Make Law Better - The Legal Innovation Agenda in Vectors and Phases

Make Law Better ( #MakeLawBetter) - The Legal Innovation Agenda in Vectors and Phases

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1. The Legal Innovation Agenda in Vectors and Phases daniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com edu | illinois tech - chicago kent law lab | TheLawLab.com #MakeLawBetter page | DanielMartinKatz.com
2. MY OPENING STATEMENT
3. IS THE MOST TRIVIALLY OBVIOUS OF ALL OBSERVATIONS
4. AT ALL LEVELS …
5. AND NOTWITHSTANDING THE SUBMARKET IN QUESTION …
6. LAW IS FAILING TO DELIVER VALUE COMMENSURATE WITH THE COSTS WE IMPOSE …
8. THERE ARE MANY REASONS FOR THE CHALLENGES WE FACE …
9. BUT I THINK THE MOST SIMPLE EXPLANATION IS THAT …
10. THIS IS A FIELD HISTORICALLY CENTERED AROUND SUBJECT MATTER EXPERTISE
11. AND *NOT* FOCUSED ON OPERATIONAL EXCELLENCE
13. SO OUR CONVERSATION IS *NOT* ABOUT THE CONTENT OF ANY PARTICULAR LEGAL RULE …
14. INSTEAD THIS IS ABOUT HOW WE CAN CONTINUE TO … #MAKELAWBETTER
15. BECAUSE NOTWITHSTANDING THE CHALLENGES
16. IT IS NOT ALL DOOM AND GLOOM …
17. THERE ARE GREEN SHOOTS
18. THERE ARE AN INCREASING NUMBER OF FOLKS …
19. WORKING GLOBALLY … WORKING DOMESTICALLY …
20. TO ADVANCE THE LEGAL INNOVATION AGENDA
21. I WOULD BE REMISS IF I DID NOT MENTION THIS EVENT WHICH RECENTLY TOOK PLACE …
22. 22 COUNTRIES 40 PARTICIPATING CITIES 600+ HACKATHON TEAMS ~5000 PARTICIPANTS GLOBALLY
23. I THINK IT JUST ONE OF MANY SIGNALS THAT INDICATE THAT THERE IS A REAL APPETITE FOR CHANGE
25. BY WAY OF INTRODUCTION
26. I AM AN ACADEMIC
27. I AM A SCIENTIST & TECHNOLOGIST
28. I AM A SCIENTIST & TECHNOLOGIST
29. I AM A TEACHER
30. LexPredict.com CONSULTANT & ADVISOR TO THE FORTUNE 1000, AM LAW 200, LEGAL TECH STARTUPS, ETC.
31. I AM IN THE LEGAL INNOVATION AND VENTURE SPACE AS AN ADVISOR
32. LexPredict.com I AM AN EMPLOYER 15+ TEAM MEMBERS
33. I MENTION ALL OF THIS ONLY SO THAT YOU MIGHT BE ABLE TO ORIENT SOME OF MY COMMENTARY
34. AND UNDERSTAND THAT I AM *NOT* A MERE SPECTATOR OR COMMENTATOR BUT RATHER AN ACTIVE PARTICIPANT … #SKININTHEGAME
35. SO TODAY ALLOW ME TO SHARE SOME OF MY NOTES FROM THE FIELD
36. THE LEGAL INNOVATION AGENDA
38. WHEN WE TALK ABOUT ‘INNOVATION’ IN LEGAL …
39. THE ROBOT LAWYERS NARRATIVE IS THE FAVORITE STORY IN THE MEDIA
49. THERE IS MUCH MORE TO THE LEGAL INNOVATION CONVERSATION
50. IN OTHER WORDS, #LEGALINNOVATION IS FAR BROADER THAN JUST #LEGALTECH PER SE
51. LEGAL INNOVATION LEGAL TECH
52. LEGAL INNOVATION CAN BE DECOMPOSED INTO AT LEAST SIX VECTORS
53. SIX VECTORS OF LEGAL INNOVATION
54. PROCESS IMPROVEMENT A.I. + LEGAL ANALYTICS HUMAN CAPITAL + CULTURE INTELLIGENT MONETIZATION MARKET(S) STRUCTURE + DYNAMICS PRODUCTIZATION AND SCALE
56. MARKET(S) STRUCTURE
57. OF COURSE THERE IS NO LEGAL ‘MARKET’ PER SE BUT RATHER A SET OF SUBMARKETS
58. (VISUAL VIA BILL HENDERSON)
59. Service The Nature of the Offering Type of Client (Prototypical) (Product + Service) Hybrid Product Fortune 1000 Mid Cap Companies Retail A2J THE MARKET(S) IN LAW
60. ALTHOUGH A MORE NUANCED ACCOUNT COULD BE OFFERED FOR EACH SUBMARKET
61. IN GENERAL, THE INCUMBENTS ARE UNDER ATTACK FROM VARIOUS DIRECTIONS … #DEATHBY1000CUTS
62. NOT ONE SINGLE ‘DISRUPTOR’ BUT RATHER THE SUM OF VARIOUS DISRUPTIVE TRENDS #DEATHBY1000CUTS
64. OBVIOUSLY LAW FIRMS ARE ALWAYS COMPETING AGAINST OTHER LAW FIRMS …
65. THE (OTHER) COMPETITORS (i.e. the entities folks might not see)
66. BIG 4 LPO’S #LEGALTECH CLIENTS
75. BIG 4 LPO’S #LEGALTECH CLIENTS
78. "This reflects the remarkable rate of change taking place in the legal market," said Liam Brown, Founder and Chairman of Elevate, "and it demonstrates that Elevate has assembled the right team at the right time to help our clients address the complex business challenges and unprecedented cost pressures they face.
83. It has $300+ million in revenue in 2016 The equivalent of an AMLaw 100 Law Firm
84. 2000+ total employees
86. LABOR ARBITRAGE PROCESS ARBITRAGE DATA / TECHNOLOGY INTEGRATION LADDER TO A FULL PLATFORM SOLUTION
88. BIG 4 LPO’S #LEGALTECH CLIENTS
89. LEX.STARTUP
90. OVER THE PAST FEW YEARS, WE HAVE SEEN SIGNIFICANT GROWTH IN THE SHEER NUMBER OF STARTUPS …
91. THERE ARE A VARIETY OF DIFFERENT LISTS
92. ANGELLIST 749+ ‘LEGAL TECH’ COMPANIES 1800+ IF YOU ONLY SEARCH UNDER ‘LEGAL’ HTTPS://ANGEL.CO/COMPANIES? MARKETS%5B%5D=LEGAL+TECH
93. tech.law.stanford.edu STANFORD CODEX LIST 790+ COMPANIES
94. 691 COMPANIES ON THIS LIST INCLUDING MY COMPANY WHICH IS NOT INCLUDED ON THE OTHER TWO LISTS THEREFORE BOB’S 
 LIST IS THE BEST LIST :) https://www.lawsitesblog.com/2016/04/towards- accurate-listing-legal-tech-startups.html
95. THINGS ARE HEATING UP ON THE CONTINENT AS WELL BEYOND US + UK
96. FRENCH LEGAL TECH GERMAN LEGAL TECH
97. BELGIUM LEGAL TECH DUTCH LEGAL TECH
98. AND BEYOND …
99. AUSTRALIA LEGAL TECH ISRAEL LEGAL TECH
100. AFRICAN LEGAL TECH http://legaltechafrica.co/startups/
101. BRAZIL LEGAL TECH MEXICO LEGAL TECH
103. ALTHOUGH LEGAL IS A RELATIVELY SMALL SECTOR OF THE ECONOMY
104. WE HAVE HAD SOME WAVES OF VENTURE FUNDING …
105. HERE ARE JUST A FEW EXAMPLES …
106. 5.8M SERIES A 2M SEED
107. 8.7M SERIES A 4.3M SEED ROUND
108. 12M SERIES B 7M SERIES A 1.8M SEED
109. 10.25M+ RAISED THUS FAR
110. 1M SERIES A 150K SEED
111. 20M SERIES C 7M PRIOR ROUNDS
112. 125M+ RAISED THUS FAR
113. OF COURSE WE HAVE HAD A NUMBER OF FAILED COMPANIES #PARFORTHECOURSE
114. BUT WE HAVE ALSO SEEN A NUMBER OF NOTABLE EXITS
117. R e p o r t e d s a l e price between $35 million and $40 million. Final Number was likely between $80 - $100 million A n u m b e r o f venture capitalists have invested in t h e c o m p a n y , including Silicon Valley’s Sequoia C a p i t a l w h i c h invested $7 million in 2007 ....
121. COLLECTIVELY THESE ENTITIES ARE DOING THE LION’S SHARE OF LEGAL INDUSTRY R + D
122. AND THERE IS STILL PLENTY TO PLAY FOR …
123. SIGNIFICANT AMOUNTS OF CAPITAL ARE COMING INTO THE MARKET IN JUST THE PAST MONTHS
133. BIG 4 LPO’S #LEGALTECH CLIENTS
134. EVERY CUSTOMER IN ANY SECTOR FACES THE DIY VS BUY DECISION
137. ~450 ~1200+ Number of Attendees Number of Attendees 2016 2017
138. #cloc2017
139. #cloc2018 2200+ Attendees
140. Legal Operations
141. Legal Operations
142. Legal Operations
147. SOME OTHER RELATED EXAMPLES
148. 36! “From!se)lement!informa0on!and! contracts! to! sensi0ve! client! data! and! beyond,! Liberty! Mutual! creates! and! stores! ever:growing! volumes! of! unorganized! data! across! its! worldwide! offices! and! databases.”! “I've!seen!a!real!transforma0on!in! the! legal! department! just! having! t h a t! i n f o r m a 0 o n! v i s u a l l y! available."! “The' legal' department' is' now' w o r k i n g' p r e d i c 7 v e' a n d' prescrip7ve' analy7cs,"' i.e.' ways' to' analyze' data' that' enable' forecas7ng'for'legal'issues.”'
149. 34!
150. 37! “I"believe"strongly"that"data"analy2cs"is" a"new"fron2er"in"the"legal"space.”" Susie!Lees! General!Counsel!! Allstate!! “Leveraging" data," not" only" that" we" possess" but" that" our" law" firms" have" amassed"over"the"years,"offers"a"plethora" of" un<tapped" opportuni=es—not" simply" to" help" us" forecast" and" manage" legal" expenses," but" also" to" help" our" clients" make"more"informed"business"decisions.”"
152. PRODUCTIZATION AND SCALE
153. HISTORICALLY - LAW HAS BEEN A SERVICE BUSINESS THROUGH WHICH SUBJECT MATTER EXPERTISE IS DELIVERED
154. HOWEVER, IT IS (SLOWLY) BECOMING MUCH MORE …
155. Service The Nature of the Offering Type of Client (Prototypical) (Product + Service) Hybrid Product Fortune 1000 Mid Cap Companies Retail A2J THE MARKET(S) IN LAW
156. The Nature of the Offering THE MARKET(S) in LAW Type of Client (Prototypical)
157. PRODUCTIZATION OF LEGAL KNOWLEDGE
158. PRODUCTIZED KNOWLEDGE IS WHAT THE BIG 4 DO BEST
159. AMONG OTHER THINGS, PRODUCTS HELP WITH THE ‘JUST IN TIME’ IMPERATIVE (LABOR CANNOT SCALE INSTANTANEOUSLY)
160. PRODUCTS ALSO HELP YOU CROSS-SELL SERVICES
161. PRODUCTS MAKE YOU HARD TO GET RID OF …
162. PRODUCT BUSINESS WILL TYPICALLY HAVE A HIGHER MULTIPLE (THAT IS NOT BY ACCIDENT)
164. SO WHY DON’T WE SEE MORE SCALABLE TECHNOLOGY OFFERINGS CREATED BY LAW FIRMS ?
165. TECH IS A CAPITAL INTENSIVE FIELD
166. LAW FIRMS ARE PARTNERSHIPS AND GIVEN RULE 5.4 FIRMS CANNOT SHARE PROFITS WITH #NONLAWYERS WHERE #NONLAWYERS = HUMANS
167. THIS IS THE ORIGINAL SIN OF LAW AS A BUSINESS …
168. LAW FIRMS HAVE (1) NO REAL ASSETS (2) CASH ACCOUNTING THIS HAS LIMITED SCALE
169. THIS HAS MADE ACCESS TO CAPITAL FAR MORE DIFFICULT
171. THESE ARE THE HEADWINDS …
172. HOWEVER, NOTWITHSTANDING EACH OF THESE ENTITIES (AS WELL AS OTHERS) ARE UNDERTAKING SOME SORT OF R+D OPERATION
173. THE RACE IS ON …
180. GIVEN MICRO- INCENTIVES MIGHT NEED TO CREATE A SEPARATE CAPITAL FUNDED R+D OPERATION
184. WITH SOME NOTABLE EXCEPTIONS … THERE ARE SIMILAR SCALABILITY ISSUES IN OTHER SUBMARKETS
186. PROCESS IMPROVEMENT
187. THE INDUSTRIALIZATION OF THE ARTISAN
188. ACROSS THE ECONOMY THERE ARE MANY EFFORTS TO CONVERT AN ARTISANAL PROCESS INTO AN INDUSTRIAL PROCESS
189. WE WANT TO ENSURE THAT WE CAN MAINTAIN THE ARTISAN ELEMENTS THAT DO ADD VALUE
190. WITH RESPECT TO A GIVEN PROCESS THERE IS OFTEN A SIGNIFICANT SPREAD Kim Craig from Seyfarth Lean Consulting
191. RECENTLY MET WITH THE GENERAL COUNSEL OF A LARGE COMPANY WHO OVER PAST DECADE HAD REDUCED THE LEGAL EXPENDITURES OF THE COMPANY BY NEARLY 50% USING THE LEAN METHODOLOGY
192. LEAN, SIX SIGMA AND OTHER PROCESS IMPROVEMENT METHODOLOGIES
193. CAN HELP IMPROVE ALMOST EVERY SUBSECTOR IN LAW
194. THE INTELLECTUAL ORIGINS CAN BE TRACED TO A MUCH EARLIER TIME
195. THE INTELLECTUAL ORIGINS CAN BE TRACED TO A MUCH EARLIER TIME
196. the toyota production system lean ideas lean for enterprises (white collar, etc.)
197. CONVERT HIGH VOLATILITY PROCESS
198. CONVERT HIGH VOLATILITY PROCESS INTO A LOWER VOLATILITY PROCESS
199. WHAT YOU ARE LEFT WITH IS A VERY USEFUL ARTIFACT …
200. THE PROCESS MAP
201. IT CAN AID IN Increasing Response Times Predicting Resource Loads Coordination Across Stakeholders Increasing Margins on Work
202. I AM VERY HAPPY TO HAVE THE CHANCE TO HELP CO-TEACH THIS CLASS
204. SOME ADDITIONAL RESOURCES
208. ARTIFICIAL INTELLIGENCE + LEGAL ANALYTICS
209. THIS IS A TOPIC FOR WHICH I AM OFTEN CALLED UPON TO COMMENT
210. AND FOR WHICH A FULL FLEDGED PRESENTATION COULD BE OFFERED …
211. https://www.slideshare.net/Danielkatz/ artificial-intelligence-and-law-a-primer ACCESS MORE HERE
212. ARTIFICIAL INTELLIGENCE IS A BROAD FIELD
213. LETS LOOK AT THESE SPECIFIC SUBFIELDS
214. data driven AI rules based AI
216. EXPERT SYSTEMS
217. http://www.reinventlawchannel.com/richard-susskind- future-of-artificial-intelligence-and-law RICHARD SUSSKIND DEVELOPED THE FIRST EXPERT SYSTEM IN LAW IN THE 1980’S
218. HERE ARE SOME MORE CONTEMPORARY EXAMPLES …
219. BUT THE BASIC IDEA IS TO ENCODE THE RULES THAT GOVERN A DECISION MAKING PROCESS AND TURN IT INTO SOFTWARE
220. TURBOTAX IS THE MOST COMMERCIALLY SUCCESSFUL EXPERT SYSTEM OF ALL TIME (30+ MILLION USERS)
221. (BUT WITH OTHER INCREASING INTERESTING CAPABILITIES) “DRAG + DROP A.I.” FOR BUILDING LEGAL EXPERT SYSTEMS
222. EXPERT SYSTEMS FOR ACCESS TO JUSTICE #A2J #A2JAUTHOR DECISION TREE A2J AUTHOR www.a2jauthor.org Used over 3.5 Million times 2.1 Million Documents generated IMPACT
223. EXPERT SYSTEMS WILL BECOME CHATBOTS (THUS HELPING BRIDGE THE DATA VS RULES DIVIDE)
225. DATA DRIVEN A.I.
226. THE ALTERNATIVE TO HARD CODING THE RULES IS TO LET DATA DO THE LIFTING …
227. DATA DRIVEN A.I. = MACHINE LEARNING NATURAL LANGUAGE PROCESSING
229. THE ULTIMATE GOAL IS TO PREDICT SOME CLASS OF LEGAL OUTCOMES
230. HERE ARE JUST A FEW USE CASES IN LAW
231. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding)
232. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Contract Terms/Outcomes Data Driven Transactional Work
233. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
234. #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
235. #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work #Predict Regulatory Outcomes Data Driven Lobbying, etc.
237. IN MANY INSTANCES BLENDS OF INTELLIGENCE WILL OUTPERFORM A SINGLE STREAM OF INTELLIGENCE
238. THE PSEUDOCODE OF OUR TIMES …
239. HUMANS + MACHINES HUMANS OR MACHINES >
240. Professor Katz noted …“We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.” May 2nd 2017
242. THE STRUCTURE OF HOW THE WORK IS DONE
243. A.I. Tool Suite (Legal) Data Scientist Subject Matter Expert(s) + +
244. the path (hint: it’s hard to skip steps) Current State Assessment Strategic Planning Tool Selection Solution Design Descriptive Analytics Diagnostics Forecasting
245. Simple Data Models Claim&Type& Statutes& Outcome& Payout& 00001& Employment&–& Single&Plain>ff&& Race& Title&VII& SeCled& $10,000& 00002& Employment&–& Single&Plain>ff&& Race& Title&VII& SeCled& $70,000& 00003& Employment&–& Single&Plain>ff&& Age& Title&VII& & Summary& Judgment& N/A& 00004& Employment&–& Single&Plain>ff&& Gender& Title&VII& & Dismissed& N/A& 00005& Employment&–& Single&Plain>ff&& Age& Title&VII& & Dismissed& N/A& 00006& Employment&–& Single&Plain>ff&& Disability& ADA&Title&III& SeCled& $21,000& Ma4er&No& Ma4er&Type& 00001& Employment&–& Single&Plain>ff&& 00002& Employment&–& Single&Plain>ff&& 00003& Employment&–& Single&Plain>ff&& 00004& Employment&–& Single&Plain>ff&& 00005& Employment&–& Single&Plain>ff&& 00006& Employment&–& Single&Plain>ff&& General Practice-Specific
246. Complex Models Too Lawsuits Docket Record Related Disputes
247. Fill in the gaps $10k Base Damages Claimed Filed in ‘Unfriendly’ Jurisdiction Conservative Appointed Judge Repeat Plaintiff’s Counsel $6.3k + $3.1k (Yes) - $4.4k (No) + $4.2k per case (<= 6 cases) + $0.40 per $1 claimed - $1.6k per case (7 < cases) Claim Type + $2.3k (Disability) - $7k (Age) Matter Management Matter Management Matter Management Outside Source [ ]
248. Practice- Related Data Synthesis and Transformation Interface and Interaction Task Norms Collaboration Culture Coordination PuQng&It&Together&
251. LET ME OFFER JUST A FEW OTHER OBSERVATIONS ON THE STATE OF A.I. AND LAW
252. WE NEED MORE PEER REVIEWED VALIDATION STUDIES (1)
253. AND LESS MARKETING MATERIAL
254. THERE ARE AN INCREASING NUMBER OF RELEVANT ACADEMIC PAPERS (AND IN SOME CASES ASSOCIATED COMPANIES)
259. “…I study choice of law by analyzing the nearly 1,000,000 contracts that have been disclosed to the Securities and Exchange Commission between 1996–2012.”
260. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698 Katz DM, Bommarito MJ II, Blackman J (2017), A General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698.
262. (2) ECONOMICS ASSOCIATED WITH LEGAL A.I. ARE RAPIDLY CHANGING
263. MACHINE LEARNING AS A SERVICE #MLaaS + ENTERPRISE OPEN SOURCE MOVEMENT
264. ARE FUNDAMENTALLY CHANGING THE POSSIBILITY FRONTIER
265. WE ARE GETTING A BROADER MENU
266. WE ARE GETTING MORE STANDARDIZATION
267. I WORK THIS OUT FURTHER HERE …
268. https://www.slideshare.net/Danielkatz/ machine-learning-as-a-service-mlaas-open- source-and-the-future-of-legal-analytics-by- daniel-martin-katz-michael-j-bommarito-ii
269. BUT SUFFICE TO SAY IS NO LONGER THE ONLY GAME IN TOWN
270. Machine Learning as a Service #MLaaS
271. Machine Learning as a Service #MLaaS
272. Machine Learning as a Service #MLaaS
273. Machine Learning as a Service #MLaaS
275. (3) WE WILL GO THE WAY OF THE WORLD AND SEE MORE OPEN SOURCE OFFERINGS IN LEGAL
276. WE NEED MORE #OPENSOURCE IN LAW
277. I AM PRACTICING WHAT I PREACH
279. contraxsuite.com
280. https://github.com/LexPredict/lexpredict-contraxsuite
283. INTELLIGENT MONETIZATION
284. ONE OF THE CHALLENGES THAT LEGAL INNOVATORS HAVE FACED IS PROBLEM SELECTION
285. PERFORMANCE PEDIGREE > HISTORICALLY IN LAW
286. PERFORMANCE IS *NOT* RIGOROUSLY MEASURED …
287. INDEED, ALMOST EVERY TIME WE BENCHMARK EXPERTS THEY ARE NOT NEARLY AS GOOD AS WE THINK
288. EVEN IF YOU HAVE A BETTER MOUSETRAP …
289. IF YOU ARE 5% OR 10%+ BETTER AT SOMETHING
290. YOU NEED TO FIND A CUSTOMER (CLIENT) WHO ACTUALLY CARES
292. SOMETIMES IT IS WAY EASIER TO SELL #LEGALINNOVATION TYPE IDEAS
293. TO OTHER SECTORS IMPACTED BY LEGAL DECISIONS OR LEGAL RISK
294. IN OTHER WORDS, ACROSS THE ENTIRE ECONOMY …
295. THE BOUNDARIES BETWEEN MARKET SECTORS ARE BEGINNING TO EVAPORATE …
296. LEGALTECH REGTECH FINTECH INSURTECH ON THE MARGINS THERE IS NO REAL DIFFERENCE BETWEEN THE FOLLOWING FIELDS -
297. WHICH IS JUST A WAY OF IDENTIFYING THAT ASPECTS OF ‘LAW’ ARE ACTUALLY JUST FINANCE, INSURANCE IN THE FIRST PLACE …
298. LITIGATION FUNDING
299. LITIGATION FUNDING +1568% in 4 years
300. LAW AS INSURANCE
301. EVENT DRIVEN LEGAL TRADING
302. LEGAL SIGNAL PROCESSING
304. https://www.slideshare.net/Danielkatz/fin-legal-tech-laws-future-from- finances-past-some-thoughts-about-the-financialization-of-the-law- professors-daniel-martin-katz-michael-j-bommarito
306. HUMAN CAPITAL + CULTURE
307. THE DEMOGRAPHIC REALITY …
308. WE ARE ABOUT TO HAVE THE LARGEST GENERATIONAL TRANSFER IN HUMAN HISTORY
309. THIS IS A MAJOR CHALLENGE TO EVERY ORGANIZATION
311. ORGANIZATIONS NEED DIFFERENT (BETTER) HUMAN CAPITAL IN ORDER TO SUPPORT ALL OF THESE TRENDS HIGHLIGHTED HEREIN
312. AND THIS SHOULD BE REFLECTED IN THE CONTENT OF LEGAL EDUCATION
313. LIBERAL ARTS LEGAL.EDU
314. POLYTECHNIC LEGAL.EDU LIBERAL ARTS LEGAL.EDU
315. Daniel Martin Katz, The MIT School of Law? A Perspective on Legal Education in the 21st Century, University of Illinois Law Review (2014)
316. I OUTLINE IN THIS PAPER MY VISION FOR WHAT LAW.EDU COULD BE ... (AND I AM NOT ALONE IN THIS VIEW)
320. SO WITH THAT UNDERSTANDING OF THE CURRENT INPUTS, DYNAMICS AND CONSTRAINTS
321. I WOULD LIKE TO LOOK OVER THE #LEGALHORIZON …
322. FROM CHAOS + COMPLEXITY TO REINVENTION
323. BLOCKCHAINLAWCLASS.COM
324. FROM HUNTER GATHERER SOCIETIES TO A HIGHLY COMPLEX GLOBAL ECONOMIC SYSTEM(S)
325. ECONOMIC HISTORY TO DATE CAN BE SEEN AS A QUEST TO SCALE TRUST AND LOWER TRANSACTION COSTS
326. MESOPOTAMIA GREECE ROME MEDIEVAL ERA MERCHANT LAW RENAISSANCE PRE ANALOG ERA THE CORPORATION MARITIME LAW + INSURANCE HUNTER GATHERERS CODE OF HAMMURABI SOME EARLY TRANSACTION ENHANCING INNOVATIONS AND SOCIETIES
327. THERE ARE A VARIETY OF DIFFERENT WAYS TO DIVIDE UP RECENT HISTORY AND PROJECT FORWARD …
328. WE COULD DIVIDE UP THE PAST ~150 YEARS INTO THREE SEPARATE ERAS ANALOG ERA DIGITAL ERA COMPUTATIONAL ERA
329. EACH ERA LAYERS UPON THE PRIOR PERIODS … ANALOG ERA DIGITAL ERA COMPUTATIONAL ERA
330. THE WORLD ECONOMIC FORUM CALLS THIS THE ‘FOURTH INDUSTRIAL REVOLUTION’
332. I WOULD CALL IT THE COMPUTATIONAL AGE …
333. INDEED HERE IS OUR BLOG WHICH HAS BEEN RUN SINCE 2009 (FEEL FREE TO SET THIS AS YOUR HOMEPAGE) HTTPS://COMPUTATIONALLEGALSTUDIES.COM/
334. TELEGRAPH TELEPHONES INTERNET LONG TAIL COMMERCE (MERCHANT LAW TAKE 2) CRYPTO INFRASTRUCTURE ANALOG ERA DIGITAL ERA COMPUTATIONAL ERA INTERNET OF THINGS FIAT CURRENCY EARLY COMPUTERS GLOBALIZATION PERSONAL COMPUTERS ARTIFICIAL INTELLIGENCE AUTOMOBILE CREDIT CARDS CHARGE CARDS
335. SOME OF THE KEY INGREDIENTS OF THE COMPUTATIONAL ERA …
336. CRYPTO INFRASTRUCTURE INTERNET OF THINGS ARTIFICIAL INTELLIGENCE + + ETC. + { }
338. THESE PHASES MAP REASONABLY WELL ON TO WHAT WE HAVE SEEN (AND WILL LIKELY SOON SEE) IN THE FIELD OF LEGAL INNOVATION
339. ANALOG DIGITAL COMPUTATIONAL
340. COMPUTATIONAL Chaos + Complexity Process + Predictability Reinvention + Value Creation ANALOG DIGITAL
342. A FEW EXAMPLES OF THE PATH TO REINVENTION …
343. (1) CONTRACTS / TRANSACTIONS
344. CHAOS + COMPLEXITY
345. CHAOS + COMPLEXITY PROCESS + PREDICTABILITY
346. Contract Management Dashboard Open Contracts for Review 150 11.7% compared to 170 this time last year Contracts by Type NDA’s Master Service Agreements Contracts Reviewed (YTD) 1798 9.3.% compared to 1983 this time last year Input Risk Mitigated $6.4M 30.0% compared to $5M this time last year Average Cycle Time (YTD) 19 days 26.7% compared to 15 days last year Total Value of All Contracts $600.2M 29.6% compared to $500M this time last year Software Licenses Sales Agreements 602 426 246 312 Global Filters from 01/01/2017 Status to 12/31/2017 Practice Matter Type 0 7 13 20 26 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Average Contract Cycle Time Agreement Name Agreement Type Days Since Receipt Division Contracts by Complexity Low Medium High 48% 37% 15% Contracts Awaiting Review NDA Software License NDA Sales Agreement MSA John Smith HR Packet MS Jane Jones HR Packet Widget Agreement Acme Services HR IT HR Sales Team IT 3 29 8 37 56
347. WHILE THIS IS A HELPFUL STEP IT IS NOT REINVENTION
348. AN EXAMPLE OF REINVENTION
349. https://medium.com/aigang-network/the-synergy- between-insurance-and-blockchain-77da2849c211
350. “WEATHER INDEXED INSURANCE IS BASED ON LOCAL WEATHER INDICES. PAYOUTS ARE TRIGGERED BY THE SPECIFIED COMPONENTS OF THE INDEX RATHER THAN CROP YIELDS” https://wrma.org/weather-risk-by-application/agriculture/
351. INSURANCE CONTRACT CONTINGENCY STORED ON BLOCKCHAIN RAW SENSOR DATA (PRIVATE OR PUBLIC) PREPROCESSED AND SERVED UP REGULARLY CHECK FOR CONTINGENCY SATISFACTION IF CONTINGENCY IS SATISFIED INITIATE PAYMENT OR START AUDIT PROTOCOL OR PROCEED TO FINAL HUMAN APPROVAL
352. EARLY EXAMPLES IN LAW-LAW LAND
360. (2) DISCOVERY COMPLIANCE CONVERGENCE
361. THE DISCOVERY + COMPLIANCE CONVERGENCE
366. THE GOAL SHOULD BE PREVENTION
367. BEHAVIORAL PATTERNS AND SIGNALS INTELLIGENCE
368. #DOLESSLAW http://www.reinventlawchannel.com/ron-friedmann-do-less-law/
370. (3) LEGAL RISK MANAGEMENT BECOMES INSURANCE
371. TODAY WE USE LAWYERS AS THE COMPUTERS OF LEGAL RISKS
372. BUT IN THE OTHER APPLICATIONS WE USE UNDERWRITING AND RISK TRANSFER VEHICLES
373. THIS IS JUST ONE EXAMPLE OF HOW WE (PARTIALLY) DELAWYERED A LEGAL PROCESS
374. LAW AS INSURANCE
375. HERE WE ALMOST FULL DELAWYERED THE TRANSACTION
377. THERE IS MORE WHERE THAT CAME FROM …
378. LITIGATION FUNDING
379. LITIGATION FUNDING +1568% in 4 years
380. WHERE LAW IS FINANCE LAW WILL LOOK MORE LIKE FINANCE #PROPTRADING
381. CLAIM MOST OF TODAY’S LEGAL AI BUYERS SELECT ON THE BASIS OF UI/UX NOT ACTUAL PERFORMANCE SIZZLE > STEAK
382. “... law firms will participate in an arms race where they advertise their AI models, and the future of the law business is monetizing bespoke AI models.” http://www.legalexecutiveinstitute.com/iltacon-2018-innovation/
384. (4) PLATFORMS AND THE LEGAL INFO ECONOMY
385. TWO YEARS AGO
386. MICHAEL SKAPINKER FROM THE FT WROTE THE FOLLOWING
387. APRIL 11 2016
388. AMONG OTHER THINGS THE ARTICLE EXPLORED CHANGES IN THE BUSINESS OF LAW
389. INCLUDING THE POSSIBILITY OF AN ‘UBER MOMENT’ FOR LAWYERS
390. THERE HAVE BEEN MANY OTHER ARTICLES ALSO EXPLORING THESE DYNAMICS …
391. AND IT SEEMS THAT THIS IDEA IS …
392. VERY MUCH IN THE ZEITGEIST (IN ONE FORM OR ANOTHER)
393. WHETHER IN LAW OR IN ANOTHER SECTOR OF THE ECONOMY …
394. THE DIGITAL PLATFORM IS ONE OF THE DEFINING FEATURES OF THE POST INTERNET ECONOMY
395. ‘DIGITAL PLATFORMS ARE COMPLICATED MIXTURES OF SOFTWARE, HARDWARE, OPERATIONS, AND NETWORKS’
396. FREELANCE LABOR (UPWORK, ELANCE, TASK RABBIT) SOCIAL NETWORKING (FACEBOOK, LINKEDIN) INTERNET BASED RETAIL (AMAZON, EBAY, ANGIE’S LIST) FINANCIAL / HR FUNCTIONS (WORKDAY, WORKFUSION) TRANSPORTATION (UBER, LYFT, SIDECAR) MOBILE PAYMENT (MAHALA, SQUARE)
397. PLATFORMS AND THE BUSINESS OF LAW
398. APPLYING A SOMEWHAT LOOSE DEFINITION …
399. THE LAW FIRM COULD BE CALLED THE ORIGINAL ‘PLATFORM’ IN LAW
400. ENTREPRENEURIALLY MINDED LAW FIRM LEADERS WERE ABLE TO ASSEMBLE TEAMS OF EXPERTS
401. PARTICULARLY ACROSS THE 20TH CENTURY THE LAW FIRM REPLACED A MUCH MORE DECENTRALIZED FORM OF INDUSTRIAL ORGANIZATION
402. WHILE AN ANALOG FORM OF PLATFORM, THE LAW FIRM ALLOWED FOR A DIFFUSE SET OF EXPERTISE TO BE BUNDLED AND SOLD TO A CLIENT
403. OTHERWISE THE CLIENT HAD TO ORGANIZE THE NECESSARY EXPERTS AND ASSOCIATED LABOR TO SUPPORT THEIR SPECIFIC LEGAL TASKS …
404. THE CLIENTS THEMSELVES ARE WORKING TO DEVELOP PLATFORMS AND ECOSYSTEMS
405. NIKE ALLIANCE
406. GE LAWYER RATING SYSTEM “YELP FOR LAW’ ?
407. ON THE RETAIL SIDE OF THE BUSINESS … LEGALZOOM, CLIO, AVVO ARE ALL MAKING PLATFORM PLAYS
408. THERE ARE SEVERAL VERSIONS OF ‘UBER FOR LAWSUIT FUNDING’
409. AND THERE ARE MANY OTHER VERSIONS OF ‘UBER FOR LAWYERS’
410. ON THE TECH SIDE OF THE LEGAL BUSINESS …
411. THERE IS ALSO MOVEMENT TOWARD PLATFORM OFFERINGS …
415. THIS WILL BE A BIG PART OF THE LAW BUSINESS OF THE 2020’S …
416. (5) SCALABLE JUSTICE AND THE MODRIA OF EVERYTHING
417. WHY DO HAVE TO ‘GO TO COURT’ ?
418. CAN’T WE HAVE MORE SCALABLE VERSION OF THE JUSTICE SYSTEM?
419. EBAY
420. CAN WE USE VASTLY ALTERNATIVE DELIVERY MODELS?
421. FIRST WE WILL GET CHATBOTS …
423. ? Meet Ellie a Virtual Therapist developed at USC THE TRUE ROBOT LAWYER
425. CLOSING ARGUMENT
426. LEGAL INNOVATION IS A 5000 YEAR OLD FIELD
427. BUT AS NOTED EARLIER WE SORT OF STALLED OUT SOMEWHERE ALONG THE WAY …
428. WE ARE GETTING THINGS BACK ON TRACK …
429. I AM BULLISH ON THE FUTURE …
430. NOT BECAUSE I AM AN OPTIMIST
431. BUT RATHER BECAUSE OF TRENDS THAT ARE IN PLAY
432. AND THE OPPORTUNITIES THAT LIE AHEAD
434. TO THE LEGAL INNOVATORS …
435. I SAY — STAY THE COURSE
436. THE LEGAL INNOVATION AGENDA
437. IS GLOBAL
438. IS GLOBAL IS GROWING
439. IS GLOBAL IS GROWING AND IS SLOWLY WINNING
444. ComputationalLegalStudies.com BLOG
445. @ computational
446. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law @ thelawlab.com