The E-Discovery Games

Dave Lewis, Ph.D.
President, David D. Lewis Consulting
Co-founder TREC Legal Track
Testifying expert in Kleen Products, LLC, et al. v. Packaging Corp. of America, et al
Fellow of the American Association for the Advancement of Science
75+ publications; 8 patents in:
e-discovery
information retrieval
machine learning
natural language processing
applied statistics
Past research positions: University of Chicago, Bell Labs, AT&T Labs
http://www.DavidDLewis.com

Kara M. Kirkeby, Esq.
Manager of Document Review Services for Kroll Ontrack
Previously managed document reviews on complex matters for a large law firm
Member: Minnesota State Bar Association (Civil Litigation Section), the Hennepin County Bar Association, the American Bar Association, Minnesota Women Lawyers (Communications Committee)
Served as a judicial law clerk for Hon. Karen Klein, Magistrate judge of the U.S. District Court of North Dakota
J.D., magna cum laude, Hamline University School of Law
E-mail:
[email protected]

Discussion Overview
What is Technology Assisted Review (TAR)?
Document Evaluation
Putting TAR into Practice
Conclusion

What is Technology Assisted Review?

Why Discuss Alternative Document Review Solutions?
Document review is routinely the most expensive part of the discovery process. Saving time and reducing costs will result in satisfied clients.
Traditional/Linear
Paper-Based
Document
Review
Online Review
Technology
Assisted
Review

Why Discuss Alternative Document Review Solutions?
Conducting a traditional linear document review is not particularly efficient anymore
Focus instead on a relevance driven review process involving lawyers and technology working together

What Is Technology Assisted Review (TAR)?
Three major technologies:
Supervised learning from manual coding
Sampling and statistical quality control
Workflow to route documents, capture manual decisions, and tie it all together in a unified process
recall: 85% +/- 4%
precision: 75% +/- 3%
Presented by Dave Lewis

Supervised Learning: The Backbone of TAR
By iterating supervised learning, you target documents most likely to be relevant or on topic, creating a virtuous cycle:
Presented by Dave Lewis

Software learns to imitate human actions
For e-discovery, this means learning of classifiers by imitating human coding of documents
Any content-based sorting into classes can be imitated
Responsive vs. Non-responsive
Privileged vs. Non-privileged
Topic A vs. Topic B vs. Topic C
Widely used outside e-discovery:
Spam filtering
Computational advertising
Data mining
Supervised Learning: The Backbone of TAR
Presented by Dave Lewis

Text REtrieval Conference (“TREC”), hosted by National Institute of Standards and Technology (“NIST”) since 1992
Evaluations open to academics and industry
TREC Legal Track (since 2006) provides simulated review for responsiveness task
Focus is on comparing technology assisted approaches
Not a human vs. machine bakeoff
Not a product benchmark
However, results suggest advantages to technology assisted review
Research & Development: TREC Legal Track
Presented by Dave Lewis

High effectiveness of TAR runs
Best T-A runs in TREC 2009 examined 0.5% to 4.1% of collection while finding an estimated 76.7% of responsive documents with 84.7% precision
Low effectiveness of manual review
Substantial effort needed by TREC organizers to clean up manual review to point it can be used as gold standard
An argument can be made (Grossman & Cormack, 2011) that 2009 data shows TAR results better than pre-cleanup manual review
Research & Development: TREC Legal Track
Presented by Dave Lewis

Analyze
What is Technology Assisted Review?
Train
START:
Select document set
Identify training set
Knowledgeable human reviewers train system by categorizing training set
System learns from training; prioritizes documents and suggests categories
Evaluate
Evaluate machine suggestions
END: Produce documents
Presented by Dave Lewis
Quality control production set
Human reviewers:

SELECT
Manually review documents for training
Key docs from your side or opponent
Docs found by searches on key terms
Docs prioritized for review
Random (non-QC) docs
Docs difficult for previous iteration's classifier (active learning)
Effectiveness increases as training set grows
various
docs for training
random docs for QC
manual
review
train classifiers
auto-code documents
compare coding with elite coding on random sample
estimate effectiveness for entire set
review for privilege
PRODUCTION
priority
docs for review
YES
NO
Learning and Classification
Presented by Dave Lewis

Manually review prioritized documents
Needs of case
Classifier predictions
If classifier is accurate enough, trust its call on responsiveness?
Privilege is more sensitive
Manually select some subsets for 100% privilege review
Employ sampling for other subsets
Classifiers can also help identify likely privileged docs
Production
Presented by Dave Lewis

Any binary classification can be summarized in a 2x2 table
Linear review, automated classifier, machine-assisted...
Responsive v. non-responsive, privileged v. non-privileged...
Test on sample of n documents for which we know answer
TP + FP + FN + TN = n
Classification Effectiveness
Presented by Dave Lewis

True Negatives
False Positives
True Positives
False Negatives
Classifier Says "Yes"
"Yes" is Correct
All Documents
Classification Effectiveness
Presented by Dave Lewis

Recall = TP / (TP+FN)
Proportion of interesting stuff that the classifier actually found
High recall of interest to both producing and receiving party
Classification Effectiveness

Precision = TP / (TP+FP)
Proportion of stuff found that was actually interesting
High precision of particular interest to producing party: cost reduction!
Classification Effectiveness

Seminal 1985 study by Blair & Maron
Review for documents relevant to 51 requests related to BART crash
Boolean queries used to select documents for review
Process iterated until reviewer satisfied 75% of responsive documents found
Sampling showed recall of less than 20%
B&M has been used to argue for everything from exhaustive manual review to strong AI
Real lesson is about need for sampling!
Research & Development: Blair & Maron
Presented by Dave Lewis

Want to know effectiveness without manually reviewing everything. So:
Randomly sample the documents
Manually classify the sample
Estimate effectiveness on full set based on sample
Type of estimates:
Point estimate, e.g. F1 is 0.74
Interval estimate, e.g. F1 in [0.67,0.83] with 95% confidence
Sampling is well-understood
Common in expert testimony in range of disciplines
Sampling and Quality Control
Presented by Dave Lewis

SELECT
various
docs for training
random docs for QC
priority docs for review
manual
review
train classifiers
auto-code documents
compare coding with elite coding on random sample
good enough to produce?
estimate effectiveness for entire set
review for privilege
PRODUCTION
Manually review random sample for QC
Use best reviewers here
Estimate recall, precision, etc.
Of auto-coding, manual review, or both combined
Estimates used in:
Deciding when finished
Tuning classifiers (and managing reviewers)
Defensibility
Auto-coding can also be used to find likely mistakes (not shown)
YES
NO
Sampling and Quality Control
Presented by Dave Lewis

Putting TAR into Practice

Barriers to Widespread Adoption
Industry-wide concern: Is it defensible?
Concern arises from misconceptions about how the technology works in practice
Belief that technology is devoid of any human interaction or oversight
Confusing “smart” technologies with older technologies such as concept clustering or topic grouping
Limited understanding of underlying “black box” technology
Largest barrier: Uncertainty over judicial acceptance of this approach
Limited commentary from the bench in the form of a court opinion
Fear of being the judiciary’s “guinea pig”

Developing TAR Case Law
Da Silva Moore v. Publicis Groupe
Class-action suit: parties agreed on a protocol signed by the court
Peck ordered more seeding reviews between the parties
“Counsel no longer have to worry about being the first ‘guinea pig’ for judicial acceptance of computer-assisted review … [TAR] can now be considered judicially approved for use in appropriate cases.”
Approximately 2 weeks after Peck’s Da Silva Moore opinion, District Court Judge Andrew L. Carter granted plaintiff opportunity to submit supplemental objections
Plaintiff later sought to recuse Judge Peck from the case
Stay tuned for more….

Developing TAR Case Law
Kleen Products v. Packaging Corporation of America
Defendants had completed 99% of review, Plaintiffs argue that they should use Predictive Coding and start document review over
Not clear whether Defendants did more than keyword search
Other notable points from Kleen Products
Defendants assert they were testing their keyword search queries, not just guessing
Argue they did not use Predictive Coding because it did not exist yet
Stay tuned for more….

Technology Assisted Review: What It Will Not Do
Will not replace or mimic the nuanced expert judgment of experienced attorneys with advanced knowledge of the case
Will not eliminate the need to perform validation and QC steps to ensure accuracy
Will not provide a magic button that will totally automate document review as we know it today

Technology Assisted Review: What It Can Do
Reduce:
Time required for document review and administration
Number of documents to review; if you choose an automated categorization or prioritization function
Reliance on contract reviewers or less experienced attorneys
Leverage expertise of experienced attorneys
Increase accuracy and consistency of category decisions (vs. unaided human review)
Identify the most important documents more quickly

TAR Accuracy
TAR must be as accurate as a traditional review
Studies show that computer-aided review is as effective as a manual review (if not more so)
Remember: Court standard is reasonableness, not perfection:
“[T]he idea is not to make it perfect, it’s not going to be perfect. The idea is to make it significantly better than the alternative without as much cost.”
-U.S. Magistrate Judge Andrew Peck in Da Silva Moore

What is Intelligent Review Technology (IRT) by Kroll Ontrack?
Intelligent Prioritization
Intelligent Categorization
Automated Workflow
Reviewing Efficiently, Defensibly & Accurately
Augments the human-intensive document review process to conduct faster and cheaper discovery

Cut off review after prioritization of documents showed marginal return of responsive documents for specific number of days
Cut off review of a custodian when, based on prioritization statistics that showed only non-responsive documents remained
Used suggested categorizations to validate human categorizations
Used suggested categorizations to segregate documents as non-responsive at >75% confidence level. After sampling that set, customer found less than .5% were actually responsive (and only marginally so). Review was cut off for that set of documents
Used suggested categorizations to segregate categories suggested as privilege and responsive at >80% confidence. Sampled, mass categorized
Use suggested categorizations to mass categorize documents and move them to the QC stage, by-passing first-level review
Used suggested categorizations to find documents on a new issue category when review was nearing completion
Successes in the Field: Kroll Ontrack’s IRT

Successes in the Field: Kroll Ontrack’s IRT

Conclusion

Parting Thoughts
Automated review technology helps lawyers focus on resolution – not discovery – through available metrics
Complements human review, but will not replace the need for skillful human analysis and advocacy
We are on the cusp of full-bore judicial discussion of Automated Review Technologies
Closely monitor judicial opinions for breakthroughs
Follow existing best practices for reasonableness and defensibility
Not all Technology Assisted Review solutions are created equal
Thoroughly vet the technology before adopting

Q & A