Predictive Coding Legaltech

1. Predictive Coding 2.0 Making E-Discovery More Efficient and Cost Effective John Tredennick Jeremy Pickens Jim Eidelman

2. How Many Do I Have to Check? 1.  You have a bag with 1 million M&Ms 2.  It contains mostly brown M&Ms 3.  You cannot see into the bag 4.  You have a scoop that will pull out 100 M&Ms at a time 5.  Your hope is that there are no red M&Ms in the bag 6.  You pull out a scoop and they are all brown How many scoops do you need to review to be confident there are no red M&Ms?

3. Let’s Take a Poll How many scoops? 2 1 3 5 10 20 100? 500? 1,000?

4. How Confident Do You Need to Be? Does 95% work? How about 99% How many errors can you tolerate? §  Five out of a hundred? §  One out of a hundred? §  One percent = 10,000 At a 95% confidence level and 5% percent margin of error: 384 M&Ms At a 99% confidence level and 1% margin of error: 459 M&Ms At a 100% confidence level and 0% margin of error: 1,000,000 M&Ms

5. Predictive Coding

6. Does it Work?

7. What Have the Courts Said?

8. What Have the Courts Said? “Until there is a judicial opinion approving (or even critiquing) the use of predictive coding, counsel will just have to rely on this article as a sign of judicial approval. In my opinion, computer-assisted coding should be used in those cases where it will help ‘secure the just, speedy, and inexpensive’ (Fed. R. Civ. P. 1) determination of cases in our e-discovery world.” Magistrate Judge Andrew Peck

9. Predictive Coding 1.0 1.  Assemble your corpus. 2.  Assemble a seed set of documents. 3.  Review the seed set. 4.  Apply machine learning and automatically tag the remainder of the corpus.

10. Predictive Coding 1.0 §  Tremendous gains in review effectiveness §  Substantial cost savings §  It works. Often quite well ….when the corpus is complete.

11. 67.5 uploads per case 533 matters, nearly 36,000 uploads across the matters.

12. 166.3 days loading case This is collection driven, not loading limits.

13. In which upload and on which day do your responsive documents show up? 67 166 uploads days Terms that do not appear early begin appearing later.

14. Machine-Assisted Decision Making Upload timeline of 6 TB case. When should machine-assisted Is it here? decision making (e.g. early case assessment) begin? Or here?

15. Example: Responsive Early, Junk Later To: bob@company.com, alice@company.com From: charles@company.com Subject: Company Picnic Bob, would you coordinate with Alice and make sure we have enough hamburger buns for the company picnic? Please try and find them at a reasonable price. Responsive Junk

16. Example: Junk Early, Responsive Later To: bob@company.com, alice@privatemail.com From: charles@company.com Subject: Get Together Let’s get together at 7pm at the Sports Bar to discuss pricing of our components. The Broncos are playing and I really want to watch Tebow. Junk Responsive

17. Problems With Predictive Coding 1.0 The corpus is almost never complete §  Continuous collection and rolling uploads §  When does “Early Case Assessment” begin? Changing Issues §  Responsiveness is “bursty” Shifting Concept Relationships §  Due both to increasing corpus and changing issues §  Exploration is extremely limited

18. Our Approach Predictive Coding 2.0 necessitates the ability to deal with dynamic change and flux. We have developed a flexible analytics framework based on bipartite graphs It is aware of changes in corpus and in coding so as to enable smart review and adaptive related concept suggestion as information pours in.

19. Our Approach Avoid the lock-in that arises due to poor decision making that occurs early in the matter when corpus (collection) and coding information is incomplete. Goal: Continuous Case Assessment

20. What Is Underneath? A full bipartite graph of the documents and features (e.g. words, phrases, dates) that comprise those documents

21. Terms Documents

22. Feedback: Immediate and Continuous Continuous feedback aids better decision making and predictive coding. Adapts to both: New arrival of coding information New arrival of documents and terms

23. Terms Documents

24. Predictive Coding 2.0 Feedback – and improvement – is iterative, continuous, amplified. The more you review, the less you have to review % of Docs Examined Manually

25. Better Decisions As Understanding Improves Term relationships change over time Using continuous improvement, decisions can be revised and refined as the matter proceeds.

26. Terms Documents Time uncovers new relationships

27. Looking at Concepts Over Time 20% 65% Start with the lube fuels key term piping fob battery purityethane “fuel” mounted petrochemicals redundant fin batteries paraxylene At 20% compartments cif mixture phy these are airflow fwd the related ansi swopt ventilation brentpartials terms chargers brg stainless locswap rotor benzene And at 65% bleed diff accessory spd plenum liquids detector opt

28. Related Terms Through Coding Filters

29. Terms Documents Responsive NonResponsive

30. Putting Related Concepts to Work The whole corpus Topic 203 …whether the Company had met, or could, would, or might meet its financial forecasts, models, projections, or plans… Topic 205 …analyses, evaluations, TREC collection projections, plans, and reports on with many topics the volume(s) or geographic identified location(s) of energy loads.

31. Model In the Whole Collection Term Score Look at the keyword “model” modeling 1000 equation 864 Scope is the stochastic 706 whole collection variables 677 parameters 518 probability 365 simulation 337 assumption 325 returns 251 curves 211

32. Model In Topic 203 Term Score Look at the keyword “model” flows 1000 assumptions 913 Scope: Topic 203 gains 872 shares 864 meeting liquidity 486 financial fluctuations 374 forecasts analysts 285 cents 254 whitewing 237 handles 166

33. Model In Topic 205 Term Score Look at the keyword “model” bids 1000 congestion 611 Scope: Topic 205 loads 455 constraints 354 analyzing clearing 292 energy zonal 194 volumes signals 192 procure 190 dispatch 152 csc 120

34. Model In Comparison Now, Whole Corpus Topic 203 Topic 205 imagine this modeling flows bids with batches equation assumptions congestion and coding stochastic gains loads changes variables shares constraints over time! parameters liquidity clearing Note: Our system probability fluctuations zonal can accept any simulation analysis signal combination of coding and assumption cents procure metadata filters to dynamically returns whitewing dispatch assess your data curves handles csc

35. Summary Incomplete Collections Changing Coding Calls Havoc for Machine Coding

36. Predictive Coding 2.0 Problem: The corpus is almost never complete Answer: Review Algorithms that are iterative and continuous Problem: Changing Issues Answer: Review Algorithms that are adaptive and continuous Problem: Shifting Concept Relationships Answer: Concept Relationships that are calculated dynamically, on- the-fly, and coding-aware. Continuous Case Assessment

37. Analytics Consulting §  Analytics consulting and predictive ranking for nearly 4 years §  How it started -- Before “Predictive Coding” became popular: “Can’t you predict what documents are probably relevant based on your review so far?” – Judge, SDNY §  Predictive Ranking: Iterative search techniques + algorithms §  Then off-the-shelf Predictive Coding 1.0 technologies §  Catalyst’s research is exciting! We apply the research to real-world scenarios. Applying Bipartite Analytics…

38. Smart Review with the Bipartite Analytics Technology Advantages: §  Accurate §  Dynamic §  Flexible §  “Just in Time” suggestions

39. Smart Review Scenarios 1. “What happened” – examples: FCPA investigation, conspiracy ECA 2. Typical large scale litigation with lots of ESI – e.g., class action lawsuit 3. Highly complex litigation with multiple issues – e.g. patent and unfair competition claims

40. Scenario 1 – What happened? Goal: Rapidly determine facts and resolve matter if possible Applying the Technology Small number of knowledgeable attorneys drill into documents using the fusion of advanced search features and flexible predictive coding.

41. Scenario 1 – What happened? Goal: Rapidly determine facts and resolve matter if possible Applying the Technology Small number of knowledgeable attorneys drill into documents using the fusion of advanced search features and flexible predictive coding. §  Faster location of valuable “veins” of information due to search filters §  Rapid learning and application of that learning through flexible, “just in time” predictive coding 2.0. §  “Choose your own adventure”

42. Scenario 2 – Large Scale Litigation Goal: Minimize cost because of learning across large document set, increase quality with focused review, and maximize protection of privilege and trade secrets Applying the Technology: §  Prioritized review based on rapid, continuous learning §  Large scale defensible culling §  More accurate ranking of “potentially privileged” documents

43. Scenario 3– Highly Complex Litigation Goal: Review and produce with multiple and changing issues Applying the Technology §  Rapid learning across multiple topics §  Leverage ability to adjust for change in topics §  Review quality improves because of focus §  Explore otherwise hidden subjects with Concept Explorer §  Leverage learning across narrow, focused lines of inquiry (e.g., emails between two people in a narrow time window) §  Protect privileged documents

44. Predictive Coding 2.0 Making E-Discovery More Efficient and Cost Effective John Tredennick Jeremy Pickens Jim Eidelman

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