chains and distributed ledgers. And there's been a lot of emphasis in my view I guess in both our views on the notion of erasure and the sort of difficulty in implementing erasure or the right to be forgotten in the context of blockchain. But there has been very little emphasis on some of the other problems which in our view are actually a lot more complicated. One of them being how you comply with sort of restrictions on cross border transfers of data. And so our proposal is to design something we call binding network rules, which those of you who are sort of trained in data protection privacy might be familiar with the concept of binding corporate rules, which is a mechanism for intra company transfers of data across borders. So some of the really prominent examples of binding corporate rules are ones from sort of AmEx or MasterCard or sort of large global tech companies as well into it being one of them. So there are sort of, you know, examples in the wild, and we wanted to see how we could sort of transpose this into the blockchain context. So some more sort of meta context there is there are sort of three mechanisms for transferring data across borders under regulation like the GDPR for example so one is on the basis of geography or jurisdiction so that's something like an adequacy determination so for example the EU has determined that a country, for example, Argentina or Japan has sufficient sort of protections in place to allow transfers between those countries. A second one is where hours falls which is this idea of binding corporate rules which is within the context of a single organization or entity. And a third is something called standard contractual clauses or model clauses which are negotiated, obviously contractually the name gives it away. And we were thinking you know what is the best sort of model for something like blockchain and so where we've landed is that the network can effectively be construed as sort of an entity or an organization. And that transfers of data within that network or organization are effectively intra company like transfers obviously their issues around whether sort of a blockchain network is like a company and there's some really good research we can bring in there from all of our good enough and others around sort of distributed organizations which we will do. But it's just sort of a mental model for how we apply this type of framework. And then we look at how you know how the sort of rules would be designed and how they would be finding and how they would sort of implement core principles. How liability is allocated and all the rest so that's the sort of background and does it do you want to get into some of the applications. And so I found this epiphany of Elizabeth's that binding corporate rules could be extended to something like a distributed network, like a blockchain and and composed legally as binding network rules to be very, very provocative and very interesting and potentially to be us, you know, it will take some work to to work out the implementation, but potentially very valuable. And so the conversation that we had that led to the article was putting together kind of like peanut butter and so if you say that this concept of binding corporate rules applied to distributed networks as binding network rules as the chocolate, the peanut butter might be an area I used to practice in a lot which was creating over arching rules that's for commercial networks, so supply chains, it would be called the trading partner agreement, and the umbrella rules, or in payment networks you mentioned, MX, I'm more familiar with visa, and then a couple of payment networks with public and private sector partnerships, like EBT, it would be called the operating rules would be the umbrella agreement and then participation agreements for the, you know, like the acquiring bank and the issuing bank and the card holders, that sort of thing. Identity federations it's the same thing you have single sign on across a bunch of enterprises. There's some sort of trust framework or some overarching agreement and participation contracts that make those rules enforceable. And so just looking at these design patterns, it seemed like one thing that we could talk about modeling, and maybe testing a little bit would be structuring these binding network rules against the general this general tried and true design pattern. And there's two, there's two aspects that we thought would be particularly helpful one of them is that it architecturally breaks things into three layers and that's good to do things that are modular and the way that Sandy was talking about. One layers that top layer I mentioned the sort of community rules one big set of rules that apply to the entire network. So with the visa operating rules, I think that's like a like a five volume set that deals with a lot of stuff. The second middle layer are the contracts, those are just a few pages anyone that's ever signed like a card holder agreement, or a merchant agreement so you could process credit cards know it's a few pages and that's the contract that that makes the overarching rules enforceable. And then there's this very interesting third layer ends the lowest layer of granularity and that I call the transactional layer. So in a supply chain, it would be like EDI ed effect like here's the transaction code for a quote on for how much I'll sell you a thousand minutes. Here's the invoice code. Here's the acknowledgement code. Here's the receipt. Here's, you know, we, we received it a chipping and receiving its trend, lots of transactions with credit cards, you know, it's the payment transactions you're at a point of sale, or something like that you push the card through it's a little bit of data, and it's high volume, high velocity. Well, just think architecturally breaking into these three layers and make sure that the, that the network and the data flow were support reflect that we think is very useful it's very scalable. The other one is another design concept which is breaking the three key dimensions that they're clear and separate and yet aligned. And that's business legal and technical. So, like, you can remember that as BLT, like a BLT sandwich, you know, as three layers, but greater than the sum of its parts when you eat it, because it's so delicious together so you can actually see some examples of this, of this design pattern that in my law practice and consulting work, some clients have kindly allowed this to be published under Creative Commons so we'll bring that in, in the article as well but basically you've got the way I do it anyway, I've got a business section.  So, that's about 80% of the argument over that is liability but you know IP order of precedence like where where does these rules fit compared to other rules and contracts you may have legal stuff and then the third part, most most delightful perhaps is the technology and where the standards where the, how do you onboard to the system what kind of monitoring we're doing where the security requirements that we have. And, you know, by having kind of a single write down of the business terms, the legal terms and the technology terms is an opportunity to use like a unified glossary to let people see across those terms to make sure that there's alignment and harmonization and ultimately integration of the legal part with the business really driven by the business part and the technology so that the, the, the legal dimension and the technology dimension are at all times supporting and reflecting the business deal. A lot of times when you lawyer these things, you can come up with you know beautiful work of art, we think 200 page contract and it's just, you know, the engineers and the technologists don't read it. You know, sometimes a lot of the business people don't read and even if they did, you know, a week in a month a quarter, a few years go by and you get this continental drift between the legal stuff. What's really happening by integrating these things using computational law techniques, we can make sure that they remain aligned, and as you change some things you get the appropriate changes in all those sections and you trigger the conversations you have to trigger. So some of the things that happened well that were, that could be a good fit for the concept of binding network rules would be in this insurance space, which is one of the creative comments examples that will provide where they set up a identity federation to make sure that brokers and agents could sign on once matter which insurance provider they were using or which product they were using with its home insurance auto insurance life insurance or so forth. And when I helped them write those rules and architect that system, we had business people from all the different providers you know the Hartford progressive and others figure out what the big the business term should be we had legal legal people from from across the organizations including the vendors and the agents and the brokers come up with the business the legal terms and again most of that conversation was around the hot potato of liability and the technology people from all the organization setting up like you know what we're talking about the sample metadata, how do we connect the end points, how do we exchange stuff how do we migrate from one system to another. So by basically making sure people are on the same page across the organizations participating in the network, and that we have this real continuous conversation between the business people, the legal people and the technology people so that the system supports and reflects the understanding is a second design pattern that will bring to bear. So that's the basic concept of finding network rules. We've got, you know, this is a beta launch and so we need to still think through some of the permutations of that we wanted to take the opportunity to tell you about our ideas to hopefully spark some questions and some up and some, maybe some further ideas from from you all and you may find yourself credited and attributed with your content being in our paper. So there you have it finding network rules. So with that, is there got time for one quick question. Maybe anybody. No. All right, Brendan. Well, thanks. I got a small comment. And it's related to that design. And it was a discussion about this idea of policy and how this idea of policy relates to both the higher level goals of an organization and the lower level implementation and the feedback loops you get by looking at a policy, and then from that how that affects the implementation, and then how that implementation feeds back up, and then that affects the goals of the organization and then that feeds back down, and then changes the policy again. So this idea of policy is also a very interesting concept because it spans the entire stack. And I want to add that because I think that fits in here somewhere. I mean, if you look at sort of binding corporate rules, they're not the idea is that it's sort of this self governing mechanism within a single organization or entity and so effectively as a collection of policies. And the legal bindingness comes from sort of the organizations binding itself to its sort of set of policies and therefore transforming them into rules. And yes, they're sort of a there's a supervisory mechanism where the relevant sort of, you know, regulatory authority has to actually supervise that the organization is adhering to its own rules. But yeah, I guess that's really, it's an interesting sort of how does the policy evolve and this is some of what does I was talking about is that what we want to do is not just say, you know, this is sort of analogous to the BCR context but we want to say this is how you'd implement it from a technical perspective so for example you enforce these rules via smart contract potentially in a DLT or in a blockchain framework so make them actually sort of living rules and policies that are sort of self executing if you will. What a world that would be great so thank you very much Elizabeth. Okay, so now we yeah. Okay, so next up we have a great collaborator. Sam Harden, Sam can you hear us. I can hear you can hear me. Yes, you're coming through well. Excellent. So who here has heard of doc assemble. Oh, excellent. Thank you. Great so, you know, a real question that we have here this being in primarily an engineering school or trying to take an engineering approach to this transformation of law is how exactly do we do it, like what tools do we use. How do you build this how do you, how do you learn how to build it. So in that spirit, we wanted to bring Sam Harden forward to talk a little bit about his approach and actually Brian can you say how we, how we talent scouted him and basically what people can expect. Sure. So, it was last summer, and I was in Berlin at the time and I saw Sam tweet out a link to this great tutorial set of videos that he created. And what was interesting about this set of videos is that doc assemble is this really great and robust tool for creating expert systems that can interact with different API is and function very computationally. And it's all about the law and it's all open source. And he created one of the biggest challenges that I've had in my experiences with doc symbols getting it set up and configured to different environments. And what Sam did in the very first video was show how you can set up and install the system using Amazon web services so that it's functional and interactive straight out of the box. And now you can quickly and easily start deploying these technologies in ways that are that are very much enabling computational law. And so with that I'll let Sam get in a little bit to, you know, what his vision for this is and how it's going to kind of change the future of what we're doing. And maybe to just outline what the three tutorials are that we're going to be publishing in the first release if you will Sam. So, Sam, you're on. Hi everybody, my name is Sam Harden, I'm watching you video. I am down in Florida wasn't able to make it up to see everybody in person so thank you for having me. I'm an attorney I'm a computer programmer. I'm also a legal technology consultant that works with measures for justice, as well as a legal research company called V lax, and other consulting work on the side. And as as Daza mentions, I have been working on instructional videos around a technology called DACA symbol and I wasn't able to tell who in the audience is familiar with DACA symbol. We had about almost half of the people raise their hand. That's, that's fantastic. You know, I am a DACA symbol convert, primarily because, you know, until DACA symbol there wasn't an open source tool that was easy to use at least to my knowledge to let you create these expert systems to let you create interviews and to let you really code the law in and hard bake it into something programmatic that you could then turn around and share with others and have clients or have applicants use the program to do certain things to generate documents to run the logic that you wanted as a programmer and as a lawyer. So, I've been creating these tutorial videos to give people who don't have a ton of programming expertise you don't have a ton of. Can you still hear me. Okay, if everybody can still hear me. I'm sorry about that I don't know what's going on. But as I was saying I wanted to put power in people's hands to be able to run this kind of expert system and toy with it and modify it and use it in any way they wanted to, so that they could do these really cool things and have that power to do these things. You know in my mind, all of the legal technology tools that I've seen come along are created as scalpels they do one specific thing they're, you know, discovery applications or their AI, you know research application or thing like that they're all wall gardens DACA symbols open source it's a Swiss Army knife in a world of scalpels, and I think that's really kind of incredible as to how it empowers lawyers legal professionals and others to create things using something that's open source. So as Daza mentioned the first three videos are about setting up DACA symbol, creating the start of an interview using DACA symbol and how to bake in some conditional logic using DACA symbol. And if you haven't had a chance to play with doctors. I really encourage you to look it up. There's a ton of examples online. The creator of john of DACA symbol Jonathan pile has done an incredible job of documenting how to use the system that he's created and released out into the wild for anyone to use.  In addition to creating the interviews and using the variables from the interviews as Python variables to run any number of operations. There are a lot of projects where you know I use DACA symbol to ingest PDF files, strip the text out of the PDF files and run them through AI algorithms to classify the text using different legal topics. I mean that's one of the more complex things that you can do with it, but it's really simple to do it using DACA symbol it's quite amazing. So I want to thank, thank you all for having me I hope you enjoy the videos and thank you. Thank you for making them it made it so much easier to be able to get the environment set up than to do the first couple of things. One of the things we're using it for by the way, which is in keeping with the theme of our first release automated legal entities is to do a short interview with people that want to create the LLC is our use case and we basically elicit from them the name of the LLC, some things about the operating agreement, some other stuff, and then we're able to use DACA symbol to compile that into the correct filings with the Secretary of State to actually, and then to receive the information back using some little integration that we have through the rest API so you can create the entity and tomorrow if anyone's interested in hacking on that we've got some people flying in to further that prototype and then to sort of on a on a cron job do the annual filing to be to have listeners for emails if there's notices or things and then we haven't figured this part out yet but ultimately we want to include the dissolution of the LLC to kind of wind down the assets and do some of the other stuff that's more complex but the dream is to completely contain arise a legal entity. What is the user interface for that and how do you manage the document flow and the key events back and forth we we think DACA symbol can be a major part of that and we're using Kubernetes and some other technologies as well. And we're building some of our own Python but it's doc assemble in the middle and we think it we're testing it right now but we think it can provide the key components of a solution. And part of what we're doing with Sam is taking his videos which really do speak for themselves, but so that we can contribute something. We're developing little kind of online educational pages so that we're wrapping around the video links to key documents some checklists and things like that we've we've got a complete transcript of everything he said so you can follow along. Step by step a step by step guide. So, before we move to the next author any questions or comments on DACA symbol, because it's so good. Okay, so great thanks thanks again for joining us and thanks for your tutorials. Thank you. Next up we have one of our researchers in the human dynamics lab. So, can we apply computational load to legal systems in general, and data protection legal regime in particular. Well, I as per the legal tradition I provide a clear and ultimate answer to it, which is, it depends. And it depends on a lot of things related to the ontology of a legal system, because we have to deal first with many legal systems, not only the US common law, but we have the UK common law, the civil law system, and so forth. And then we have to deal with the structure of the legal system that is made up, not only of norms, but also of principles of hierarchies between the norms, and among the norms and the sources of the law. So, who can design the law, not only the parliament, but either at a local level. So our computational law system must be able to communicate among the legal systems and to embrace all the complexity of the ontology of these legal systems. So what I do in my contribution is essentially, first I make a comparison of these legal systems, how they work, how the privacy legal regime works differently in the US and in Europe, and I want to highlight something that might strike you. But in Europe, we don't own our data, we don't have the property of our data. So, just a clue, when you look at the GDPR, don't look at it as we have the property of the data. For that, I analyze also the limitations of the computational law systems nowadays. Why? Because we need for this complexity interpretation. So, I destructure computational law and the legal reasoning according to the methodology of a legal logic to find common ground for both of them, but still the interpretation is a limitation. So I then try to address this kind of issue with some provocation, one referred to the limitation of computational law, and that deals essentially also with semantics, because the norm is not just some word in some order with a fixed meaning, but it is composed in its proposition and precepts with several meanings that can overlap depending on the situation. So what I propose is to think about legal systems and especially civil law systems as a sort of quantum mechanics realm, because I found many analogies, which I believe are even ontological relationship between quantum mechanics and law. And essentially what I state is that if there's such analogy, we might exploit the same laws of quantum mechanics and the same approach to measure the phenomenon with the law. And with this approach, we might be able to understand several phenomenon or to predict them in a probabilistic way. I give you an example of the outcome of a jury because a jury is composed by humans. Humans behave according to several patterns, but they must stick within the framework of the law. And I give you an example of what I mean by the connection between quantum realm and for instance data protection. There's an effect in privacy that is called the chilling effect in which if you are the data subject and you are monitored constantly, you change your behavior. So if we drive along the street and we have the police car behind us, we behave differently. And this is exactly how the observer interacts with the observation, with the object of the observation. Last step, I finally connect some other dots, and I try to connect social physics with all these arguments. Why? Because social physics, as I believe in common with the law, one special characteristics. The norm, this precept, tend to or wants to modify the human collective behavior. And so does the social physics. So I try to describe how it can be used in this kind of system. Thank you. Awesome. Great. So we had said it's not a deep academic journal, but we do make exceptions that this is you'll see soon this article is actually very substantive. And one of the parts that I'm particularly happy about is the term setting. So, you know, these law in general society in general so cross jurisdictional so cross boundary so global now, that just the term setting between civil law jurisdictions and common law jurisdictions, and some of the basic dynamics and design patterns for international transactions is is one of the strongest contributions in this article, then the application to computational methods in a privacy context with data is flowing is such a great use case. And then these very MIT, I'd say, where the observations about analogous potential ways to look at it like as physics at quantum level and at other levels as well. I really think brought it all together. Let me answer. Are there any comments or questions on on that Diana. I'm just wondering if you're using Eisenberg principle because it seems so and transposing back to law. And the second question is, is most related with there's a lot of people working in flood usually philosophy natural language processing on signifiers and so on. One of problems is when we have a bunch of court decisions, the meanings change across time but the signifiers are the same. So the temporal networks analysis actually is a tool that we have been using lately, because even Roman law, for instance, is the same and today is the same but for instance, a woman is not property but in the end. So it's just kind of the input is this two ideas if you're looking to temporal networks analysis names in natural language processing. And the second one if you have the inspiration of Eisenberg principle because it seems so. I start from the second. Yes and no. So I didn't have an inspiration from that. I did have the inspiration from the chilling effect I described and then I started to think with this kind of quantum mechanics paradigm. The bottom up approach. And I did find many analogies with the, what I call the legal entanglement, the quantum legal leap and so on. It can be described in the low as well. So to answer the first question. Essentially, yes, natural language processing is part of my analysis was because I didn't mention it to sum up everything in a nutshell, but I address also the role of speech interfaces as the future primary tool of communication between the users or the professional users and a computational low system embedded in it. So that's absolutely part of the deal because the old sticks with the interpretation of the words in the correct meaning in that correct situation. So, we have time for one more. Fascinating. Under civil law, if one doesn't own their data. What do they own. Have you some hour to discuss it. So it's a short question long answer in a national and national. We have like sensible personality rights on it, but they are limited by the non availability of some rights like dignity, freedom, body disposition, and so on. So, my understanding of at least the genesis of it is, is that in Europe you have ownership rights, but not ownership ownership rights or rights to have a copy of it to understand the to understand and control the disposition of the data. Essentially, right. And so it, it. This way of thinking comes from English common law. Right. And I believe, I mean, what people tell me is that's the, the rights that you have so you don't actually own it in part because it's co created. So like, for instance, cell phone data, but you wouldn't have that data unless somebody invested billions of dollars to put up cell phones and provide you that service. Okay, so, so there's some, some argument about co ownership there. And that's a question of, of what are the rights on the two side. At least that's my understanding. As I said, I'm not a lawyer. Well, in the US it's, it's technically right for my knowledge to speak about ownership of the data, because in the US that protection deals with the appropriate paradigm, which entails several legal effects in Europe is it's reverted. So we can dispose of our data, we can decide how to, to give them, but the data are protected by others principle is true that on the same kind of data. And of the same data can, there can be some pluris subjective right. So we don't speak about a co ownership but pluris objective rights, which means, for example, my DNA is not only mine but provides information of my parents to so is a pluris objective data. The definition inferred by my metadata and by a data controller is my data because it's related to me. And so, given the GDPR definition is a data on which I have rights, but they have rights to and typically intellectual property rights. So there's a conflict. And that's why I claim, we will always need an interpretation, because a law is also about conflicts among the rights principles, interests, and so on. Thank you so much. Great. And so we promised you complex digital automated autonomous organizations. And John Clippinger is going to deliver on that. Put us in the context it's really great to be here for this, the formation is general. A few years ago, I was at the Berkman Center report is the Berkman Klein Center, and we set up something called a law lab we did Wilson since senior term sheet made it and put in list programs and tied it in cap tables and provide genetic genetic algorithms to evolving contract laws so see things happening. And then that was sort of an odd thing to do. And I'd like to build on Sandy's point that there is a new way of thinking about firms in a new way of thinking about capital and finance that has to reflect our time. And so they talk about what I call generative firms and generative capital they sort of go together. And the belief is we really have to invent new forms of organization that can deal with the challenge of our times. And one of my interests one of the things I did have done. I'm currently with a city science group. I used to be the Sandy's group and before that I was set as with Berkman started a number of companies they also set up something called token Commons Foundation Switzerland. Where we were concerned about climate change and how to create a way of recording and sort of generating certificates that you could trade Rex.  But we have a short period of time in which to make a transition from a static infrastructure to a dynamic infrastructure so a lot of things we're talking about decentralization. So what's happening AI driven systems or something is absolutely a necessity in order to meet these challenges. And so we also have the inequity challenges that are out there. And this is in rebels happening around the world we're the seeing the systemic changes we're seeing a change, just as you went from futility to market based economies we're moving out of market based economies we're reorganizing ourselves in very fundamental ways, with very strong consequences. And part of that, and my belief is that we got to design things that are not against nature but of nature that reflect natural principles and biologics and how they organize themselves. And actually it's much more, it's more much more adequate and much more sophisticated system designs and the mechanical designs that we've lifted from basically the 17th century trickle down doesn't work. We really have to, I think that's a recognition that we are whole structures of how we organize your society and look at how we invest capital you say okay we make a mess of things but then we'll set up a nonprofit involved will have some mechanism redistribution. The argument here is that we really have to build things that that are designed to make these changes. And so the carbon caps don't work. And so the what I would argue there's a need to have be an end to extractive capitalism that the idea of a third party makes an investment and take the money out of the system and then it accrues to a special group. And the capital dominates the whole allocation of resources in order to maximize returns for a small group. That is a fundamental principle that creates externalities that doesn't work. That's why we're in the crisis that we have both with the climate and our social inequities. Part of this is rethinking sort of the what a universal human rights are this notion of positive rights, and that was different than how the US Constitution is done and actually working with South Africa they have a very advanced constitution where they talk about positive rights. So people have an obligation to have a right to housing they have a right to health care and it's the obligation of the government to do that the government can be sued if it doesn't do that. So this is organized around outcomes is that sort of process, you're designing systems to achieve certain outcomes and you evaluate the system in terms of how it will achieve that outcome. Part of the work that we've done sort of the city science group is to create these analyses they collect data and look at cities and say okay what outcomes you want for a city or a community or a district. And that could be and how do we change the performance in terms of the city the whole collective interaction of different kind of asset types, different components of the city to change positive outcomes and that part of the positive outcome would be sustainability be be equity to eliminate pollution.  What are the processes what are the mechanisms that effectively achieve that. We, there's concept of gender to design, Neil Gershwin fellows in the fab led to the book on design gender designer reality. There's a lot of work that's being done on an auto desk and they've done a design where you start with a set of outcomes and then you just enter generate a lots of different variants of that you have a like a fitness function the slugs for certain attributes. How nature does it. So can we, we take some of those concepts imply them to the design of different kinds of firms different kind of organizational structures and our focus has been on zero carbon resilience affordability and equity. So we're we're looking at cities or area just not had to be a whole city could be a district and even could be smaller than that. How do we create affordable housing and how does that trade off against other kinds of requirements because you also want to have things that are sustainable and how do you have this balancing act between different kinds of challenges and how do you create the proper set of incentives. Part of my background has been also in the whole area of token on X and token design. So I've been advised a lot of different companies and how to design tokens and so what I call micro monetary policy, want to create the incentives to do this. It's a biologic design to generate explore asset combinations and realize a reduction in costs. So you how do you how do you really build that into the system and make an organism basically this design to achieve these kind of outcomes. One of the things that it's important to notice is that when you look at different they're the 22 super different sectors that are represented here. And basically what you're seeing is that most sectors when they get what they become exponential growth and become highly deflationary so there's there's there's a lot of value that's generated in reducing the cost of something. The coordination costs in order that should be captured in a lot of times it's not captured by the current mechanisms. And that that's one of the things that we want to do. The only thing we want to do is be able to create an asset class. That is actually what I call a strange attractor it's a tractor for investment that's negatively correlated with the current fossil, a fossil economy. So if you want to draw you have a short period of time you want to direct money into a particular a new infrastructure. This is a big challenge how do you do that how do you create the financial incentives to do that you start to see what's happening in fossil economies with the what they call stranded assets of oil and fossil, because now it's cheaper to produce energy through solar than intruded more fossil economy so there is starting to transition, but when you start to price in the full cost of the climate and the insurance guy you see what happens in California and you say well no no the actually, how do we factor that that's going to be a challenge to banks. So that we believe that that's going to create a demand for a new set of asset classes. So this is part of the general thinking. So the idea is a new firm a charter new kind of chart I mean that when we think of a firm and a corporation we think of it you know it's designed for how to admit how to aggregate capital how to just reduce liability, and how to distribute benefits to people are willing to put risk capital into what happened in tripping shipping and trade. But in this case we want to do something else and this is what the articles about what I call a reflexive mutual series LLC reflects even the sense that what you rather than extracting capital out to goes to a third party investor.  And then guard is an excellent example it's a six and a half trillion dollars successful mutual organization is able to reduce costs and benefits members is a series of something called a dollar series corporation that allows you to create sort of replicas of the same thing so you want to create something as viral that can replicate itself and maintain a certain set of principles and be interoperable so that's another design consideration we have. This is sort of the reverse engineering the traditional capital structure. I say one of the things I've been involved in designing tokens and utility token security tokens and how they have the regulations that surround that is it's still very murky right now. But if you look at the capital structure and how different kinds of assets are described you can you sort of reverse engine or their engineer those, not to the benefit of the third party the outside investor but to the mutual organization itself. So we have a, we have a platform which we're doing this it was designed for compliance and regulations security tokens but you can turn it on its head, have a different set of permissions around that. There's another one in there. Oh, here we go. Part of this idea is is to mimic biology and basically what I call sort of metabolic capitalism or our catalytic capitalism and so there's work that's done in complexity sciences about applying ideas that are used when they call our catalytic sets that things in combination create greater value than isolation. So that's what we think is that you don't look at just one asset class you can look at say at housing housing and combined with mobility combined with 5G in together create greater value than when seen by themselves. So the, the, somehow this is not cooperating here. But the end game here is is be able to create sort of local currencies that reinforce the value proposition of the particular mutual organization. And that's, that's in essence the, the, the concept. We're implementing it there's a project in South Africa and Cape Town that has properties that we're going to explore this idea of how do you, how do you achieve positive rights to this kind of organizational structure and capital structure. Wow. Okay, John, John Clippinger. Not so fast. I would keep any questions or feedback on that one. Reflexive adaptive. Really mutual born serious organization. Right. Okay, so, oh, here's a hand. When you listening. Can you just say a little bit more about the Cape Town project it sounds. Yeah. So what there is a developer in Cape Town is also an investment company that has bought a number of properties there. And, and, and he's his view is that you have a post colonial society has huge and so they want to do is be able to for someone who's paying the rent and paying basically rent thing for their energy paying certain utility bills or whatever be able to achieve ownership over a period of time and then build up an asset class basically they can pay off their obligation to the the finance or of the development but themselves take ownership and create a new kind of almost like a comments it's like a reserve asset that they own that they can use that to invest in other. So this this individual is once he has a property of about 1000. I think. Apartments but there's a whole set of so they want to look at this way as well. And the idea of there in their constitution they have a concept of positive rights. And the point is the positive right. So can you design the system in a sense to deliver against those those those those positive rights and hold it accountable. So it's a new is new governance mechanism. It's a new legal framework, and you we're using a blockchain and all that kind of stuff to create certain transparency. But it's to the point it rapidly evolves it's an iterative system it's not a fixed system it learns it gets piggyback and then keeps changing itself. And your obligation or your accountability is to the outcomes. John that hits the nail on the head. Really tremendous tremendous stuff. I just wanted to make a comment and it's that. Yes, this is all around the rubric of a circular economy but what's really interesting here is to put a slightly different spin on it. Essentially, the notion that you are your own currency, you are your own coin. Right. Think about that. You're paying, paying into the own your own system, generating your own asset for whatever you need, and then paying back into it changes the dynamics of everything. It just just add to that one of the slides didn't come through I don't know what happened but there's a, there's a whole idea of being able to create digital twins as a group called versus that allows you to. It's amazing stuff be be able to the machine learning recognize objects and give those objects, they'll have wallets and identities and permissions and they have. Spatial contracts that govern them. So you have this digital overlay in which you can sort of then have governance. And you can govern different kinds of physical activities around that so that, and it also has with it a D ID decentralized identity within it. So you're collecting data, agreeing governance mechanism but you're not violating the privacy. It's a very, it's a very powerful technique. Okay, here here. Thank you john. One more. So we're working hard to engineer, you know, the mechanics of completely digital organizations. And then, as we get better at that it raises the question. And what would you do with them in a digital economy here that's one vision. So, next up, we have a professor of law, Colin Starger, who's written another, it's a very interesting crossover article between telling that that could easily be published in a traditional law review and yet also is very much not only data driven in its observations, but also has data directly embedded in it. And so it's exactly the shape of the type of publication that that we seek. And his points of view are also very provocative so Colin, if you could introduce yourself and and and give us a flash talk on your article. Great. Can you hear me. Yes, you're coming through loud and clear. Okay, perfect. Thank you, Daza, and thank you Brian for organizing this I'm sorry folks that I can't be in the room with you although this is quite an interesting experience to see my giant face some miles away. I'm going to be a law professor at the University of Baltimore, and as Daza said I'm very many ways I'm just a traditional law professor I write academic articles, but I've also have a strong interest in social justice and and a technology background. And my idea for this article was to write something that I was calling a mobile native or we can otherwise think of it as a responsive law review article, based on an original data set that itself would be open for further analysis. And then subsequently, the article arises out of my work in the trenches of Maryland's pre trial justice system so for three years I ran the pre trial justice clinic here at the University of Baltimore, and that worked with our state public defender the state public defender to challenge cases in which indigent defendants had not achieved pre trial release they've been detained prior to their trial. And we would argue the students working in a law firm, which is the clinical model, many law schools across the country use it, would attempt to get those folks out making specific arguments under Maryland law, using a vehicle known as Vegas was essentially a bail appeal. But based on that work I saw a lot of alarming things and I realized that there was a need for an academic treatment. And the descriptive thesis of my argument is that the, the presumption of innocence is dead in the pre trial context and that we should be very alarmed about this. And the data that support my conclusion come from a massive database of scraped Maryland court records. And I analyze this data to look at a phenomenon of what's known as no leprosy key or no process, or simply the dropping charges so it's a Latin term no process is what is recorded when the prosecutor ops rather than to proceed with charges that were brought after an arrest or after an indictment to drop them. And the reason why this is troubling, the phenomenon itself isn't troubling at all but the context in which this is troubling is the pre trial context. And what happens just by way of background for those of you that don't know if you are arrested and charged with a crime. Very quickly you will go before a judge and that judge will determine whether or not you are going to be released prior to your trial date, or detained prior to your trial date many people are familiar with the bail, you pay money, and then if you return you get your money back, many people have heard about how unfair that is to poor people, unaffordable bails and there's a lot of motion around the country a lot of litigation that's been going on challenging back. But there's also a category of held without bail. So basically you can be held without bail you can be released on a bail which you may or may not make, or you can be released. And the problem is, is that many, many people are detained prior to trial, just to be clear prior to trial under the eyes of the law you are presumed innocent. They're detained either because they're unable to make a bail, or because the judge determines that the charges against them are so serious and their background is so bad that we're not going to risk letting them out. And then after 30 days, a month, two months, three months, all of the charges are dropped, and they're released from jail, having served all of that time for absolutely no point. And they're not reimbursed for that in any way, their lives are hugely disrupted, the lives of people around them that depend on them are hugely disrupted and it's all for naught. The assumption of innocence that they were always sort of wrapped in proved to be true because the charges were dropped. Now we saw this anecdotally in our practice a lot and our practice began to look at how widespread the problem was when we're formulating challenges but from the academic perspective from the perspective that I was wrapping into this article. I focused my research on the four largest counties in Maryland from the years 2013 through 2017 inclusive so that's five years and 167,000 cases that went moved through the district courts. And you should know that in 60% of those cases, 60% of the 167,000 cases, every single one of the charges was null crossed. The cases were dismissed in their entirety. And for a subset of about 12,000 people, 12,000 people over five years, not only were all of their charges dropped, but they were in jail the entire time, while their charges were pending.  That was the total shared between those 12,000 people approximately 12,000 people. On average they served 46 days before they were released with all of those charges dropped. And the median was about 39 days. So that's a serious disruption. And there's every reason to believe or so I argue that the problem is not isolated to Maryland, and the article kind of explains why this is the case and there's plenty of visualizations. So I should say that I undertook something unusual for law professor I suppose is that I undertook this analysis using a SQL database which I then imported into Python and roll into a flask app and did all these little things that could be of it being a mobile native law review. But with that sort of techie angle to it put to the side the second part of the article is more classic normative scholarship. And I argue why this outcome of having so many people incarcerated pretrial really for no reason and defying what the presumption of innocence should mean. And that can be blamed on a case called Bell the wolfish. So hence the title of the article the argument that cries wolfish, and without getting into the doctrine too much. Essentially there was dicta in that Supreme Court case that suggested that the presumption of innocence did apply pretrial, and it suggested that the presumption of innocence was merely a way of allocating the trial burden, the prosecutors burden to prove every element of the crime beyond a reasonable doubt. And for a number of reasons I argue that was wrong to begin with, and is wrong now, not the least of which was that that was decided in the era before plea bargaining, pretty much defined what happens. And that was the case of trial outcomes, and in which a trial was much more frequent was before the era of mass incarceration, and it was before this era that we're seeing up these mass null process. So that's the gist of the article I'm super excited that this is going to be part of the MIT's computational law report. And I hope that it makes a contribution that can help bring some traditional law review type of scholarship in with this extremely innovative form that Brian and Des are pioneering so thank you. Thank you. Yeah, so justice, you know, can it be quantified. Well, here's one way it can be or perhaps injustice is what we're really quantifying here. So that part about the, you know, over 100,000 cases that he was able to wrangle ultimately into sequel and then refactor into Python and those flascaps he talked about a part of how we're going to be how we are producing this media. So you'll be able to see the data and sort of explore the data a little bit embedded right within within the articles. And then we're also going to be staging the data as well in a public facing GitHub repository, after the article comes out so that people can figure out, you know, deeper insights, deeper meanings and we can use this as kind of like a springboard into a more advanced dialogue about what the data can do to compute the law so that it's more fair to everybody. Okay, so any. So, one question I have, I'm going to use my imaginary gavel. Would there be any objection here to our extending the adjournment from 12pm to say 1210pm. Okay, I wasn't sure. Okay, so hearing no objection by unanimous consent, we will adjourn at 1210. And is there any questions or feedback for Colin while we have him. How much does everybody love what Colin's doing. A lot. Yeah, here we go. Beautiful. Thank you so much Colin for for your work and for joining us for the inaugural issue. Thank you. So next up our last author we have a lot more content. You could read all about it here, but the next author, the last author we have to talk about the most recently accepted content is Professor Jonathan Askin. Thanks Dazza and I promise I'll be very quick, especially since we heard Dazza speak this morning of peanut butter and chocolate combining in his BLT analogies to legal tech. In any case, I got to tell you I've been in academic for 12 years and I've mostly been shiftlessly shiftlessly waiting for a publication like this.  That is legal academic scholarship. I think it's a monumental waste of our time and energy and it gets read by three subject matter experts and then it dies in the vortex. The fact that something like this now exists is sort of a dream come true for me, I've got, I can't tell you how many ideas are percolating in my head about things I want to write specifically for this publication and not for any of the other legal journals out there. The fact that we have this opportunity to cross pollinate with technologists with business people with scientists with policymakers is the scenario I envision when I started teaching and thought this would be the kind of stuff I should and could be writing. In fact, two years ago, Dazza and I and some others were instrumental in launching the computational law and blockchain festival. And out of that grew Stanford's computational law and blockchain journal, which I thought was going to be something like this but frankly it is another one of the legal academic journals that is designed and geared mostly for academics. I love the fact that this is reiterative that this allows for multimedia capabilities, most of which are beyond my abilities but certainly not among the abilities the rest of the folks in the room. So my students and I have been brainstorming to figure out how we can contribute effectively. We, in fact, my students and I were the legal counsel to establish the computational law report. And we had fitted with all sorts of new fangled dows and Vermont BB LLCs and platform cooperative mechanisms. We sort of overthought everything and decided at this point let's put a stake in the ground and make sure that there is a structure today for the MIT computational law journal so we're simple Massachusetts LLC. I mean, someone embarrassed to say but at least it's a placeholder structure to allow Dazza and Brian to take in proceeds and get the thing launching with some liability protection at least. So we thought the first interesting piece for us to write for the first issue would essentially be a short piece on using legal automation tools to set up a computational law report so it's sort of a meta self reflexive piece on the processes. We went through and the tools we experimented with and frankly 12 years of learning by my part on how to engage students to become digital attorneys and to what extent you allow them to use automation tools to allow them to get behind paywalls. Do you allow them to experiment with new corporate structures for innovative clients. So our piece is going to be sort of our thinking and rationale as we help these folks as legal support for this innovative sort of journal. It might be a little bit sort of lighthearted and tongue in cheek but the goal is to have something there for the first issue on the processes of automating a journal such as this. Indeed. Thanks Jonathan and thanks for helping us set up the LLC. This is this is in line with part of the podcast series that we're doing in addition to the substantive podcast with Harvard's case law access project and with others and the media with Sam Harden. We're also taking a page out of the book of gimlet media and we're doing a kind of like how I built this like two or three podcasts of just how the how this publication came apart came upon us and and and how we're structuring it differently. So we hope to feature Jonathan's articles part of that. And certainly actually they used a tool. We tried a few different tools but the final one we tried. I think it came out of the global legal. Did it come out of the thing with legal tech labs only that we collaborated on a few years ago. And you know they elicited, you know, not many is like 15 or 20 questions from us, and then just just propounded this like 30 page like really tight operating from Massachusetts LLC. So there's a lot of good learning in here. Okay, what's next. Oh, I forgot. Up next, we have the look ahead. So this is where we look ahead to what's going to be coming in the future beyond release one. And so we're going to be doing a lot more advisory editorial board and peer review outreach. We're going to be doing a lot of supporting organization outreach so if you know of any organizations that you know would be interested in stuff like this we would love to work with them. And we have the piece that Shawna had talked about earlier in the introductions that's being co led with cool Brian, Brian you listening where we're trying to figure out computational law solutions to some of these modern day slavery issues. And then we also have a pesky issue about getting an ISSN number. All that more. So all of our data is going to have a DOI with the ISSN. So it'll actually the data sets, as well as the articles and the applications will all show up in ways that actually make sense for citation and within law reviews and publishing. And so in this way we can actually begin to cite to data science in an inappropriate way that that you could include in a blue book. And so we're going to be doing some cited law review article or for that matter filings with a court or just for your academic enjoyment. Yeah, and then moving on beyond that for our second release, which is going to be first half of 2020. We're talking about this idea of opening up information, standardizing that information and figuring out how to derive more utility from the information that exists more than just something like a paper might, for example, we want to really focus on this transition from not just digital but to from paper to computational. And I think this will be a really fun theme we've already got a few people in the pipeline who are interested in submitting content and if you would be interested in submitting under this theme. I would be very excited to work with you and solicit as many things that you have to create and in as many forms as you have the brainpower to create. Could you pull up the pub website to include. Oh yeah. Right. So, so this idea for a computational law report actually did start a couple of years ago with a brainstorm that Jonathan mentioned with legal hackers our favorite group, civic hacking group. It's part of the inaugural computational on blockchain festival which is now in its third year. One of the, we had an idea of growing three publications from that one at Stanford focused on blockchain law and policy one here at MIT focused on computational law, and one at Berkeley at bolt, that was going to be more privacy focused. So we talked about that Stanford. They're so good at startups at Stanford, they actually got it up and running and they're about a year ahead of us. They've been very helpful with us in advising us from their experiences and helping us get up and running. So we're going to be able to bring up bolt up and running next. One of the things we're doing also I should say is we're trying to collaborate across these new fangled publications these media publications on in in in a new way of using the common tag set. So, there's already two or three issues of the Stanford law. We'll have our first one released in early December as Brian said, and we've had conversations with the editors of the crypto economic systems journal here in DCI digital currency initiatives publication Stanford and ours so that will be, we have basically a common, we have like a collaborative document where we're making the words in the tags for articles we're just using the same super set of tags. And we're working with our platform which is called pub pub. It's an open source publishing tool where we're publishing all of our, all of our publications. It's sort of like medium but you know even more featureful for this deeper type of publication to enable it so that for our, like I guess they call it federated or journals that when you click on one of those tags. All of the articles across our journals that are tagged with that with that term will show up. So we hope that this will be one of the ways that we can break down those barriers and and help to facilitate and catalyze idea flow. So we promised also a beta launch. This is not public yet because we do want to get a round of edits but can you if you yeah so on one of these that we in fact have the now current drafts of all of the content on line you can't see it in the public view. But if you log in with Brian's idea my ID, you can actually see all the content so that we're not lying it's really here. Yep, and let me get to one of the videos because those are probably on the more functional side but pub pub one of the nice things about it is that it allows for iframe. So we can just plop the YouTube videos in there. We can plop the flash gaps in there so that we can actually get more utility than you would be able to out of just something that's stuck in paper. And so that's really exciting to us. And so what while the idea for this started a couple of years ago, you know, the, at that point, the computational team at MIT was pretty small. I was the only main person and there was a few other concert visitors. What happened that was different was in our annual computational law course on this past year. Brian you listening and and many other people TMA and Brian Wilson Camila, the Brendan and many other people that aren't here. We got together and we're talking about it we just said to ourselves well why don't we just do it. Like let's just make it almost like a class project and we rapid prototype something on pub pub. Thank God, none of that content from that two hour session has survived, but it did take a page out of this legal hacking book and the MIT hacker ethic of let's do something quick that and iterate it over time. I just want to really underscore the primary role that Sandy had by providing leadership and space and some resources and partners like Thompson Reuters lab and cool Brian and others around the world who came who came forward to give us good ideas and to and to make things like coffee available. And most especially Brian Wilson, who basically has paused his previously scheduled life in Kansas City and and move to Boston and and who's done the lion share of the work putting all of this together. So, I, we can just if you'd please join me in taking a moment to recognize and thanks Brian Wilson. Thank you. And I'd also like to take a moment to thank Daza as well without getting connected with him four or five six years ago now I there's no way that I would be here and I don't think there's any way that something like this would be even possible. So let's give Daza a round of applause for enabling this idea. And with that, I've pulled up one of the pub pub pages that we haven't made public yet. It's a lot of pubs and one sentence. But no, so we're going to be having the 2020 IP computational law course this January, I think the dates that we settled on are the seventh, eighth and ninth, and we will be. posting a sign up form so that if you want to access that virtually, you will be able to. So, one of the things that we like about the course is it's also digital first so that everybody from around the world can be involved so that their voices can be heard and so that we can collaborate with people in places like Hong Kong and, you know, all these different parts of the world, Portugal Diana, who's also in our in our course and it's been a major help all around the world. And let me see, I think that's, and so if you want to know how to sign up and find out more law.mit.edu and for the launch in December, we'll be pointing law.mit.edu to this beautiful pub site. And so I want to thank everybody for coming. Thank you for the support. Thank you for making this possible. So let's engineer the law. Let's hack the law together. Thanks.