Technology-Assisted Review Solutions Predictive Coding Language-Based Analytics SM (LBA) © 2013 RenewData

© 2013 RenewData

Technology-Assisted Review Solutions• Predictive Coding• Language-Based AnalyticsSM (LBA)

© 2013 RenewData 2

Where TAR Fits in the EDRM

© 2013 RenewData 3

A Major Challenge All Clients Face

Now – with Data exploding, Discovery has become a Data problem.

In the Past – Discovery was viewed mainly as a Legal problem…

http://cracking-the-code.blogspot.com

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For every 5 documents reviewed…

The Challenges of Document Review

… 4 usually have nothing to do with your matter

5XCostTime

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* Resnik et al. 2006

Reviewers understand issues differently

They interpret and code documents differently

QC practices are too weak to provide good feedback

It is too expensive to go back and fix errors

ReviewAccuracy Less than *

50%

How do you identify and correct errors before it is too late?

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Knowledge gained by reviewers stays with the reviewers…Case team has to read documents again to understand key facts…

Takes too long before issues are fully understood…

Takes too long to understand how players communicate…

How do you gain the full benefit of your review team’s work?

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• Defining terms: “TAR” and“CAR” are synonymous• What is it?

– TAR is the process of leveraging both technology and human intelligence to rapidly identify relevant documents in a large data set

• Objective?– To reduce costs and give the legal team the ability to

hone in on important documents more quickly• All TAR are not the same (processes or results)

Technology-Assisted Review (TAR)

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Types of TAR

• Predictive Coding • Language-Based AnalyticsSM (LBA)

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• Uses pattern matching to “predict” if relevant documents are present

• Need to “train” software to look for relevant documents• Create a “seed set” that trains the “AI” (artificial

intelligence) in the algorithms in the software• “More like” the “seed set”• Iterative process• Success is highly dependent on skill of the trainer• Wide variance in # of documents to train a seed set

Predictive Coding

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• Linguistic approach using human understanding of language, context, synonyms, and logical expressions

• Coupled with the power of the computer to apply rules consistently without error

(N.B.: Contrast this with what happens in “Traditional Linear Review” where

human reviewers are expected to apply the rules consistently without error or misinterpretation)

Language-Based AnalyticsSM (LBA)


• 2 Tools:– One for Processing– One for Review

Language-Based AnalyticsSM (LBA)


• Surgical– Ability to give specific instructions and input to find

exactly what you want– Access to language experts to create queries that

leverage actual language in documents• Accurate and Defensible

– Statistical analysis of results to ensure high level of accuracy and defensibility

– Systematic approach easily defensible (can be printed)– Zero Defect Test against the discard pile

• Reusable asset if more documents are produced

LBA for “Content Filtering”


• Easy– Simple highlighter function for reviewers to use

• Fast– Multiple documents tagged instantaneously

• Management– Monitor reviewer highlights in real time– Make targeted corrections immediately across all

data with a simple mouse click– Quickly provide feedback to reviewers

• Reusable asset if more documents are produced

LBA for “Review Acceleration”


Results of LBA “Review Acceleration”

150,000 Documents; 15% of documents relevant; Average of 24 hits per highlight3 Reviewers; 350 Docs per day

Fast access to relevant documents

Significant time and cost saved(Typically over 80%)

LBA “Review Acceleration”

Traditional Linear Review


• Better insight for case team• Significantly faster• Better review quality• Simple – highlighter feature• Accuracy assured – no risk• Defensible – no “black box”• Over 80% cost savings

LBA Review Acceleration – in a nutshell

reap significant benefits with a small change in your workflow with no added risk


Combining Both LBA Tools

Using LBA “Content Filtering” Accelerates Review Even More

LBA “Review Acceleration”

Traditional Linear Review


• Da Silva Moore v. Publicis Groupe, 11 Civ. 1279 (ALC)(AJP)(S.D.N.Y. Feb. 24, 2012)

• U.S. Magistrate Judge Andrew J. Peck did not endorse any particular TAR method

• “What the Bar should take away from this Opinion is that computer-assisted review is an available tool and should be seriously considered for use in large-data-volume cases….”

• “Computer-assisted review now can be considered judicially-approved for use in appropriate cases.”

Court Acceptance of TAR


Questions and Further Discussions


Evolution of Solutions

Black and White -> Higher Confidence

“Fuzzy” -> Lower Confidence

Keyword Filtering

Near Duplicates

Email Threading

Predictive Coding

Language Based

AnalyticsSM


Kelvin H. ChinConsulting DirectorRenewData9500 Arboretum BlvdSuite L2-120Austin, TX 78759(512) [email protected]@kelchin273

CONTACT INFO:

mailto:[email protected]

http://www.renewdata.com/


• Kelvin H. Chin is Consulting Director at RenewData. In this role, Chin consults with clients on eDiscovery best practices and appropriate forms of review acceleration. He provides guidance on effective ways to manage costs associated with document review, without compromising on defensibility. He also works closely with clients to develop tailored solutions to meet their unique data archiving needs related to compliance monitoring, data retention management, and legal holds.

• Chin has more than 20 years of strategic, leadership, management, and client services experience in legal services and consulting firms, and has managed eDiscovery and document review for large litigations and government investigations. He has recruited, trained and led teams to manage consultative, client-driven service relationships with CEOs, CFOs, and GCs at Fortune 500 and middle market companies. He was previously with the AmLaw100 firms Womble Carlyle Sandridge & Rice LLP and Edwards Angell Palmer & Dodge LLP, as well as the litigation firm Theodora Oringher PC, where he was chief client development officer. He was a regional vice president for a decade with the American Arbitration Association, a corporate associate at Choate Hall & Stewart LLP, a FINRA mediator/arbitrator for the financial services industry, and co-founder of a technology start up.

• Chin is a frequent presenter to national and state bar associations, international and national industry and trade groups, universities, and business and law schools, and is a National Advisory Council member of Asian Americans Advancing Justice. He is a graduate of Dartmouth College, Yale Graduate School, and Boston College Law School.

Kelvin H. Chin