The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms

Professor Daniel Martin Katz presentation -- The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms as well as a discussion of SCOTUS and Associated Stock Market Returns aka "Law on the Market"

Transcript

1. Experts, Crowds + Algorithms Applied to SCOTUS daniel martin katz The Three Forms of (Legal) Prediction blog | ComputationalLegalStudies.com edu | illinois tech - chicago kent law lab | TheLawLab.com page | DanielMartinKatz.com
2. TODAY I WOULD LIKE TO BEGIN WITH A PRACTICAL SET OF LEGAL AND LAW RELATED PROBLEMS
3. AS THESE ARE THE CLASSIC QUESTIONS THAT CLIENTS POSE ON AN ONGOING BASIS …
5. HOW MUCH IS THIS GOING TO COST ?
6. HOW LONG WILL THIS MATTER TAKE ?
7. ARE WE GOING TO WIN ? WHAT IS OUR EXPECTED LIABILITY?
9. IF YOU REFLECT UPON IT …
10. LAW-LAW LAND HAS MANY PREDICTION PROBLEMS
11. THE ONLY QUESTION IS ON WHAT BASIS THOSE PREDICTIONS WILL BE OFFERED
13. I WOULD LIKE TO REMIND EVERYONE AT THE OUTSET
14. BEFORE THERE WERE COMPUTERS
15. HUMANS DID ALL OF THE COMPUTING
16. IF YOU REFLECT UPON OUR OWN DECISION MAKING …
17. HERE ARE JUST A FEW COGNITIVE PROCESSES …
18. LOOK FOR PATTERNS WEIGH VARIABLES MAKE CONCEPTUAL LEAPS (using analogical reasoning)
19. ABSTRACTION OF A PROJECTING WEIGHTS INTO A DECISION f( ) dimension 1 dimension 2 dimension 3 . . . . dimension n OUTPUT (Prediction, Decision, etc.) and / or INPUTS
20. PATTERN MATCHING evolutionary biology is an algorithm which privileges good pattern matching
21. PATTERN MATCHING Biology is why you have trouble with Pie Charts
22. PATTERN MATCHING But are very good at interpreting distances
23. ANALOGICAL REASONING Lawyers are particularly good at this task
25. IN ALL OF HUMAN HISTORY …
26. WE HAVE DEVELOPED THREE APPROACHES TO PREDICT OUTCOMES …
27. IN LAW OR MORE BROADLY …
28. THOSE THREE APPROACHES ARE EXPERTS CROWDS ALGORITHMS
30. MORE SIMPLY STATED … WE CAN ASK EXPERT CROWD ALGORITHM TO PREDICT SOMETHING
31. IN LAW, BOTH HISTORICALLY AND STILL TODAY HUMAN DECISION MAKING IS THE DOMINANT FORM OF COMPUTATION
32. IN THE PAST DECADE THERE HAS BEEN AT LEAST SOME EXPLORATION AT ALTERNATIVES
33. LETS CALL IT THE ROBOT LAWYER THESIS …?
35. OR MORE REALISTICALLY IT SHOULD BE CALLLED THE USE OF ALGORITHMS TO INTERROGATE PATTERNS IN DATA
36. REMEMBER AN ‘ALGORITHM’ IS JUST A RECIPE FOR A MACHINE TO COMPLETE A TASK
37. A.I.
38. ARTIFICIAL INTELLIGENCE
39. THIS IS A TOPIC FOR WHICH I AM OFTEN CALLED UPON TO COMMENT
40. AND FOR WHICH A FULL FLEDGED PRESENTATION COULD BE OFFERED …
41. https://www.slideshare.net/Danielkatz/ artificial-intelligence-and-law-a-primer ACCESS MORE HERE
42. BUT AT A HIGH LEVEL LET ME JUST SAY …
43. ARTIFICIAL INTELLIGENCE IS A BROAD FIELD
44. LETS LOOK AT THESE SPECIFIC SUBFIELDS
45. data driven AI rules based AI
47. DATA DRIVEN A.I.
48. DATA DRIVEN A.I. = MACHINE LEARNING NATURAL LANGUAGE PROCESSING
50. THE ULTIMATE GOAL IS TO PREDICT SOME CLASS OF LEGAL OUTCOMES
51. HERE ARE JUST A FEW USE CASES IN LAW
52. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding)
53. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Contract Terms/Outcomes Data Driven Transactional Work
54. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
55. #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
56. #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.
58. TODAY I WANT TO TAKE THESE AND OTHER IDEAS AND DISCUSS THEIR APPLICATION TO SCOTUS PREDICTION
59. SCOTUS PREDICTION IS A PASTIME
60. EVERY YEAR, LAW REVIEWS, MAGAZINE AND N E W S PA P E R A R T I C L E S , T E L E V I S I O N A N D RADIO TIME, CONFERENCE PANELS, BLOG P O S T S , A N D T W E E T S A R E D E V O T E D T O QUESTIONS SUCH AS: H O W W I L L T H E C O U R T R U L E I N T H I S PARTICULAR CASE? IN THE DIRECTION OF WHICH PARTY WILL AN INDIVIDUAL JUSTICE VOTE?
61. JUDICIAL PREDICTION IS ALSO THE LAW + POLITICAL SCIENCE GRAIL QUEST
62. YOU COULD START WITH HOLMES AND THE LEGAL REALISTS BUT THESE WERE *NOT* REALLY SCIENTIFIC EFFORTS
63. FRED KORT, PREDICTING SUPREME COURT DECISIONS MATHEMATICALLY: A QUANTITATIVE ANALYSIS OF THE “RIGHT TO COUNSEL” CASES, 51 AMER. POL. SCI. REV. 1 (1957). 1957 S. SIDNEY ULMER, QUANTITATIVE ANALYSIS OF JUDICIAL PROCESSES: SOME PRACTICAL AND THEORETICAL APPLICATIONS, 28 LAW & CONTEMP. PROBS. 164 (1963). 1963 A CO U P L E O F E A R LY E F F O RT S
64. COMPUTERWORLD JULY 1971 PROGRAM WRITTEN IN FORTRAN (THE 91% PREDICTION MARK WAS LIMITED TO CERTAIN CASES) HAROLD SPAETH
65. JEFFREY A. SEGAL, PREDICTING S U P R E M E C O U R T C A S E S PROBABILISTICALLY: THE SEARCH AND SEIZURE CASES, 1962-1981, 78 AMERICAN POLITICAL SCIENCE REVIEW 891 (1984) 1984 A N I M P O RTA N T L AT E R E F F O RT
66. Columbia Law Review (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: B U T T H I S WA S T H E PA P E R T H AT I N S P I R E D O U R E F F O RT S 2004
68. EXPERTS
69. 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:
70. experts
71. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts From the 68 Included Cases for the 2002-2003 Supreme Court Term
72. these experts probably overfit
73. they fit to the noise and not the signal
75. if this were finance this would be trading worse than S&P500
76. #NoiseTrading
77. #BuffetChallenge
78. #BuffetChallenge
79. like many other forms human endeavor law is full of 
 noise predictors …
80. from a pure forecasting standpoint
81. the best known SCOTUS predictor is
83. the law version of superforecasting
85. CROWDS
86. not enough crowd based decision making in institutions
88. “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.”
91. FA N TA SY S COT U S I S A S CO O L A S I T S O U N D S
92. FA N TA SY S COT U S WA S F O U N D E D B Y J O S H B L AC K M A N I N 2 0 0 9
93. P R I Z E S A N D S P O N S O R S H I P H AV E VA R I E D B U T T H E R E H AV E B E E N T E N S O F T H O U S A N D S O F $ $ A N D P R I Z E S D I ST R I B U T E D OV E R T H E Y E A R S
96. U S E R S C R E AT E A LO G I N A N D ACC E S S T H E S I T E
97. O N A CA S E B Y CA S E B A S I S , U S E R S CA N E N T E R T H E I R R E S P E C T I V E P R E D I C T I O N S
98. U S E R S A R E F R E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
99. https://fivethirtyeight.com/features/ obamacares-chances-of-survival-are-looking- better-and-better/ ( S O M E T I M E S I N I N T E R E ST I N G WAY S A S S H O W N A B OV E ) U S E R S A R E F R E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
100. F O R E AC H CA S E , W E A R E A B L E TO T R AC K TO P E R F O R M A N C E O F P L AY E R S A N D CO M PA R E I T TO T H E O U TCO M E O F T H E CA S E S
101. We can generate Crowd Sourced Predictions
103. S U M M A R Y STAT I ST I C S
104. 6 S COT U S T E R M S
105. 4 2 5 L I ST E D CA S E S 6 S COT U S T E R M S
106. 7 2 8 4 U N I Q U E PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 S COT U S T E R M S
107. 7 2 8 4 U N I Q U E PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 3 6 8 5 9 P R E D I C T I O N S 6 S COT U S T E R M S
108. W E H AV E A S I G N I F I CA N T A M O U N T O F P L AY E R T U R N OV E R WO R K I N G W I T H R E A L DATA ( ~ 3 % O F M A X PA RT I C I PAT I O N )
109. S O M E T I M E S F O L K S C H A N G E T H E I R VOT E S 6 3 6 8 5 9 5 4 5 8 4 5 F I N A L P R E D I C T I O N S OV E R A L L P R E D I C T I O N S WO R K I N G W I T H R E A L DATA
110. T H E N U M B E R O F P L AY E R S H A S D E C L I N E D B U T T H E E N G AG E M E N T R AT E H A S I N C R E A S E D WO R K I N G W I T H R E A L DATA
111. W E B E L I E V E T H I S I S * N OT * R E A L LY S U RV I VO R S H I P B I A S B U T R AT H E R A R E V E L AT I O N M E C H A N I S M ( I . E . YO U ST I C K A R O U N D I F T H I N K YO U A R E G O O D AT T H E U N D E R LY I N G TA S K )
112. S U M M A R Y DATA
114. CROWD MODELING PRINCIPLES
115. C R O W D S O U R C I N G C R O W D S O U R C I N G D O E S * N OT * R E F E R TO A S P E C I F I C T E C H N I Q U E O R A LG O R I T H M
116. C R O W D S O U R C I N G G E N E R A L LY R E F E R S TO A P R O C E S S O F AG G R E G AT I O N A N D / O R S E G M E N TAT I O N O F I N F O R M AT I O N S I G N A L S
117. VA R I O U S S I G N A L TY P E S T H E I N P U T S I G N A L S CA N A S S U M E M A N Y D I F F E R E N T F O R M S I N C LU D I N G F R O M M O D E L S O R I N D I V I D UA L S O R S E N S O R S ( O R S O M E CA S E S E V E N OT H E R C R O W D S )
118. CROWD OF INDIVIDUALS The most well know approach involves extracting ‘wisdom’ from crowds where crowds are built from individual people
119. CROWD OF SENSORS Note crowds need not be composed of humans but could be networked IT systems Decentralized Distributed Ledgers -or- Oracles -or- IOT sensors with Crowdsourcing Validation #Blockchain #InternetofThings #Crypto
120. #CRYPTO CROWD Thanks to Team Augur for the Shoutout
121. Random Forest Model Breiman, L.(2001). Random forests. Machine learning, 45(1), 5-32. Grow a set of differentiated trees through bagging and random substrates (predict using a consensus mechanism) C R O W D O F M O D E L S
123. A S W E R E V I E W E D T H E C R O W D S O U R C I N G L I T E R AT U R E …
124. W E O B S E RV E D T H AT I T WA S D I F F I C U LT TO A P P LY T H E P R I N C I P L E S TO C R O W D S S U C H A S O U R S
125. C R O W D S O U R C I N G I S ‘ U N D I S C I P L I N E D ZO O O F M O D E L S ’ J E S S I CA F L AC K P R O F E S S O R S A N TA F E I N ST I T U T E D E C . 2 7 , 2 0 1 7 ( V I A T W I T T E R )
126. W E AG R E E … A N D T H U S I N T H E PA P E R W E J U ST STA RT E D OV E R …
127. A N D AT T E M P T E D TO B U I L D C R O W D S F R O M F I R ST P R I N C I P L E S …
129. CROWD CONSTRUCTION FRAMEWORK
130. W E O U T L I N E A G E N E R A L F R A M E WO R K F O R CO N ST R U C T I N G C R O W D S F R O M F I R ST P R I N C I P L E S
131. I N T H E C L A S S I C CO N D O R C E T J U R Y S E T T I N G , M O D E L S TY P I CA L LY U S E P R E D I C T I O N S F R O M A L L PA RT I C I PA N T S
132. H O W E V E R , M O D E L S CA N A L S O TA K E I N TO ACCO U N T I N F O R M AT I O N ( S I G N A L S ) F R O M S O M E S U B S E T O F PA RT I C I PA N T S ( D E F I N E D U S I N G E I T H E R I N C LU S I O N R U L E S O R E XC LU S I O N R U L E S )
134. E X P E R I E N C E P E R F O R M A N C E R A N K STAT I ST I CA L T H R E S H O L D I N G W E I G H T I N G C R O W D CO N ST R U C T I O N R U L E S
135. C R O W D CO N ST R U C T I O N R U L E S T H E I N T E R AC T I O N O F T H E S E R U L E S Y I E L D S * N OT * A N I N D I V I D UA L M O D E L B U T R AT H E R A M O D E L S PAC E
136. T H E M O D E L S PAC E M O D E L S PAC E F E AT U R E S 2 7 7 , 2 0 1 P OT E N T I A L M O D E L S
137. T H E M O D E L S PAC E 1 + ( 2 8 · 9 9 · 1 0 0 ) = 2 7 7 2 0 1 F I R ST T E R M I S T H E S I M P L E ST C R O W D S O U R C I N G M O D E L W I T H N O S U B S E T O R W E I G H T I N G R U L E S S E CO N D T E R M CO R R E S P O N D S TO 2 8 M O D E L S ( CO M B I N AT I O N O F P E R F O R M A N C E T H R E S H O L D / W E I G H T I N G R U L E S ) F O R E AC H CO M B I N AT I O N O F 9 9 R A N K A N D 1 0 0 E X P E R I E N C E T H R E S H O L D S
139. MODEL TESTING & RESULTS
140. W E S I M U L AT E T H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S M O D E L ( S ) ACC U R ACY
141. A LT H O U G H I T I S A L A R G E M O D E L S PAC E W E D O H I G H L I G H T T H E P E R F O R M A N C E O F F O U R E X A M P L E M O D E L S ( A N D T H E N U L L M O D E L )
142. B A S E L I N E A LWAY S G U E S S R E V E R S E N U L L M O D E L
143. M O D E L 1 A L L C R O W D S I M P L E M A J O R I TY
144. M O D E L 2 F O L LO W T H E L E A D E R W I T H N O T H R E S H O L D I N G
145. M O D E L 3 F O L LO W T H E L E A D E R W I T H E X P E R I E N C E T H R E S H O L D I N G ( X P = 5 )
146. M O D E L 4 M A X I M U M ACC U R ACY ( T H E R E A R E AC T UA L LY S E V E R A L M O D E L CO N F I G U R AT I O N S W H I C H O F F E R R O U G H LY E Q U I VA L E N T P E R F O R M A N C E ) E X P E R I E N C E T H R E S H O L D O F 5 C R O W D S I Z E I S CA P P E D AT 2 2 E X P O N E N T I A L W E I G H T W I T H A L P H A O F 0 . 1
147. CASE LEVEL CUMULATIVE ACCURACY
148. JUSTICE LEVEL CUMULATIVE ACCURACY
149. DISTRIBUTION OF JUSTICE LEVEL MODEL ACCURACY
151. W E S I M U L AT E T H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S R O B U ST N E S S O F P E R F O R M A N C E
152. ROBUSTNESS VISUALIZED T H I S I S A L L R E L AT I V E TO T H E N U L L M O D E L ( O F A LWAY S G U E S S R E V E R S E )
153. ROBUSTNESS VISUALIZED T H E CO N TO U R P LOT F L AT T E N S T H E D I M E N S I O N A L I TY O F T H E S PAC E ( E AC H C E L L I S T H E AV E R AG E M O D E L P E R F O R M A N C E OV E R A L L OT H E R M O D E L PA R A M E T E R AT E AC H E X P E R I E N C E , R A N K CO M B O )
154. ROBUSTNESS VISUALIZED J U ST I C E L E V E L CA S E L E V E L
155. T H E K E Y I D E A
156. N OT A L L M E M B E R S O F C R O W D A R E M A D E E Q UA L
157. W E M A I N TA I N A ‘ S U P E R C R O W D ’ W H I C H I S T H E TO P N O F P R E D I C TO R S U P TO T I M E T- 1
158. the ‘supercrowd’ outperforms the overall crowd (and even the best single player)
159. H T T P S : / / A R X I V. O RG / A B S / 1712 . 0 3 84 6 H T T P S : / / PA P E R S . S S R N . C O M / S O L 3 / PA P E R S . C F M ? A B S T R AC T _ I D = 3 0 8 5710
161. ALGORITHMS
162. 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.
163. Our algorithm is a special version of random forest (time evolving) http://journals.plos.org/ plosone/article?id=10.1371/ journal.pone.0174698 available at RESEARCH ARTICLE A general approach for predicting the behavior of the Supreme Court of the United States Daniel Martin Katz1,2*, Michael J. Bommarito II1,2, Josh Blackman3 1 Illinois Tech - Chicago-Kent College of Law, Chicago, IL, United States of America, 2 CodeX - The Stanford Center for Legal Informatics, Stanford, CA, United States of America, 3 South Texas College of Law Houston, Houston, TX, United States of America * dkatz3@kentlaw.iit.edu Abstract Building on developments in machine learning and prior work in the science of judicial pre- diction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the jus- tice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications. Introduction As the leaves begin to fall each October, the first Monday marks the beginning of another term for the Supreme Court of the United States. Each term brings with it a series of challenging, important cases that cover legal questions as diverse as tax law, freedom of speech, patent law, administrative law, equal protection, and environmental law. In many instances, the Court’s decisions are meaningful not just for the litigants per se, but for society as a whole. Unsurprisingly, predicting the behavior of the Court is one of the great pastimes for legal and political observers. Every year, newspapers, television and radio pundits, academic jour- nals, law reviews, magazines, blogs, and tweets predict how the Court will rule in a particular case. Will the Justices vote based on the political preferences of the President who appointed them or form a coalition along other dimensions? Will the Court counter expectations with an unexpected ruling? PLOS ONE | https://doi.org/10.1371/journal.pone.0174698 April 12, 2017 1 / 18 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: 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. https://doi. org/10.1371/journal.pone.0174698 Editor: Luı ´s A. Nunes Amaral, Northwestern University, UNITED STATES Received: January 17, 2017 Accepted: March 13, 2017 Published: April 12, 2017 Copyright: © 2017 Katz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data and replication code are available on Github at the following URL: https://github.com/mjbommar/scotus-predict-v2/. Funding: The author(s) received no specific funding for this work. Competing interests: All Authors are Members of a LexPredict, LLC which provides consulting services to various legal industry stakeholders. We received no financial contributions from LexPredict or anyone else for this paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
164. T H E S O U R C E CO D E F O R O U R A LG O PA P E R I S AVA I L A B L E O N
166. W E CA L L O U R A LG O PA P E R A ‘ G E N E R A L’ A P P R OAC H
167. B E CAU S E W E A R E N OT I N T E R E ST E D I N A LO CA L LY T U N E D M O D E L B U T R AT H E R A M O D E L T H AT CA N ‘ STA N D T H E T E ST O F T I M E ’
168. G E N E R A L S COT U S P R E D I C T I O N 243,882 28,009 Case Outcomes Justice Votes 1816-2015
169. G E N E R A L S COT U S P R E D I C T I O N 70.2% accuracy at the case outcome level 71.9% at the justice vote level 1816-2015
170. W E P R E D I C T * N OT * A S I N G L E Y E A R B U T R AT H E R ~ 2 0 0 Y E A R S ( 1 8 1 6 - 2 0 1 5 ) O U T O F S A M P L E
171. N O W I T I S WO RT H N OT I N G T H AT P R E D I C T I O N O R I E N T E D PA P E R S A R E C U R R E N T LY S W I M M I N G AG A I N ST M A I N ST R E A M S O C I A L S C I E N C E ( A N D L AW )
172. CAU S A L I N F E R E N C E I S T H E H A L L M A R K O F M O ST Q UA N T O R I E N T E D L AW + S O C I A L S C I E N C E S C H O L A R S H I P
173. I T I S B E ST S U I T E D TO P O L I CY E VA LUAT I O N ( S U C H A S D O E S T H I S PA RT I C U L A R P O L I CY I N T E RV E N T I O N AC H I E V E I T S STAT E D O B J E C T I V E S )
174. O R I N STA N C E S W H E R E E STA B L I S H I N G L I N K S B E T W E E N CAU S E A N D E F F E C T A R E C R I T I CA L
175. B U T T H E R E I S A N A LT E R N AT I V E PA R A D I G M # P R E D I C T I O N
176. M AC H I N E L E A R N I N G P R E D I C T I V E A N A LY T I C S ‘ I N V E R S E ’ P R O B L E M B -S C H O O L CO M P S C I P H Y S I C S P R E D I C T I O N
177. Andrew D. Martin, Kevin M. Quinn, Theodore W. Ruger & Pauline T. Kim, Competing Approaches to Predicting Supreme Court Decision Making, 2 Perspectives on Politics 761 (2004). “the best test of an explanatory theory is its ability to predict future events. To the extent that scholars in both disciplines (social science and law) seek to explain court behavior, they ought to test their theories not only against cases already decided, but against future outcomes as well.”
178. https://www.computationallegalstudies.com/2017/08/28/legal-analytics-versus-empirical- legal-studies-causal-inference-vs-prediction/ https://www.slideshare.net/Danielkatz/legal-analytics-versus-empirical- legal-studies-or-causal-inference-vs-prediction-redux M O R E O N T H AT TO P I C H E R E
180. T H E R E I S G R O W I N G I N T E R E ST I N T H E P R E D I C T I O N C E N T R I C A P P R OAC H
181. “There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown …. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.” Leo Breiman, Statistical modeling: The two cultures (with comments and a rejoinder by the author), 16 Statistical Science 199 (2001) Note: Leo Breiman Invented Random Forests
187. Susan Athey, The Impact of Machine Learning on Economics http://www.nber.org/chapters/c14009.pdf
188. 3 5 5 S C I E N C E 6 3 2 4 3 F E B R UA R Y 2 0 1 7 S P E C I A L I S S U E O N P R E D I C T I O N
190. EXPERTS, CROWDS, ALGORITHMS
191. P R E D I C T I O N I S N OT N E C E S S A R I LY # M L A LO N E B U T R AT H E R S O M E E N S E M B L E O F E X P E RT S , C R O W D S + A LG O R I T H M S
192. http://www.sciencemag.org/news/ 2017/05/artificial-intelligence-prevails- predicting-supreme-court-decisions Professor Katz noted that in the long term …“We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.” May 2nd 2017
193. IN MANY INSTANCES BLENDS OF INTELLIGENCE WILL OUTPERFORM A SINGLE STREAM OF INTELLIGENCE
194. THE PSEUDOCODE OF OUR TIMES …
195. HUMANS + MACHINES HUMANS OR MACHINES >
196. crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ENSEMBLE MODEL we can use machine learning methods and metadata such as case topic, lower court as well as crowd metadata to ‘learn’ the conditional weights to apply to the input signals
198. THERE ARE LOTS OF INTERESTING APPLICATIONS ONCE YOU CAN PREDICT SOMETHING …
199. AND BY PREDICT … I AM TALKING ABOUT PREDICTION AT THE PORTFOLIO LEVEL …
200. I HAVE BEEN VERY INTERESTED IN THE OVERLAP BETWEEN LEGAL TECH FIN TECH
201. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law
202. https://computationallegalstudies.com/2017/10/24/10-legal-tech-lessons-dollars-doughnuts-fin-legal-tech-via-aba-journal/
203. nearly 75+ videos and counting TheLawLabChannel.com
205. Law on the Market
206. When we would present this work on #SCOTUS Prediction folks would ask us “why do I care about marginal improvements in prediction ? “
207. Well at a very minimum — if you could predict the cases you could perhaps trade on them in the relevant securities market …
208. In other words, given our ability to offer forecasts of judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
209. http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
210. We call this idea “Law on the Market” LOTM
211. A Motivating Example Myriad Genetics NASDAQ: MYGN Market Cap of ~$3 billion+
212. Myraid Genetics “Myriad employs a number of proprietary technologies that permit doctors and patients to understand the genetic basis of human disease and the role that genes play in the onset, progression and treatment of disease.”
213. Myraid Genetics “Myriad was the subject of scrutiny after it became involved in a lengthy lawsuit over its controversial patenting practices” which including the patenting of human gene sequences ....
215. June 13, 2013 Supreme Court Offers this Decision ~10:05am
216. Initial Media Reports and Initial Trading 11:48am
217. Initial Media Reports Early Afternoon “In early afternoon trading Thursday, Myriad shares were up 5.4 percent, or $2.36, at $35.73.”
218. Final Media Reports
219. Final Media Reports
220. 9:30am Open
221. 10:00am SCOTUS
222. 11:05am MYGN Trades UP
223. 2:15pm MYGN is Off its Daily Peak but still up
224. Day 1 Close MYGN is Off Nearly 10% from Open and 20% from Daily High
225. Day 2 the Sell Off Continues
226. A Good Time to Buy an Option :)
229. SO THESE EXAMPLES REPRESENT A FORM OF EXISTENCE PROOF …
230. BUT PERHAPS THEY ARE RARE AND ANACHRONISTIC CASES …?
232. ONE OBVIOUS CHALLENGE IS THE PROSPECT THAT THIS INFORMATION IS ALREADY INCORPORATED INTO THE PRICE OF THE RELEVANT SECURITY #EfficientMarketHypothesis #Fama #EMH
233. IN ALLIED FIELDS OF HUMAN ENDEAVOR, THERE ARE FAIRLY RAPID MARKET RESPONSES TO CHANGES IN THE INFORMATION ENVIRONMENT
234. THIS ALL PRESUPPOSES A RIGOROUS INFORMATION AND MODELING ENVIRONMENT — THAT IS GENERALLY LACKING IN QUESTIONS OF LEGAL PREDICTION #QuantitativeLegalPrediction #LegalAnalytics #FinLegalTech
236. BUT PERHAPS THEY ARE RARE AND ANACHRONISTIC CASES …?
238. Theoretical + Empirical Questions
239. Market Incorporation Hypothesis Are judicial decisions already reflected in the share price ? (If this were true - we would rarely see market move post decision)
240. How General Are These Specific Examples? Theoretical + Empirical Questions (In other words, is this a general phenomenon ?)
241. What is the nature of the signal incorporation environment ? (In other words, what are the dynamics associated with does the market response ?) Theoretical + Empirical Questions
243. METHODS
244. (1) Coding / PreProcessing (2) Candidate LOTM Events (3) Formal Evaluation Using CAPM (market model of returns) (4) Evaluate Speed of Incorporation and Related Informational Dynamics
245. (1) Coding / PreProcessing We reviewed and coded 1,363 total cases decided over the period in questions. We asked a simple question - could this case plausibly impact a publicly traded security ?
246. All Data & Code is Available Here^ ^Other than the WRDS Data which is *not* open source but can be obtained from Wharton https://github.com/mjbommar/law-on-the-market https://wrds-web.wharton.upenn.edu/wrds/
247. (3) Formal Evaluation Using CAPM (market model of returns)
248. Abnormal Returns Common approach is to use index as baseline and seek to identify statistically significant deviations from that baseline We want to isolate the effect of the event from other general market movements
249. This Paper Leverages 5 Minute Data -5 Days, +5 Days A KEY POINT much higher frequency than most papers in literature
251. SOME RESULTS
252. Summary Results
253. Market Cap
254. Some Additional Cases
255. (4) Evaluate Speed of Incorporation and Related Informational Dynamics
256. Speed of Information Incorporation
257. This is very slow … Perhaps the real action is in the options market ?
258. In conclusion, we believe that this research raises many questions and justifies a range of future work in the area
259. Future Work Real Trading Strategy Analysis Other Classes of Litigation Events 8k’s and Docket Arbitrage Higher and Lower Order Analysis Litigation Reserves, etc.
260. Litigation Funding
261. Other Classes of Litigation Events Litigation Funding and Reserves Litigation Reserves
262. FT Big Read Feb 7, 2019
264. TODAY I HAVE GIVEN YOU AN OVERVIEW OF THREE APPROACHES TO LEGAL PREDICTION
265. AND DEMONSTRATED THEIR APPLICATION TO ONE PARTICULAR PROBLEM (#SCOTUS)
266. HOWEVER, I HOPE IT IS CLEAR THAT THESE TECHNIQUES CAN BE APPLIED MORE BROADLY
267. TO A WIDE RANGE OF PREDICTION PROBLEMS ACROSS THE LEGAL INDUSTRY AND BEYOND …
269. Daniel Martin Katz @ computational computationallegalstudies.com danielmartinkatz.com illinois tech - chicago kent college of law @ thelawlab.com