Fin (Legal) Tech - Law's Future from Finance's Past - Professors Daniel Martin Katz & Michael J. Bommarito II

99,319 views

Published on

In today's analogy du jour - we explore a variety of innovations in the financial technology space (i.e. fintech) and how they map to the current and future legal technology space.

Published in: Law, Technology
0 Comments
261 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
99,319
On SlideShare
0
From Embeds
0
Number of Embeds
6,683
Actions
Shares
0
Downloads
0
Comments
0
Likes
261
Embeds 0
No embeds

No notes for slide

Fin (Legal) Tech - Law's Future from Finance's Past - Professors Daniel Martin Katz & Michael J. Bommarito II

  1. daniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com law’s future from finance’s past page | DanielMartinKatz.com michael j bommarito blog | ComputationalLegalStudies.com corp | LexPredict.com page | bommaritollc.com Fin(Legal)Tech edu | chicago kent college of law edu | university of michigan cscs
  2. Today we only want to talk about one thing …
  3. Today we only want to talk about one thing … #Arbitrage
  4. But if we are going to talk about #Arbitrage
  5. Then we need to talk about why we *sometimes* 
 miss obvious opportunities
  6. opportunities that have been right under our noses all along
  7. we are all the hero of our own story
  8. we are all in our own filter bubble
  9. We must fight rigidity & impact of our own success
  10. Must get exposed to new ideas (most innovation in law started elsewhere)
  11. need to develop a
  12. less law centric view of the world
  13. the precursor to invention
  14. #arbitrage the precursor to invention
  15. see what the world is #arbitrage the precursor to invention
  16. see what the world is understand what it might become #arbitrage the precursor to invention
  17. Three Types of Lawyers (as described by paul lippe)
  18. play “whack-a-mole”, reacting to problems by creating fear and friction within organizations and the impression that there is a legal risk around every corner. Mediocre Lawyers
  19. can help clients shape (perhaps distort) external perception of risk. Merely Clever Lawyers
  20. design systems that balance risk and improve transparency, helping clients correctly price risk internally Great Lawyers
  21. Lawyer VALUE PROPOSITION (From the Client’s Perspective) (internal or external client)
  22. transactional = help price risk / help reduce information asymmetries
  23. litigation = transactional = characterize (predict) risk/exposure shift the expected value of a lawsuit help price risk / help reduce information asymmetries
  24. litigation = characterize (predict) risk/exposure shift the expected value of a lawsuit compliance = identify + prevent rogue behavior monitor behavior in (near) real time help price risk / help reduce information asymmetries transactional =
  25. the analogy du jour
  26. law = finance (insuranceaswell)
  27. law < > finance many elements in law look like finance did 25 - 50 years ago (on the long road from Black-Scholes to algorithmic trading)
  28. this is an extension of this prior talk by mike bommarito
  29. Dominant Model in Law expert centered pricing of risk
  30. Dominant Model in Law lots of unintentional self insurance rarely (if ever) based upon explicit risk models
  31. Cult of one 
 (or very small # of) person(s) thinking drives decisions with serious financial consequences
  32. hard to move to more rigorous models given borderline pathological numerophobia among lawyers
  33. Claim: fin(tech) offers lessons for many areas in law
  34. thesis statement: the financialization of the law will be an important vector of the next decade(s) in law
  35. The Two Major Branches in #FinTech
  36. The Two Major Branches in #FinTech removing socially meaningless frictions (from financial processes)
  37. The Two Major Branches in #FinTech removing socially meaningless frictions characterizing (pricing) increasingly 
 exotic 
 forms of risk(from financial processes)
  38. #Fin(Legal)Tech application of those ideas and technologies to a wide range of law related spheres including litigation, transactional work and compliance.
  39. Fin(Legal)Tech
  40. the path of fin(tech) has in part followed developments in artificial intelligence
  41. There has been lots of recent interest in applying artificial intelligence to law
  42. data driven AI rules based AI Competing Orientations in Artificial Intelligence
  43. expert systems Computational Law Data Driven Rules Based prediction models and methods network analytic methods natural language processing self executing law visual law computable codes
  44. we see a decent amount of rules based AI in legal industry
  45. that is actually pretty consistent with path of A.I. in general
  46. lots of issues with expert systems and/or rules based A.I. (without data or an evolutionary dynamic)
  47. Ultimately we are trying to learn the rules / dynamics that underlie some class of activity
  48. With that understood we want to be able to mimic / predict
  49. rules based A.I. data driven A.I. 1980’s, 1990’s, Early 2000’s >
  50. rules based A.I. data driven A.I. 1980’s, 1990’s, Early 2000’s rules based A.I. data driven A.I. 2005 - Present < > ~
  51. A.I. State of the Art
  52. A.I. State of the Art purely data centric
  53. A.I. State of the Art purely data centric augment expert forecasts w/ data
  54. iterative data < > rules A.I. State of the Art purely data centric augment expert forecasts w/ data
  55. fin(tech) is commercial field where huge advances have been made in science of prediction
  56. is an emerging field where the tools of predictive analytics are finally being employed fin(legal)tech
  57. some PUBLIC examples (many more proprietary examples)
  58. There are 3 Known Ways to Predict Something fin(tech)Borrowing in part from
  59. Experts, Crowds, Algorithms
  60. example from our own work
  61. predicting the decisions of the Supreme Court of the United States #SCOTUS
  62. Experts
  63. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T. Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  64. experts
  65. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts From the 68 Included Cases for the 2002-2003 Supreme Court Term
  66. these experts probably overfit
  67. they fit to the noise and not the signal
  68. if this were finance this would be trading worse than S&P500
  69. #BuffetChallenge
  70. #NoiseTrading
  71. law is full of 
 noise predictors …
  72. we need to evaluate experts and somehow benchmark their expertise
  73. from a pure forecasting standpoint
  74. the best known SCOTUS predictor is
  75. the law version of superforecasting
  76. Crowds
  77. crowds
  78. https://fantasyscotus.lexpredict.com/case/list/ We can generate Crowd Sourced Predictions
  79. however, not all members of crowd are made equal
  80. we maintain a ‘supercrowd’ which is the top n% of predictors up to time t-1
  81. the ‘supercrowd’ outperforms the overall crowd (and also the best single player)
  82. as of May 16, 2016
  83. not enough crowd based decision making in institutions
  84. “Software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge.”
  85. here is our commercial offering
  86. design to unlock untapped expertise in organizations #Winning
  87. Allowing for Frictionless Crowdsourcing #ManualUnderwriting
  88. Allowing for Easy Data Aggregation #KeepingScore
  89. https://lexsemble.com/
  90. https://lexsemble.com/
  91. Algorithms
  92. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T. Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  93. Ruger, et al (2004) relied upon Brieman(1984)
  94. Ruger, et al (2004) relied upon Brieman(1984) i.e. a single tree (as shown below)
  95. Leo Brieman moved away from CART in Brieman (2001)
  96. Breiman, L.(2001). Random forests. Machine learning, 45(1), 5-32. Published in Machine Learning (A Springer Science Journal)
  97. One well-known problem with standard classification trees is their tendency toward overfitting
  98. This is because standard decision trees are weak learners
  99. Random forest is an approach to aggregate weak learners into collective strong learners (think of it as statistical crowd sourcing)
  100. Random Forest: Group of DecisionTrees Outperforms and is more Robust (less likely to overfit) than a Single DecisionTree
  101. http://machinelearning202.pbworks.com/w/file/fetch/37597425/ performanceCompSupervisedLearning-caruana.pdf Random Forest (particularly with special config/ optimization) have proven to be unreasonably effective
  102. Our algorithm is a special version of random forest Black Reed Frankfurter Douglas Jackson Burton Clark Minton Warren Harlan Brennan Whittaker Stewart White Goldberg Fortas Marshall Burger Blackmun Powell Rehnquist Stevens OConnor Scalia Kennedy Souter Thomas Ginsburg Breyer Roberts Alito Sotomayor Kagan 1953 1963 1973 1983 1993 2003 2013 9-0 Reverse 8-1, 7-2, 6-3 19 19 19 19 19 20 20 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - Reverse 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - 8-1, 7-2, 6-3 9-0 19 19 19 19 19 20 20 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244 http://arxiv.org/abs/1407.6333 available at
  103. we have developed an algorithm that we call {Marshall}+ ~ random forest
  104. Benchmarking since 1953 + Using only data available prior to the decision Mean Court Direction [FE] Mean Court Direction 10 [FE] Mean Court Direction Issue [FE] Mean Court Direction Issue 10 [FE] Mean Court Direction Petitioner [FE] Mean Court Direction Petitioner 10 [FE] Mean Court Direction Respondent [FE] Mean Court Direction Respondent 10 [FE] Mean Court Direction Circuit Origin [FE] Mean Court Direction Circuit Origin 10 [FE] Mean Court Direction Circuit Source [FE] Mean Court Direction Circuit Source 10 [FE] Difference Justice Court Direction [FE] Abs. Difference Justice Court Direction [FE] Difference Justice Court Direction Issue [FE] Abs. Difference Justice Court Direction Issue [FE] Z Score Difference Justice Court Direction Issue [FE] Difference Justice Court Direction Petitioner [FE] Abs. Difference Justice Court Direction Petitioner [FE] Difference Justice Court Direction Respondent [FE] Abs. Difference Justice Court Direction Respondent [FE] Z Score Justice Court Direction Difference [FE] Justice Lower Court Direction Difference [FE] Justice Lower Court Direction Abs. Difference [FE] Justice Lower Court Direction Z Score [FE] Z Score Justice Lower Court Direction Difference [FE] Agreement of Justice with Majority [FE] Agreement of Justice with Majority 10 [FE] Difference Court and Lower Ct Direction [FE] Abs. Difference Court and Lower Ct Direction [FE] Z-Score Difference Court and Lower Ct Direction [FE] Z-Score Abs. Difference Court and Lower Ct Direction [FE] Justice [S] Justice Gender [FE] Is Chief [FE] Party President [FE] Natural Court [S] Segal Cover Score [SC] Year of Birth [FE] Mean Lower Court Direction Circuit Source [FE] Mean Lower Court Direction Circuit Source 10 [FE] Mean Lower Court Direction Issue [FE] Mean Lower Court Direction Issue 10 [FE] Mean Lower Court Direction Petitioner [FE] Mean Lower Court Direction Petitioner 10 [FE] Mean Lower Court Direction Respondent [FE] Mean Lower Court Direction Respondent 10 [FE] Mean Justice Direction [FE] Mean Justice Direction 10 [FE] Mean Justice Direction Z Score [FE] Mean Justice Direction Petitioner [FE] Mean Justice Direction Petitioner 10 [FE] Mean Justice Direction Respondent [FE] Mean Justice Direction Respondent 10 [FE] Mean Justice Direction for Circuit Origin [FE] Mean Justice Direction for Circuit Origin 10 [FE] Mean Justice Direction for Circuit Source [FE] Mean Justice Direction for Circuit Source 10 [FE] Mean Justice Direction by Issue [FE] Mean Justice Direction by Issue 10 [FE] Mean Justice Direction by Issue Z Score [FE] Admin Action [S] Case Origin [S] Case Origin Circuit [S] Case Source [S] Case Source Circuit [S] Law Type [S] Lower Court Disposition Direction [S] Lower Court Disposition [S] Lower Court Disagreement [S] Issue [S] Issue Area [S] Jurisdiction Manner [S] Month Argument [FE] Month Decision [FE] Petitioner [S] Petitioner Binned [FE] Respondent [S] Respondent Binned [FE] Cert Reason [S] Mean Agreement Level of Current Court [FE] Std. Dev. of Agreement Level of Current Court [FE] Mean Current Court Direction Circuit Origin [FE] Std. Dev. Current Court Direction Circuit Origin [FE] Mean Current Court Direction Circuit Source [FE] Std. Dev. Current Court Direction Circuit Source [FE] Mean Current Court Direction Issue [FE] Z-Score Current Court Direction Issue [FE] Std. Dev. Current Court Direction Issue [FE] Mean Current Court Direction [FE] Std. Dev. Current Court Direction [FE] Mean Current Court Direction Petitioner [FE] Std. Dev. Current Court Direction Petitioner [FE] Mean Current Court Direction Respondent [FE] Std. Dev. Current Court Direction Respondent [FE] 0.00781 0.00205 0.00283 0.00604 0.00764 0.00971 0.00793 TOTAL 0.04403 Justice and Court Background Information Case Information 0.00978 0.00971 0.00845 0.00953 0.01015 0.01370 0.01190 0.01125 0.00706 0.01541 0.01469 0.00595 0.02014 0.01349 0.01406 0.01199 0.01490 0.01179 0.01408 TOTAL 0.22814 Overall Historic Supreme Court Trends 0.00988 0.01997 0.01546 0.00938 0.00863 0.00904 0.00875 0.00925 0.00791 0.00864 0.00951 0.01017 TOTAL 0.12663 Lower Court Trends 0.00962 0.01017 0.01334 0.00933 0.00949 0.00874 0.00973 0.00900 TOTAL 0.07946 0.00955 0.00936 0.00789 0.00850 0.00945 0.01021 0.01469 0.00832 0.01266 0.00918 0.00942 0.00863 0.00894 0.00882 0.00888 Current Supreme Court Trends TOTAL 0.14456 Individual Supreme Court Justice Trends 0.01248 0.01530 0.00826 0.00732 0.01027 0.00724 0.01030 0.00792 0.00945 0.00891 0.00970 0.01881 0.00950 0.00771 TOTAL 0.14323 0.01210 0.00929 0.01167 0.00968 0.01055 0.00705 0.00708 0.00690 0.00699 0.01280 0.01922 0.02494 0.01126 0.00992 0.00866 0.01483 0.01522 0.01199 0.01217 0.01150 TOTAL 0.23391 Differences in Trends
  105. Total Cases Predicted Total Votes Predicted 7,700 68,964
  106. Justice Prediction Case Prediction 70.9% accuracy 69.6% accuracy From 1953 - 2014
  107. If You Want a Taste of How the Algorithm Works …
  108. Quantitative Methods for Lawyers
  109. http://www.quantitativemethodsclass.com/Professor Daniel Martin Katz Intro Class
  110. Legal Analytics Professor Daniel Martin Katz Professor Michael J Bommarito II
  111. http://www.legalanalyticscourse.com/Professor Daniel Martin Katz Professor Michael J. Bommarito II Advanced Class
  112. Experts, Crowds, Algorithms
  113. For most problems ... ensembles of these streams outperform any single stream
  114. Humans + Machines
  115. Humans + Machines >
  116. Humans + Machines Humans or Machines >
  117. question is how to assemble such streams for particular problems
  118. so that we are not required to rely exclusively on experts
  119. law is a field dominated by individual human experts
  120. in most fields - significant quality improvements have been made by moving from experts to ensembles
  121. Ensembles come in various forms
  122. Here is a well known example
  123. Poll Aggregation is one form of ensemble where the learning question is to determine how much weight (if any) to assign to each individual poll
  124. poll weighting
  125. A Visual Depiction of How to build an ensemble method in our judicial prediction example
  126. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model
  127. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model via back testing we can learn the weights to apply for particular problems
  128. Given our ability to offer forecasts of judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
  129. Paper Released August 24, 2015 http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
  130. lots of litigation decisions are just a version of this basic idea law = finance
  131. lots of litigation decisions are actually implicit litigation finance (or self insurance) #fin(legal)tech
  132. of course the most well known fin(legal)tech is current litigation finance industry
  133. funding sources includes: institutional crowdfunding competitive funding platform
  134. institutional www.burfordcapital.com/ http://www.gerchenkeller.com/ http://www.fulbrookmanagement.com/ http://www.longfordcapital.com/ http://www.benthamimf.com/
  135. competitive funding platform think of it as https://mighty.com/
  136. https://www.lexshares.com/crowdfunding
  137. are these funding decisions:
  138. impressionistic investing ? are these funding decisions:
  139. are these funding decisions: impressionistic investing ? or based on real underwriting ?
  140. impressionistic investing ?
  141. a lack of real transparency allows for massive returns lit(fin) [today]
  142. greater transparency reduces margins + requires better underwriting lit(fin) [future] +
  143. better underwriting requires leveraging experts + crowds + algorithms to allow for better predictions
  144. fin(legal)techpricing as
  145. hourly rates alternative fees vs.
  146. this is a really finance/insurance question
  147. Who should bear the cost associated with an overrun?
  148. As clients demand a shift toward AFA’s
  149. Question is how to rigorously underwrite / predict costs of matters?
  150. Weak Lawyer: Asserts dimensions along which their case is a special snowflake
  151. Strong Lawyer - Starts Embracing
  152. #LegalAnalytics #LegalData #PredictionScience #fin(legal)tech
  153. #fin(legal)tech law firm pricing goal
  154. it is *not* predicting cost of this particular matter where n=1
  155. correctly characterize the distributional properties of a portfolio of matters
  156. including identification of outliers both + and -
  157. apply portfolio theory
  158. to take n=1 and scale to n=many #fin(legal)tech
  159. #fin(legal)tech #self insurance today this is how you would run a more rigorous version of
  160. AIG to Launch Data- Driven Legal Ops Business in 2016 https://bol.bna.com/aig-to- launch-data-driven-legal- ops-business-in-2016/
  161. #fin(legal)tech tomorrow? learn from legal ops service offering to build a commercial insurance product offering legal cost insurance ? other exotic insurance offerings?
  162. #fin(legal)tech In such a world, Law Firm is *not* interfacing with client but rather insurance company regarding fees
  163. fin(legal)tech Transactional Work as
  164. we just discussed price of lawyers
  165. now lets think about transactional value
  166. Meet Bob
  167. Meet Bob lawyer on a major corporate transaction
  168. Meet Bob Bob is about to engage in yet another round of markup on deal terms lawyer on a major corporate transaction
  169. Meet Bob Bob is about to engage in yet another round of markup on deal terms this round is likely to generate a delay on the expected close of the deal lawyer on a major corporate transaction
  170. how much value is created by these modifications? how much delay will be introduced? vs.
  171. Need a better understanding of the actual drivers of risk
  172. Being able to compute the change in risk as a function of a change in deal terms
  173. Outside of M+A Requires Mapping of Deal Terms to actual substantive outcomes #legaldata #legalanalytics
  174. this is particularly important when non-lawyers are doing the negotiation (for example your global sales force)
  175. fin(tech) fin(legal)techvs. Additional Lessons
  176. fin(tech) is commercial field where there have have been huge advances in working with unstructured data
  177. 80%+ of the world’s data is unstructured data
  178. in a variety of ways fin(tech) has already confronted this
  179. Solution is to either let tech or human process that data
  180. And humans are actually pretty good pattern detectors
  181. But only for certain types of problems
  182. Trading (HFT in particular) is about looking for anomalies 60 Seconds of HFT
  183. Two Relevant Examples of Anomaly Detection in Law
  184. example 1
  185. the discovery + compliance convergence
  186. a hard #bigdata problem in law (near real time) compliance FCPA, Product Defect, etc.
  187. the goal is near real time monitoring
  188. defect w/5 ‘airbag’ version 1.0 backdate w/5 ‘option’ etc.
  189. near real time monitoring of version 2.0 a massive volume of communications
  190. Corp Security Beginning to mirror today’s NSA
  191. Behavior will change (i.e. rogue action will be done offline) Corp Security Beginning to mirror today’s NSA
  192. Behavior will change But Behavior Change will lag (i.e. rogue action will be done offline) (i.e. folks will craft incriminating communications at least for a while) Corp Security Beginning to mirror today’s NSA
  193. thus, discovery (in part) becomes compliance and some (only some) litigation is avoided legal standards will still shift real time monitoring will generate lots of false positives
  194. example 2
  195. we all have a tell
  196. lots of efforts to trade on sentiment lessons from fin(tech)
  197. sentiment analysis
  198. sentiment analysis
  199. understanding your opponents or other key decision makers tells (and your own)
  200. legal sentiment analysis a new source of competitive legal intelligence
  201. combating complexity through information mgmt. lessons from fin(tech)
  202. Information Management is a significant problem in legal
  203. data that could inform operations is not collected / or not regularized
  204. information necessary to undertake due diligence or other regulatory exercises is locked in an antiquated format (i.e. pdf, word, tif file)
  205. Dodd-Frank RRP for SIFI’s (Systemically Important Financial Institution) EXAMPLE:
  206. Resolution & Recovery Plans are Living Wills for Banks
  207. “The living will is effectively a roadmap and simulation of the largest possible series of transactions in a bank’s lifetime, the type of analytical exercise that is common in electronic systems design or software testing, but unprecedented in law.”
  208. Ideal RRP is a ‘War Game’ whereby a SIFI demonstrates it is robust to failure of various counterparties
  209. but requires review and understanding of the set of agreements across all business lines (p&l’s)
  210. problem is legal work product is not a pointable data object
  211. horizontal integration of legal work product in the broader corporate technology ecosystem represents a source of immediate value creation
  212. “Watson [and related machine learning technologies] will catalyze better organization of legal information and legal data, forcing organizations to better manage their current data and delivering substantial returns from this information management step alone....”
  213. for example - contracts should be born (or processed) as computational objects to point straight into finance/acct and other relevant IT systems stored legal work product
  214. sensor data + contracts talking to other contracts
  215. #InternetofContracts This is the
  216. #InternetofContracts which is a special case of the
  217. #InternetofLegalThings which is a special case of the
  218. which is a special case of the
  219. which is a special case of the #InternetofThings #IOT
  220. we are starting a decade(s) long process of overhauling the global financial infrastructure
  221. it is a massive friction reduction exercise
  222. Big 4 vs. Big Law who will get to drive this agenda? (i will bet on the big 4)
  223. only time will tell …
  224. #fin(tech)
  225. #fin(tech)
  226. #fin(tech)
  227. #fin(tech)
  228. #fin(tech)
  229. #fin(tech)
  230. but blockchain is important bitcoin is probably not that important
  231. SOME CONCLUDING IMPLICATIONS
  232. in order to support enterprise quality risk models remove unnecessary friction
  233. < >Implication #1
  234. every organization in law needs a data strategy
  235. Capture, Clean, Regularize Data to support a range of tasks
  236. Deploy Data for Specific Enterprise Applications Develop a data roadmap http://legaldatastrategy.com/
  237. http://legaldatastrategy.com/
  238. < >Implication #2
  239. every organization in law needs a relevant human capital #LegalAnalytics
  240. Either going to need homegrow your own talent
  241. and/or work with organizations who can help (consultants, etc.)
  242. Finally, we are organizing 
 this conversation
  243. FinLegalTechConference.comNovember 4, 2016
  244. Associate Professor of Law IllinoisTech - Chicago Kent Affiliated Faculty Stanford CodeX Center for Legal Informatics College of Law Chief Strategy Officer LexPredict
  245. Fellow Stanford CodeX Center for Legal Informatics Adjunct Professor University of Michigan Center for Study of Complex Systems Chief Executive Officer LexPredict
  246. LexPredict.com
  247. ComputationalLegalStudies.com BLOG
  248. @ computational
  249. TheLawLab.com
  250. Michael J. Bommarito II @ mjbommar computationallegalstudies.com lexpredict.com bommaritollc.com university of michigan center for the study of complex systems@
  251. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@

×