Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Together - Professor Daniel Martin Katz

1. measure twice, cut once
Solving the Legal Profession's Biggest Problems Together
daniel martin katz
blog | ComputationalLegalStudies.com
corp | LexPredict.com
page | DanielMartinKatz.com
edu | chicago kent college of law

2. collectively this industry
faces some real challenges …

3. challenges that have been
well documented …

4. But I believe that
we are going to persist

5. indeed, I think we can thrive…

6. my resolution is
not related to the notion that
the world owes us anything

7. But rather it is related
to this group and groups like it

8. working together help solve
the Legal Profession's
Biggest Problems

9. But we have
real work to do

10. So today I would like to
focus my comments …

11. on how we together might build
a more perfect supply chain

12. financially rigorous
measurement of the value
proposition associated
with various legal services
centered upon

13. moving items from the
‘art’ column and to the
‘science’ column …

15. so today
a presentation
in five parts …

16. the economics of law
the industrialization
of the artisan
toward an enterprise
data strategy in legal
fin (legal) tech
Legal Analytics +
#MLaaS
part 1:
part 2:
part 3:
part 4:
part 5:

18. the economics of law
Part One

19. I would like to take a step back

20. When we look at the industry…

21. under alternative conditions
its structure might have differed

22. there are fundamental economic
principles which have yielded

23. the current industrial organization
of the legal industry

25. why do we have lawyers?
(in other words what do they solve for …)

26. help navigate complexity
manage enterprise (legal) risk
+

27. Social, Economic and
Political Complexity

28. Which for our
purposes manifests
in legal complexity

29. In the face of ever
growing legal complexity
we have applied greater
and greater numbers of
human experts to solve
the underlying problem

30. Lawyer as Complexity Engineer

31. complexity keeps growing ...

32. and so has total expenditures
on legal services

33. Legal Expenditures as
a function of GDP
(some disagreement between these
plots but they project a similar trend)

34. Cobb Douglas is
the traditional way
to describe a
production
function

35. LaborCapital
Cobb Douglas is
the traditional way
to describe a
production
function

36. Capital
Cobb Douglas is
the traditional way
to describe a
production
function
Labor
historically we have
turned this dial

37. Where is are large
scale complexity filled
opportunities in law?

38. Where is are large
scale complexity filled
opportunities in law?
BANKS AS CLIENTS
(and TECH AS CLIENTS)

40. manage enterprise (legal) risk

41. Three Types of Lawyers
(as described by paul lippe)

42. 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

43. can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers

44. design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers

45. when it comes to risk …
one challenge with identifying
their value proposition
is the counterfactual

47. why do we have law firms?
(in other words what do they solve for …)

48. The Enterprise
Consumer (client)
always faces the
decision of
make versus buy

49. Solving the Peak
Load Problem

50. Provide High Value but
Rarely Utilized or Hard
to Acquire Expertise

51. Without sufficient volume
it is not sensible to make
but rather to buy …

53. lawyers and law firms
provide substantial value

54. BUT the
problem of
agency costs
always looms

55. an economic concept concerning the fee to a
“principal” (an organization, person or group of
persons), when the principal chooses or hires an
"agent" to act on its behalf.
Because the two parties have different interests
and the agent has more information, the principal
cannot directly ensure that its agent is always
acting in its (the principal's) best interests.

56. agency costs
turn
allies (friends)
into
frenemies

57. frenemy

58. agency costs muddy
the inside vs outside counsel
relationship

60. (Partially) solving the industry’s
requires engineering around
these agency issues?

61. Measurement,
Standards,
Metrics,
#DATA

62. this allows for a partial
solution to the problem

63. Many at this conference
and at conferences like this
are working on the problem

65. there are successes from other
sectors of the economy from
whom we can learn

66. where the supply chain thrived
in a metrics heavy environment…

69. the industrialization
of the artisan
Part Two

70. across the economy there are
many effort to convert an
artisanal process into an
industrial process

72. as we move toward a more metrics
centered field we want to ensure
that we can maintain the artisan
elements that DO ADD VALUE

74. My favorite non-legal example
Sal Consiglio - (Sally’s APizza) Domino’s Ad Circa 1990’s
ARTISAN INDUSTRIAL

75. the industrialization of the artisan

77. lets focus on these two because
for now this is where
process improvement and
data should be directed

79. so …
remaining mindful
of the lawyer and law firm
value proposition

80. but with an eye toward reducing
the agency costs issues …

81. every organization in law
needs a data strategy

83. Toward an Enterprise
Data Strategy in Legal
Part Three

84. every organization in law
needs a data strategy

85. Capture, Clean, Regularize Data
to support a range of tasks

86. Deploy Data for Specific
Enterprise Applications
Develop a
data roadmap

88. We want data to help support
two major things …

89. We want data to help support
two major things …
substantive
predictions
procedural
predictions

91. there is often a significant
spread between
Kim Craig @ Seyfarth Lean Consulting
Chicago Legal Technology + Innovation MEETUP

92. so think of the process map as a
first order estimation of your
actual processes

93. rich / granular data
can help illuminate
the actual processes
present in various
(legal) organizations

94. for each node in a process
we want to be able to render
a prediction about things
such as duration,cost, etc.

95. each unit of time linked + logged
to a node on the process map

96. if there is not a node than it can
be added
and thus the map becomes more
reflective of reality

97. just be careful not to create a
#ridiculogram

98. with predictions about
individual nodes

99. we can then sum to generate
predictions about the
distributional moments of an
overall matter (or phase)
(i.e. mean, variance, skewness, kurtosis)

100. this matter should take …
between 9-15 months
in 85% of the similar matters
(what about the long tail?)

101. this matter will cost…
most common range 275k - 345k
but the second mode is 555k - 625k
(and that second
mode typically is
achieved when the
following factors are
present … )

103. #LegalData Collaboration Point

104. transparency
as the relationship glue
(and trust that comes with transparency)

105. how could you facilitate
data sharing / transparency?

106. sharing data between
customer and client
(real time, no filter?)

107. are law firms
AND
corporate counsel
willing to engage in a
two way data exchange ?

109. We want data to help support
two major things …
substantive
predictions
procedural
predictions

110. Here are just a subset of the
substantive predictions we are
trying to undertake in legal …

112. #Predict Relevant Documents
Data Driven EDiscovery/Due Diligence
(Predictive Coding)
SUBSTANTIVE LEGAL PREDICTIONS

113. #Predict Relevant Documents
Data Driven EDiscovery/Due Diligence
(Predictive Coding)
#Predict Contract Terms/Outcomes
Data Driven Transactional Work
SUBSTANTIVE LEGAL PREDICTIONS

114. #Predict Relevant Documents
Data Driven EDiscovery/Due Diligence
(Predictive Coding)
#Predict Rouge Behavior
Data Driven Compliance
#Predict Contract Terms/Outcomes
Data Driven Transactional Work
SUBSTANTIVE LEGAL PREDICTIONS

115. #Predict Relevant Documents
#Predict Case Outcomes
Data Driven Legal Underwriting
Data Driven EDiscovery/Due Diligence
(Predictive Coding)
#Predict Rouge Behavior
Data Driven Compliance
#Predict Contract Terms/Outcomes
Data Driven Transactional Work
SUBSTANTIVE LEGAL PREDICTIONS

117. Data Driven Transactional Work

118. Meet Bob

119. Meet Bob
lawyer on
a major
corporate
transaction

120. Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
lawyer on
a major
corporate
transaction

121. 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

122. how much value is created
by these modifications?
how much delay
will be introduced?
vs.

123. Need a better understanding
of the actual drivers of risk

124. Being able to compute the
change in risk as a function
of a change in deal terms

125. Requires Mapping of Deal Terms
to actual substantive outcomes
#legaldata
#legalanalytics

126. this is particularly important
when non-lawyers are
doing the negotiation
(for example your global sales force)

128. Data Driven Compliance

129. 80%+ of the world’s data
is unstructured data

130. Solution is to either let
tech or human process that data

131. And humans are actually pretty
good pattern detectors

132. But only for
certain types of problems

133. Trading (HFT in particular)
is about looking for anomalies

134. the discovery + compliance convergence

139. a hard #bigdata problem in law
(near real time) compliance
FCPA, Product Defect, etc.

140. the goal is
near real time monitoring

141. defect w/5 ‘airbag’
version 1.0
backdate w/5 ‘option’
etc.

142. near real time monitoring of
version 2.0
a massive volume of communications

144. Corp Security Beginning to
mirror today’s NSA

145. Behavior will change
(i.e. rogue action will be done offline)
Corp Security Beginning to
mirror today’s NSA

146. 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

147. 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

149. #Predict Case Outcomes
Data Driven Legal Underwriting

150. A Deeper Dive
on Predicting
Predicting Case Outcomes
(other problems can be
solved using similar methods)

151. Supreme Court of United States
#PredictSCOTUS

153. There are only 3 ways
to predict something
Experts
Crowds
Algorithms

154. Experts

155. 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:

156. experts

157. Case Level Prediction
Justice Level Prediction
67.4% experts
58% experts
From the 68
Included
Cases
for the
2002-2003
Supreme
Court Term

158. these experts probably
overfit

159. they fit to the noise
and
not the signal

162. we need to
evaluate
experts and
somehow
benchmark
their
expertise

163. from a pure
forecasting
standpoint

164. the best
known
SCOTUS
predictor is

166. the law
version of
superforecasting

168. Crowds

169. crowds

172. https://fantasyscotus.lexpredict.com/case/list/
We can
generate
Crowd
Sourced
Predictions

173. however,
not all
members of
crowd are
made equal

174. we maintain
a ‘supercrowd’
which is the top n%
of predictors
up to time t

175. the
‘supercrowd’
outperforms
the overall
crowd
(and the best
single player)

178. not
enough
crowd
based
decision
making in
institutions

180. “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.”

181. in law
here
is a
commercial
offering

182. design
to
unlock
untapped
expertise
in
organizations

183. Allowing
for
Frictionless
Crowdsourcing
#ManualUnderwriting

184. https://lexsemble.com/

185. https://lexsemble.com/

187. Algorithms

188. 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
algorithms

189. we have developed an
algorithm that we call
{Marshall}+
random forest

190. 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

191. Total Cases Predicted
Total Votes Predicted
7,700
68,964

192. Justice Prediction
Case Prediction
70.9% accuracy
69.6% accuracy
From 1953 - 2014

193. 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
Revise and Resubmit @ PloS One

195. Experts, Crowds, Algorithms

196. For most problems ...
ensembles of these streams
outperform any single stream

197. Humans
+
Machines

198. Humans
+
Machines
>

199. Humans
+
Machines
Humans
or
Machines
>

201. Ensembles come in
various forms

202. Here is a well known example

203. 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

204. poll weighting

206. A Visual Depiction of
How to build an
ensemble method in our
judicial prediction example

207. expert crowd algorithm
ensemble method
learning problem is to discover when to use a given stream of intelligence

208. expert crowd algorithm
via back testing we can learn the
weights to apply for particular problems
ensemble method
learning problem is to discover when to use a given stream of intelligence

210. Legal Analytics +
#MLaaS
Act Four

211. Given large fixed costs
infrastructure
+
human capital
(data scientists)
Historically speaking

212. harder to successfully deploy
high quality enterprise
applications for relatively
narrow (sub)verticals

213. Law is a relatively small vertical
and there is lots of diversity
among tasks lawyers undertake …

214. in addition
there is a
borderline
pathological
numerophobia
among lawyers

215. Analytics /
Quant Legal Prediction
has come to law
Notwithstanding these head winds—

217. I predict some very
interesting economic
forces will impact the
#legalanalytics space

218. And applications
are about to get far
cheaper to develop

219. Emerging Business Model -
Machine Learning as a Service
#MLaaS

222. The
Cloud
Wars

223. Commercial Examples

224. Machine
Learning as
a Service
#MLaaS

225. Machine Learning as a Service
#MLaaS

226. Machine Learning as a Service
#MLaaS

227. Machine Learning as a Service
#MLaaS

228. historically one needed to
build the full stack (i.e end to
end) for an application

229. Standing on
the Shoulders of Giants

230. The (Emerging) Last Mile Problem
in (Legal) Analytics

231. Off the
Shelf
#MLaaS, etc.
(perhaps with some
configuration
and/or
customization)
Unique Domain
Specific Offering

232. MLaas + Open Source
Decreases Cost of Production
Lowers the Cost of Protoyping

234. Fin (Legal) Tech
Part Five

235. Today I have encouraged
collaboration

236. and one reason is
that both sides
(firms / clients) can
unlock more
enterprise value
by working together

237. because
past is merely prelude

238. because the biggest change in legal
not robots

239. because the biggest change in legal
financialization
not robots

241. developing a data strategy

242. developing a data strategy
leveraging #MLaaS

243. developing a data strategy
leveraging #MLaaS
we get better at predicting

244. developing a data strategy
leveraging #MLaaS
we get better at predicting
which opens the door for…
#FinTech

245. #FinTech
removing
socially
meaningless
frictions
characterizing
(pricing)
increasingly exotic
forms of risk

246. #Fin(Legal)Tech
application of those ideas and
technology to a wide range of
law related spheres including
litigation, transactional work
and compliance.

248. if we can predict we can
develop insurance

249. if we can predict we can
develop insurance
if we can predict
we can develop trading strategies

250. if we can predict we can
develop insurance
if we can predict
we can develop trading strategies
if we can predict
we have assets under mgmt.

251. Just a Few Examples of
Fin(Legal)Tech

252. #fin(legal)tech
pricing

253. it is *not* predicting cost of
this particular matter where n=1

254. correctly characterize the
distributional properties of
a portfolio of matters

255. both + and -
including identification of outliers

256. apply portfolio theory

257. to take n=1
and
scale to n=many
#fin(legal)tech

258. #self insurance
today this is how you
would run a more
rigorous version of

259. tomorrow?
learn from legal ops service
offering to build a commercial
insurance product offering
legal cost insurance ?
other exotic insurance offerings?

260. AIG to Launch Data-
Driven Legal Ops
Business in 2016
https://bol.bna.com/aig-to-
launch-data-driven-legal-
ops-business-in-2016/

262. #fin(legal)tech
In such a world,
Law Firm is *not* interfacing
with client but rather insurance
company regarding fees

264. Earlier I discussed the
application of
experts, crowds +
algorithms

265. as applied
to predicting
case outcomes

266. that was an example
of manual underwriting

267. Given our ability to offer
forecasts of judicial
outcomes, we wondered
if this information could
inform an event driven
trading strategy ?

268. Paper Released
August 24, 2015
http://arxiv.org/abs/1508.05751
available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726

271. lots of litigation decisions
are just a version of this basic idea
law = finance

273. lots of litigation decisions
are actually implicit litigation finance
(or self insurance)
#fin(legal)tech

274. But most implicit litigation
finance is not based upon
rigorous underwriting …
law =! finance
(but it will)

276. http://www.slideshare.net/
Danielkatz/fin-legal-tech-laws-
future-from-finances-past-
professors-daniel-martin-katz-
michael-j-bommarito-ii

277. TheLawLab.com

278. FinLegalTechConference.comNovember 4, 2016

280. In sum, I believe …

281. Over the coming years,
we are going to be able
financialize large elements
of the legal industry

282. By which I mean —-
apply the tools of finance
and insurance to measure /
predict a wide range of
procedural + substantive
outcomes

283. it will help better establish
the value proposition
associated with a wide
range of legal tasks …

284. As we move items from
the ‘art’ column to the
‘science’ column …

285. There will be impacts on
the industrial organization
of the legal industry

286. But what remains
there after will be a
better industry …

287. it will help focus every individual
and every organization on the
places where they actually provide
a return on investment (ROI)

288. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@