Predictive Coding aka Technology Assisted Review aka Computer Assisted Review

25 Pages·2015·2.31 MB·English

Predictive Coding, TAR, CAR
NOT Just for Litigation
Olivia Gerroll – VP Professional Services, D4
February 26, 2015
Agenda
Drivers
•
The Evolution of Discovery Technology
•
Definitions & Benefits
•
How Predictive Coding Works
•
The Ripple Effect
•
Data and Information Governance
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Implications
•
Use Cases & Considerations
•
Selection & Technologies
•
How Do Lawyers and Court View PC?
•
Resources
•
Q&A
•
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Drivers – Big Data - Growing FAST
HOW MUCH DATA IS A PETABYTE
www.d4discovery.com
Drivers - The Technology is Not Unique
As you select, the application
zeroes in on what you like.
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Evolution of Discovery Technology
1990 1995 2000 2005 2010 2015
Stand Alone Review Apps
• •Web Based Review •“All in One” Litigation
Support Platforms
Document Imaging “ASP or SaaS”
• •
•OCR •Auto Coding •Social Media and
Cloud Collection
Email Thread & Near
Client/Server Review Tools •
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Dupe Detection
“Managed Services”
•
Computer Forensics
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Conceptual Search
•
Predictive Coding &
•
“Automated Litigation Support”
• Visualization Systems
• Assisted Review
Tape Restoration Clustering & Categorization
• •
Natural Language
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Legal Hold Management Applications
•
Artificial
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Intelligence?
www.d4discovery.com
TAR and CAR
Technology-Assisted Review (TAR), or Computer-
•
Assisted Review (CAR), is the use of advanced
information retrieval technology that helps make the
identification and review process more efficient
TAR/CAR uses components of existing technologies
•
to organize and sort documents by priority or
relevance
What differentiates TAR/CAR from other
•
technologies are concept-based search engines and
application of quantitative analysis.
www.d4discovery.com
Predictive Coding Defined
Predictive Coding is one type of TAR/CAR
•
Combines the efficiencies of concept search and statistics with the
•
knowledge of human beings
Uses an “active machine learning approach” or sometimes a
•
“support vector machine” to distinguish relevant from non-relevant
documents, based on decision made by a subject matter expert
Uses established statistical principles to measure status and
•
accuracy
The technology can be used for applying Information Governance
•
(IG) within a firm to both structured and unstructured data.
One key component of predictive coding that differs from
•
searching analytics is the methodology for training the technology
that is used to automatically classify records and improve the
accuracy and self-learning of predictive coding technology.
www.d4discovery.com
Benefits
“In the normal course of business” documents
are not organized by relevance
With a predictive coding approach a Subject
Matter Expert trains the software by coding
individual documents responsive or not
responsive, as the system samples the
population
Software calculates relevance scores for each
document based on relevance
www.d4discovery.com
How It Works
Matter expert is assigned to train the engine.
•
The software initially selects a random sample of documents.
•
The expert identifies relevant documents in the sample.
•
The software analyzes the expert’s input and creates a profile for
•
relevant and irrelevant documents.
The software generates new samples, each time learning more
•
from the expert’s input.
The process repeats until the software determines it has
•
sufficient information to scores all of the documents.
The scores are then used to make informed decisions about the data
•
management.
www.d4discovery.com
Predictive Coding Workflow - Discovery
www.d4discovery.com