1. Predictive Coding and Other Document Review Technologies – Where are we now?
Speakers: Ian Campbell, Claire Hass,
Michael Quartararo, Nathan Reichardt,
Oliver Silva,
Moderator: Regis Stafford

2. Today‘s Discussion What is Predictive Coding / TAR?
Why should law firms, vendors and corporations be using it?
Why isn’t predictive coding / TAR being used?
What are the barriers to using it and how can lawyers, vendors and corporations overcome those barriers?
What other litigation technology is on the horizon?

3. Panel Ian Campbell Claire Haas Mike Quartararo Nathan Reichardt
iCONECT
Claire Haas
Google
Mike Quartararo
Stroock
Nathan Reichardt
WilmerHale
Oliver Silva
Nutter
Regis Stafford
Reed Smith

4. What is Predictive Coding / TAR?
a.k.a. “TAR” – a.k.a. “CAR,” a.k.a. “RAR”
Machine learning algorithms and statistical probability tools used to duplicate human decision making
Software determines relevance after training by human reviewer
Computer identifies properties to predict future coding
Process continues until accuracy levels reach stability

5. Not everything is relevant in Predictive Coding !
Document Type
Examp le?
Xmplar?
Word Processing and PDF Documents 
Spreadsheets with numbers 
Spreadsheets with text
PowerPoint slides
PowerPoint slide with notes
Drawings or Images s
Calendar Appointments
Calendar appointments with an agenda
Audio and Video Files

8. Subject-Matter Expert Code Documents: Relevant / Not Relevant
The TAR Process
Define document universe
Train the system
Apply training across document universe (categorize unreviewed documents)
Test, refine categorization
Validate and finalize project
Draw
Random Sample
Categorize
Subject-Matter Expert Code Documents:
Relevant / Not Relevant

9. Continuous Active Learning

10. Why Predictive Coding / TAR?
Cost savings
Time savings
Reduced risk of errors
Sometimes volume of documents and/or value of case makes human review impractical

11. How Accurate is Human Coding?
Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review, Maura R. Grossman & Gordon V. Cormack, XVII Richmond Journal of Law and Technology 11 (2011)
Computer 77%, Humans 60%
“The myth that exhaustive manual review is the most effective…approach to document review is strongly refuted. Technology-assisted review can (and does) yield more accurate results than exhaustive manual review, with much lower effort.”
“Technology-assisted reviews require…human review of only 1.9% of the documents, a fifty-fold savings over exhaustive manual review.”

12. Da Silva Moore v. Publicis Groupe & MSL Group 287 F.R.D. 182 (S.D.N.Y. 2012)
Magistrate Judge Andrew J. Peck:
“…while some lawyers still consider manual review to be the ‘gold standard,’ that is a myth, as statistics clearly show that computerized searches are at least as accurate, if not more so, than manual review.”

13. Judge Shira Scheindlin
“Using search terms is so last decade.”

14. Barriers to the Use of Predictive Coding

15. Retrieved from: https://memegenerator.net/instance/62473404

16. Retrieved from: http://www. slideshare

17. Retrieved from: http://logikcull

18. Change is difficult pessimism risk resilience skepticism autonomy
Change in a law firm is very difficult
Change in a law firm is very difficult especially when attorneys are involved…the data
pessimism
risk
resilience
skepticism
autonomy

19. Document review services business model

20. Look– the computer did as well as the humans!
Bad Experience with Predictive Coding
Look– the computer did as well as the humans!

21. Barrier - Lack of Senior Attorney Time

22. Fear of Attack / Multijurisdictional Litigation
We can’t use predictive coding software because our opponents won’t agree to it.

23. Barrier – Vendor Selection Process
What is the most important criteria to rely upon when selecting a vendor for your TAR project?
- platform;
- personnel;
-reputation in the industry;
-cost

24. Overcoming Barriers

25. Where does TAR Fit In? -TAR can be used for culling
Collection
Original custodian data collected and preserved
Early Case Assessment
-Data culling reduces amount of data for review
-TAR can be used for culling
Document Review
-TAR typically used for relevance
-Optimized linear responsiveness and privilege review
Production
Data produced to opposing party

26. New Technologies

28. Thank you! Ian Campbell Claire Haas Mike Quartararo Nathan Reichardt
iCONECT
Claire Haas
Google
Mike Quartararo
Stroock
Nathan Reichardt
WilmerHale
Oliver Silva
Nutter
Regis Stafford
Reed Smith