It’s time to set the record straight on technology assisted review (TAR). Some people object to what they mistakenly believe is the “black box” nature of the technology, while others are hesitant to adopt an approach that they perceive as novel. This panel will dispel the myths, clarify the definitions, and shed light on the so-called “black box” of technology assisted review. Some audience members may be surprised to learn that technology assisted review is nothing new. Search and clustering technology, for example, have been commonplace for many years. The phrase "technology assisted review" simply refers to a more efficient use of people, process, and technology that is the next evolutionary step in electronic discovery. As with other legal technologies, human expertise and a proven workflow are the keys to success. This panel will clearly explain what technology assisted review is all about and how it can be used as a tool in your practice so that you can make an informed decision about adopting it in your organization

1. Technology Assisted Review (TAR):
Opening, Exploring and Bringing
Transparency to the Black Box
LegalTech 2012 | February 1, 2012 | 1:45 – 3:00 PM
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2. The content of this presentation
is the property of its authors.
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information.
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3. Speakers
• David Horrigan, Esq., Analyst, eDiscovery and
Information Governance, The 451 Group
• David Leone, Esq., Director of Litigation Support
Services, Saul Ewing LLP
• Dr. Douglas W. Oard, University of Maryland
College of Information Studies
• Mike Stringer, Co-Founder & Managing
Partner, Datascope Analytics
Moderated by:
• Doug Stewart, Director of Technology, Daegis
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4. Many Things to Many People
• What’s in a name?
 Predictive Coding
 Computer / Machine / Technology Assisted Review
 Auto-Classification / Tagging / Categorization
 Clustering / Concept Searching / Iterative Search
• Is it Defined by the Technology Used?
• Is it Defined by a Workflow?
• Is it Defined by Human / Computer Division of Labor?
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5. TAR to the Rescue?
• Growing consensus that Traditional Exhaustive
Eyes-On Old-Fashioned Human Manual Linear
Document Review is no longer sufficient
 Cost of Review
 Increasing Data Volumes
 Time Required
 Risk / Quality
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6. TAR Acceptance / Adoption Indicators
• LTNY 2012 Superstar
 9 TAR Sessions and Panels (16% of total)
• Research and Studies
• Presentations / Webinars /Podcasts
• Columns / Articles / Blogs
• Vendors and Products
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7. TAR Acceptance / Adoption
• “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.”
 Judge Andrew Peck, Search Forward, 10/01/2011 Law
Technology News, law.com
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8. TAR Acceptance / Adoption
Only thing missing seems to be widespread use.
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9. Perceived Obstacles to Acceptance
• Black Box Technology
• Technology can’t be explained (by attorneys)
• Is it defensible?
• Is it as good as eyes-on review?
• Lack of transparency
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10. What Does “Better” Mean?
D
“Better” Technique
Increasing
Success
(finding
relevant “Baseline” Technique
documents) A
C
y
B
x
Increasing Effort
(time, resources expended, etc.)
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11. Document Review
Case Knowledge
The
Black
Box
Unprocessed Coded
Documents Documents
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12. Inside Yesterday’s Black Box
Case Knowledge
Unprocessed Coded
Documents Documents
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13. Inside Today’s Black Box
Case Knowledge Keyword Search & Linear Review
“Reasoning”
“Representation”
“Interaction”
Unprocessed Coded
Documents Documents
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14. Inside Tomorrow’s Black Box
Case Knowledge Technology Assisted Review
“Reasoning”
“Representation”
“Interaction”
Unprocessed Coded
Documents Documents
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15. Representation
Unit
Message, document, container, etc…
“Map”
Evidence Many ways to do this
Content: what is IN it
Context: who SAID it to WHOM (and WHEN)
Description: what is SAID ABOUT it
Behavior: what is DONE WITH it
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16. Reasoning
Having a map improves the machine’s ability
Search to reason about documents.
Boolean queries,
example documents
Similarity in “map”
Similar documents receive similar coding
Based on content, context, behavior or description
Many ways to do this
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17. Interaction
User Interface
What can the user say?
What can the user see?
A bridge between
human thought and
Review Process machine reasoning
How does a user
move between
seeing and saying?
Review Workflow
How does a review team
allocate functions between
team members and the
systems that they use?
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18. TARgeting Your Firm
• Do Your TAR Homework
• Know Your Case
• Inform Your Legal Team
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19. Do Your TAR Homework
• Technology
 Demo the different technologies and workflows.
 Ethical Rules - Understand impact for attorneys.
• Process
 Find a process you are comfortable managing.
 Does it work within your larger process?
• People
 Find a vendor who is knowledgeable and competent.
 Educate your staff and users.
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20. Know Your Case
• Client Concerns
 Is there a legal budget? Does this fit?
 Does the client use review technologies?
• Case & Production Timelines
 Training the “brain” takes time.
 Murphy’s Law - Account for a new workflow.
• Document Volumes
 Does the volume justify the initial setup time & expense?
 Does the content & number of custodians fit?
• Review Goals
 Responsiveness, Privilege, Issues
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21. Inform Your Legal Team
• Approach
 Find a champion before you choose a case.
 All cases are not equal - wait for the right opportunity.
• Position
 Use analogies to current technologies.
 Develop a terminology and stick to it.
• List Risks and Benefits
 Explain both the upsides and the downsides.
 Have a defensibility plan at the ready.
• Expectations
 Define TAR’s role within the review workflow.
 Be prepared to LOWER their expectations.
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22. Is it reasonable?
• Yes, if we followed a reasonable process.
 Staffing
 Training
 Quality assurance
Linear Review
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23. Is it reasonable?
• Yes, if we followed a reasonable process.
 Indexing
 Query design
 Sampling
•Keyword Search
Linear Review •Linear Review
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24. Is it reasonable?
• Yes, if we followed a reasonable process.
 Rich representation
 Explicit & example-based interaction
 Process quality measurement
•Keyword Search Technology Assisted
Linear Review •Linear Review Review (TAR)
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25. Lessons Learned
• The technology is still evolving
 Be flexible to emerging best practices
• Recruit an associate expert and ally
 Act as a liaison and stay informed in the review
• Do not oversell the technology
 Manage Expectations
• Don’t forget about legacy technologies
 Leverage what you pay for in batching and review
• Any tool is only as good as the workflow
 Develop one before you begin review!
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26. “We are stuck with technology when what we really want is
just stuff that works.”
-Douglas Adams
The Salmon of Doubt:
Hitchhiking the Galaxy One Last Time
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28. Thank you!
Questions?
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