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Can Law Librarians Help Law Become More Data Driven ? An Open Question in Need of a Solution — Professor Daniel Martin Katz
1. Can Librarians Help
Law Become More
Data Driven ?
an open question in need of a solution
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
edu | illinois tech - chicago kent law
lab | TheLawLab.com
6. Helped 3.5 million+ Users
Seek Access to Justice
Guided
Interview
Completed
Document
A2J AUTHOR
www.a2jauthor.org
LOGIC
Used over
3.5
Million
times
2.1 Million
Documents generated
IMPACT
11. 3D HD Visualization of Supreme
Court Citation Network
Campaign Contributions and
Legislative Ecosystems
Six Degrees
of
Marbury
v.
Madison
Electronic
World
Treaty
Index
Radial
SCOTUS
Citation
Network
Scientific
Research
25. Can Librarians Help
Law Become More
Data Driven ?
an open question in need of a solution
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
edu | illinois tech - chicago kent law
lab | TheLawLab.com
27. A Reset on Robot LawyersI.
The Rise of #LegalAnalyticsII.
The Killer Use Case(s) - Fin (Legal) Tech)III.
The Infrastructure for #LegalAnalyticsIV.
Building a Legal Data StrategyV.
29. There has been lots of recent
interest in applying
artificial intelligence to law
30.
31.
32.
33.
34.
35.
36.
37.
38. and there is a bit of confusion
as to where we stand today
and where we are headed
39.
40.
41. data driven AI rules based AI
Competing Orientations in
Artificial Intelligence
42. 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
43. we see a decent amount of
rules based AI
in legal industry
74. 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
105. General Counsels as Legal
Procurement Specialists
TyMetrix/ELM -
Using $50 billion+ in Legal
Spend Data to Help GC’s
Look for Arbitrage
Opportunities, Value
Propositions in Hiring Law
Firms
#LegalSpendAnalytics
Quantitative Legal Prediction
118. 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:
149. “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.
W h e n p a t h o l o g i s t s m a d e t wo
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.”
154. (most pundits did not
identify as a serious
candidate him until
mid-January 2017)
Neil Gorsuch was #1
o n o u r F a n t a s y
Platform 12 Days after
Donald Trump was
elected President
(i.e Nov 20)
161. Final Version of #PredictSCOTUS
1816-2015
case accuracy
70.2%
71.9%
justice accuracy
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698
170. 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
171. By the way, you
might ask why does
one care about
marginal improvements
in prediction ?
#Fin(Legal)Tech
172. It is a fair question
because in the
private market …
improvements in
performance must
be linked up to an
actual business
model …
183. 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
184. can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers
185. design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers
190. help price risk /
help reduce information asymmetries
transactional =
191. litigation =
characterize (predict) risk/exposure
shift the expected value of a lawsuit
help price risk /
help reduce information asymmetries
transactional =
192. 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 =
193. 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 =
regulatory =
help identify (predict) the decisions
of regulators / law makers and the risk
associated with various outcomes
199. #Fin(Legal)Tech
application of those ideas and
technologies to a wide range of
law related spheres including
litigation, transactional work
and compliance.
257. "We are increasingly thinking that there's room in
legal tech for a Red Hat in legal — companies that
really focus on development of software by providing
wraparound services, but offer their software open
source," Michael J Bommarito II said.
Michael J. Bommarito
Co-Founder
CEO @ LexPredict
259. Will you resell the software to third parties?
YES%NO%
How much does ContraxSuite cost?
Will you keep derivative work open?Free%
YES%NO%
Free%$12K/year%
50% in trust for open source grants ! 50% for ContraxSuite, LLC!
260. If you are just
buying tools from
vendors you likely
have no alpha
268. D - I - K - W
From Data Strategy to Wisdom
Data$
Informa+on$
Knowledge$
Wisdom$
Direct'record'of'fact,'
signal,'symbol'
Indirect'record'or'
descrip6on$
Interpreta6on'of'
informa6on$
Ac6onable'inference'or'
heuris6c$
Data-Information-Knowledge-Wisdom
Data$
Readings'from'a'temperature'
sensor'in'Tahoe.$
Informa+on$
The'average'temperature'in'the'
month'of'December'is'32.2F.$
Knowledge$
Snow'is'likely'to'accumulate'in'
December.'
Wisdom$
January'is'a'good'month'to'plan'
a'ski'trip'to'Tahoe.$
269. When is Data Valuable?
Even when it’s not
LOW$ HIGH$
HIGH$LOW$
IMPACT'
FREQUENCY'
High3frequency,'high3impact'3'best'use'case'for'data'
• Systema/c'understanding'and'treatment'
• Standardized$reporEng'and'sta/s/cal$treatment'
• PotenEal'for'automaEon'and'predicEon'
Example:'
• Labor'&'Employment'for'a'large'employer'
• Patent'Defense'for'a'large'tech'company'
271. 33!
“Now! we! have! program! managers,!
data! analysts,! business! analysts,!
data! scien9sts,! opera9ons!
managers,!I!mean,!we!have!a!ton!of!
stuff.! That's! the! key! for! me,! is!
thinking! about! the! right! people!
doing! the! right! tasks.! That's! the!
people!part.!And!then!how!they!do!
them,! is! the! process,! and! then,!
automa9ng! parts,! is! kind! of! that!
next,!final!step.!!
"
And$ all$ of$ that$ is$ underpinned$ by$
d a t a ." Y o u$ c a n ' t$ d o$ a n y$
improvements$ unless$ you$ have$
data.$ You$ can't$ automate$ unless$
you$have$good$data.”!
272. 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.”'
276. Five reasons to care
Can you answer these questions?
1. How&many&legal&ma.ers&did&you&handle&last&year?&
2. How&much&poten:al&legal&liability&did&you&handle&last&year?&
3. How&many&hours&per&legal&ma.er&did&you&spend&last&year?&
4. How&many&dollars&per&legal&ma.er&did&you&spend&last&year?&
5. How&much&value&did&you&protect&or&create&last&year?&
277. 47!
Methods for Using (Legal) Data
Historical reporting in legal
Historical analytics in legal
Predictive analytics in legal
279. 49!
Ques&on:!What!
factors!drove!
se3lement!
amounts!last!
quarter?!
• F o r ! l a b o r ! a n d!
employment!disputes,!the!
length! of! employment!
a n d! p r e s e n c e! o f!
retaliatory! or! sexual!
harassment! claims! are!
posi&vely! related! to!
se3lement!amount!
• Disputes! origina&ng! in!
region!X!have!abnormally!
higher! se3lements! than!
expected,! given! their!
facts!
Ques&on:!What!
factors!drove!
legal!expenses!
last!quarter?!
• An! increase! in! ma3ers! in!
highCcost! jurisdic&ons! is!
posi&vely! related! to! total!
legal!expenses!
• A! decrease! in! arbitra&on/
media&on! u&liza&on! is!
posi&vely! related! to! total!
legal!expenses!
Historical analytics in legal
282. 51!
(be able to do so without a herculean effort)
1. !Measure,!monitor,!and!manage!your!resources!and!service!providers.!
!
2. !Using!data!+!experts,!model!and!improve!the!processes!you!execute.!
3. !Allocate!tasks!across!internal/external!resources!and!assess!cost!and!quality.!
4. !Manage!risk!and!be!able!to!formally!characterize!the!risks!avoided.!
5. !Jus&fy)and)explain)performance)to)the)clients.!
Five Goals for Every Legal Organization
284. Y = βo +/- β1 ( X1 ) +/- β2 ( X2 ) +/- β3 ( X3 ) +/- β4 ( X3 ) +/- β5 ( X3 ) + ε
Y = $151 + $15 ( ) + 161 ( ) + 95 ( ) + 34 ( ) +/- β5 ( ) + ε
Per
100
Lawyers
If Tier 1
Market
is True
Partner
Status
is True
Per
10
Years
Practice
Area
285. 1. Define the Parameter Space
3. Select a Model/Method
4. Validate Out of Sample
2. Collect / Normalize Data
(typically using experts)
286. Work with experts to
define relevant variables
that drive outcomes on
some problem
(experts are strong at identifying
relevant variables but have
trouble applying weights)
290. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@
thelawlab.com