Quantitative Methods for Lawyers - Class #1 - Professor Daniel Martin Katz - MSU College of Law

Big Data, Artificial Intelligence and The Great Transformation of the Market for Legal ServicesQuantitative Methods for Lawyers Class #1 Daniel Martin Katz Michigan State University College of Law

My Background Assistant Professor of Law PhD Political Science & Public Policy Fellow in Empirical Legal Studies 2009 - 2011 University of Michigan Center for the Study of Complex Systems ( Fellow 2009-2010 )

This is the Era of “Big Data”Increasing Computing PowerDecreasing Data Storage CostsFundamentally Altering the Scope of Scientific & Technical Possibility

Highlighting the Data Deluge 2008 2009 2010

Highlighting the Data Deluge 2011 2011

Highlighting the Data Deluge

So How Big is‘Big’?

How Much Data Is a Petabyte?

$600 to Buy a Disk that canStore All of the World’s Music$1000 Harddrive Could Easily Store Every Written Judicial Decision in U.S. History

http://bulk.resource.org/

Law is Data

Big Data isTransforming Various Industries

A Brief Discussion of the McKinsey Study

http://www-01.ibm.com/software/data/bigdata/industry.html

Financial ServicesHelp agents find patterns, understand context andflag abuse by analyzing customer correspondence inconjunction with structured information.Predict consumer behavior by using cross-line ofbusiness details to correlate consumer debt ratiosand transaction patterns.Automate entity identification and risk profiling basedon SEC and other regulatory filings, with the abilityto assemble, model and stay current on large volumesof information.Consume, analyze and act on real-time market datawhile maintaining sub-millisecond response times, evenunder extreme data loads.

RetailAnalyze social media data in conjunction withcustomer buying data to predict customer behavior,and track sentiment and brand perception.Monitor social media sources for rumors, deliberatefalse information, and impersonation of employees tomore quickly understand and correctmisinformation.Issue marketing promotions, analyze their success inreal time, and adapt promotions to optimize outcome.Analyze linkages between specific online advertisingand recommendations to buying behavior so that youcan strategically merchandise and place effectiveadvertising.Run highly complex pattern detection algorithms onyears, rather than months of transaction data,allowing the organization to rapidly detect andrespond to new fraud scenarios and exposures.(Also True of Financial services)

HealthcarePerform complex real-time analytics onphysiological streams of data in ICUenvironments to detect life-threateningconditions in time to proactively intervene.Manage and analyze real-time sensor data toassist chronic disease patients.Capture and analyze clinical information fromelectronic health records to speed the creationand diffusion of medical knowledge.Track a wide variety of data streams and leverageprior benchmarks to potentially help to exposethe early signs of an epidemic

TelecomPerform real-time mediation with the ability tohandle billions of call detail records per day.Process real-time call data to predict customerchurn and remediate customer satisfaction issues(ex: dropped calls) as soon as they happen.Enable real-time geo-mapping and marketingpromotions.Analyze social media data with customer buying datato predict customer behavior and track sentimentand brand perception.Unlock the insights embedded in call center voicerecordings by doing voice-to-text conversions, thenperforming advanced text analytics on theconverted recordings.

http://www.youtube.com/watch?v=dPv8psZsvIU

Okay But ...

Data is only half the story

Computation andArtificial Intelligence

The Artificial Intelligence Revolution is On

The Artificial Intelligence Revolution is On ....But it is not what we thought

‘Soft’ Artificial Intelligence

“Practically every financial transaction,from someone buying a cup of coffee tosomeone trading a trillion dollars ofcredit default derivatives, is done insoftware ....Health care and education, in my view, arenext up for fundamental software-basedtransformation. My venture capital firmis backing aggressive start-ups in both ofthese gigantic and critical industries.We believe both of these industries, whichhistorically have been highly resistant toentrepreneurial change, are primed fortipping by great new software-centricentrepreneurs ...Companies in every industry need toassume that a software revolution iscoming.”

The Artificial Intelligence Revolution is On

Play Videohttp://www.youtube.com/watch?v=lI-M7O_bRNg&feature=relmfu

Play Videohttp://www.youtube.com/watch?v=DywO4zksfXw&feature=related

That’s Me

Some Applicable Terms

Natural Language ProcessingKnowledge Representation Feature Extraction Feature Selection Machine Learning Classification Clustering

http://www.drewconway.com/zia/?p=2378

A More Meaningful‘Soft’ AI Example?

E-Discovery

A Broad Idea ofWhat is Possible...

Key Point is That Human ReasonersHave Scale Limitations

Examples: Enron Email: What do fraudand corporate meltdown look likefrom the inside?Half a million messages, 150 users—mostly Enron’s senior managementMade public by the FERC during investigationPurchased and released by Leslie KaelblingAvailable here: http://www.cs.cmu.edu/~enron/Visualizations here: http://hci.stanford.edu/jheer/ projects/enron/v1/

Examples: Enron Email: What do fraudand corporate meltdown look likefrom the inside? Visualizations here: http://hci.stanford.edu/jheer/projects/enron/v1/

visualize clustering results – color coded to enron business e-mailspie charts indicate categorizations of e-mail traffic Visualizations here: http://hci.stanford.edu/jheer/projects/enron/v1/

Visualizations here: http://hci.stanford.edu/jheer/projects/enron/v1/

The ScientificStudy of LawInforming theFuture of Law Practice?

Blog Run with MichaelBommarito II (Michigan CSCS) Jon Zelner (Princeton Ecology & Evolutionary Biology)

A Few Small Projects from My Lab Group

If I Can Do This ...

Imagine WhatGoogle or IBM Could Do...

Legal Information RetrievalLeveraging the Visual Cortex

Exploring theUnited States Code

Developing a Visual Interface for the United States Code

The United States Code

The United States CodeCompiled Version of Federal Statutory LawDrawn from the Statutes at LargeDoes not Include Admin Regulations

These are the 50 titles in the Code

A Perspective on the Scope of Law in a Modern SocietyHow Large is the United States Code?How Complex is the United States Code?Can Interface Design Help Mitigate thatComplexity?

How Large is the United States Code?

How Large is the United States Code? ~Title 29 - Labor

This is the United States Code

The US Code as a Mathematical ObjectHierarchical Structure Title 26 Subtitle A Chapter 1 Subchapter F Part I Section 501 Subsection (c) Paragraph (3)

The US Code as a Mathematical Object Citation NetworkExample: Tax Evasion Title 26 - Tax Might Cite Title 18 - Crimes &Criminal Procedure http://computationallegalstudies.com/ 2009/09/14/the-structure-of-the-united- states-code/

Hierarchical Structure + Citation Network

+Linguistic Content United States Code Features~ 24 million “words”

The US Code as aMathematical Object Published in Physica A (October 2010)

Visually Exploringthe Path of Precedent

United States Supreme Court Case to Case Citation NetworkNodes = Cases Decided by the Supreme CourtDirected Edges = Citations between Cases

Case to Case Citation Network of the United States Supreme Court Oregon v. Elstad 530 U.S. 428 (1985) Weems v. Miranda v. Dickerson v. U.S.United States, Arizona 530 U.S. 428217 U.S. 349 384 U.S. 436 (2000) (1910) (1966)

Exploring the Path of Precedent?Dynamic Visualization of the Early Supreme Court Citation Network

PLAY MOVIE! http://computationallegalstudies.com/ 2010/02/11/the-development-of-structure-in- the-citation-network-of-the-united-states- supreme-court-now-in-hd/Cases Decided by Citations in the Citations fromthe Supreme Court Current Year prior years

Six Degrees of Marbury v. Madison A Sink Based Visualization

The SinkMethod

Developing a Visual Interface for the Path of Precedent

An Example from anAlternative Domain

http://www.autodeskresearch.com/projects/citeology

Improving Key Word Search with Visual Interface

Legal Language Explorer daniel martin katz michael j bommarito ii julie seaman adam candeub eugene agichtein

We Develop a Simple WebInterface that Leverages the Visual Cortex

Relies on Our Ability to Engage in Pattern Detection

To Explore Linguistic Patterns in Large Corpora of Legal Documents

We Start with the Full Text Corpus of Decisions of theUnited States Supreme Court 1791-2005

Develop a N-Gram Based Explorerwith a Portal to the Full Text, etc.

From Text to N-Grams...

Generate the N-Gram Mapping (1) extract the opinion text from each case and store it as a sequence of characters (2) convert this sequence of characters into a sequence of words through Word Tokenization (Penn Tree Bank Algorithm) (3) iterate over words in the length M sequence from index 1 to index M + 1 – N

Generate the N-Gram Mapping

Legal Language Explorer: A Brief Tour of The Interface

Go toLegalLanguageExplorer.com And Access This Interface

Notice The Default Search IsInterstate Commerce, Railroad, Deed

For Each Comma Separated PhraseThe Frequency Plot Appears Below

Currently Supporting Every U.S. Supreme Court Decision From 1791 - 2005

Years Can Be Changed By The End User

Click Here to Access Various Advanced Search Features

The AdvancedSearch Features

Are the Observed Trendsa Function of changes in the volume of the case docket?

Normalization allows End User to control for the Size of the Case Docket

Change the Graph Type By Clicking Here

An Alternative Presentation of the Data

Export the Chart Data(If You Want to Replot in Stata, Excel, R, etc.)

The Results(If You Want to Replot in Stata, Excel, R, etc.)

Access a Case List and Full Text Results

Here are the Returned Results

Download List in Excel(or any .csv)

Access theFull Text aParticular Case

Click Through to AccessResults fromBulkResource.org

For More Information - Full Version of the Paperhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=1971953

ForImplementation Details (Including the Key Value Storage Method, etc.) http://www.michaelbommarito.com/ blog/2011/12/16/building-legal- language-explorer-interactivity- and-drill-down-nosql-and-sql/

Lots MoreComing Soon :)

Smarter Than You Think ...

Next Time ...

The Deathof Big Law &

Law’sInformation Revolution

Quantitative Methods for Lawyers - Class #1 - Professor Daniel Martin Katz - MSU College of Law

by Daniel Katz on Jan 20, 2012

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