Seven Observations Regarding Innovation and the Legal Industry - Professor Daniel Martin Katz
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seven observations by daniel martin katz edu | chicago kent college of law blog | ComputationalLegalStudies.com corp | LexPredict.com regarding innovation and the legal industry page | DanielMartinKatz.com
At the outset, I should note there are several distinct markets
for legal services
Enterprise Law
Small and Medium
Enterprises
Retail
Government
Criminal
five legal sub-sectors
Enterprise Law
Small and Medium
Enterprises
Retail
Government
Criminal
five legal sub-sectors
My Friend Bill Henderson ...
has some basic data on these markets
Enterprise Law
statistics via Bill Henderson
Entities with (>50+Million)Fortune 1000 other large entities
Enterprise Law
statistics via Bill Henderson
Size of Market $150 billionNumber of Potential Clients 40,000
Small/Medium Enterprises
statistics via Bill Henderson
Size of Market $60 billionNumber of Potential Clients 6 million
Retail / Main Street
statistics via Bill Henderson
Number of Potential Clients 318 million
Size of Market $75 billion (with huge potential upside)
I will toggle between these related but distinct legal sub-markets
lex.startup
horizontal integration of legal IT
polytechnic legal education
complexity and legal production functions
quantitative legal prediction
lean law
seven observations
technology aided access to justice
observation 1
What is the hallmark of the bespoke legal work?
Complexity
Social, Economic and Political Complexity
Which for our purposes manifests in legal complexity
In the face of ever growing legal complexity we have applied greater and greater numbers of human experts to solve the underlying problem
Lawyer as Complexity Engineer
complexity keeps growing ...
and so has total expenditures on legal services
Legal Expenditures as a function of GDP(some disagreement between these plots but they project a similar trend)
Cobb Douglas is the traditional way
to describe a production
function
Labor Capital
Cobb Douglas is the traditional way
to describe a production
function
Labor Capital
Cobb Douglas is the traditional way
to describe a production
function
historically we have turned this dial
~1984 - 2009 ~2009 - Present
Returns to Legal Experts (Labor)
~1984 - 2009 ~2009 - Future
Returns to Legal Technology (Capital)
Legal is a mature market
and in mature marketsefficiency trumps growth
What does the change in the return structure imply?
Far greater returns in process improvement
substitution of capital for labor
observation 2
all of the above is a necessary precondition for legal entrepreneurs
Lex.Startupis beginning to take hold
15 2009
Lex.Startup
15 2009
Lex.Startup
15 425+2009 2015
Law or Legal Related Companies as highlighted by Josh Kubicki @ ReInventLaw London 2013
Lex.Startup
The VC Community Is Turning to Legal
and investing real money
R e p o r t e d s a l e price between $35 million and $40 million.
Final Number was l i k e l y b e t w e e n $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 Si l icon Valley’s Sequoia C a p i t a l w h i c h invested $7 million in 2007 ....
And There is Lots More in this Space ...
So what are these folks doing?
R + D Function in the Legal Industry
observation 3
The Rise of Quantitative
Legal Prediction
Quantitative Legal
Prediction
Data Driven Law
Practice
It Has Already Begun ...
implication is that every organization in legal
needs a data strategy
Every organization needs relevant human capital
(and in law such human capital is in limited supply)
Quantitative Methods for LawyersProfessor Daniel Martin Katz Fall 2014
Legal Analytics
Professor Daniel Martin KatzProfessor Michael J Bommarito II
“The software identifies standard and terms in contracts, and its benchmarking tools show lawyers how their current document compares to the standard.”
TyMetrix - Using $50 billion+ in Legal Spend Data to Help GC’s Look for Arbitrage Opportunities, Value Propositions in Hiring Law Firms
Legal Procurement (High End of Market)
There are 3 Known Ways to Predict Something
Algorithms, Experts, Crowds
example from my own work
predicting the decisions of the Supreme Court of the United States
experts
crowds
BlackReed
FrankfurterDouglasJacksonBurtonClark
MintonWarrenHarlan
BrennanWhittakerStewartWhite
GoldbergFortas
MarshallBurger
BlackmunPowell
RehnquistStevensOConnor
ScaliaKennedySouter
ThomasGinsburgBreyerRoberts
AlitoSotomayor
Kagan
1953 1963 1973 1983 1993 2003 2013
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algorithms
For most problems ... ensembles of these streams
outperform any single stream
Humans +
Machines
Humans +
Machines>
Humans +
Machines
Humans or
Machines>
question is how to assemble such streams for particular problems
so that we are not required to rely exclusively on experts
observation 4
Our Paper for the Symposium
Information Management is a significant problem in legal
data that could inform operations is not collected /
or not regularized
information necessary to undertake due dilligence or other regulatory exercises is
locked in an antiquated format (i.e. pdf, word, tif file)
Dodd-Frank RRPfor SIFI’s
(Systemically Important Financial Institution)
EXAMPLE:
Resolution & Recovery Plans are Living Wills for Banks
“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.”
Ideal RRP is a ‘War Game’ whereby a SIFI demonstrates it is
robust to failure of various counterparties
but requires review and understanding of the set of agreements across all
business lines (p&l’s)
problem is legal work product is not a
pointable data object
horizontal integration of legal work product in the
broader corporate technology ecosystem represents a source of immediate value creation
“Watson [and related 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....”
for example - contracts should be born
(or processed) as computational to point straight into finance/acct
and other relevant IT systems
stored legal work
product
play “whack-a-mole”, reacting to problems by creating fear and
friction within organizations and the impression that there is a legal