Getting started using technology assisted review can be difficult if lawyers aren't used to this type of technology. Part 4 of this webinar series provides in depth coverage on how to get started with TAR tools.

1. Demys&fying
Technology
Assisted
Review
Sonya
L.
Sigler
Part
4:
Ge@ng
Started

2.
Demys&fying
Technology
Assisted
Review
Agenda
 Overview
of
TAR
Spectrum
 Your
Purpose
for
Using
TAR
 What
the
TAR
Technology
Does
 The
TAR
Balancing
Act
 People
 Process
 Technology
 QuesBons

3.
Demys&fying
Technology
Assisted
Review
Overview
of
TAR
Spectrum
Linear
Review
Culling
IteraBve
search
Review
Accelerated
Review
Email
Threading
Near
Duplicate
DetecBon
RA
-­‐
Clustering
CategorizaBon
(Supervised)
Automated
Review
Relevance
Ranking
Machine
Learning
Latent
SemanBc
Indexing
(staBsBcal
probability)
PaPern
Analysis
Sampling
Data
for
High
Precision
and
Recall
Rates
Per
Document
Cost
Organiza3on
Commitment

4.
Demys&fying
Technology
Assisted
Review
Underlying
Technologies
StaBsBcal
-­‐
#s
based
LinguisBc
–
word
based
Key
word
Search
Ontologies
Lucene
dtSearch
Other
Search
Engines
Rules
Based
Systems
Bayesian
ClassificaBon
Latent
SemanBc
Indexing
Support
Vector
Models

5.
Demys&fying
Technology
Assisted
Review
Your
Purpose
for
Using
TAR
 BePer,
Cheaper,
Faster
 CombinaBon
 All
3…
Money
(Cheaper)
Time
(Faster)
Accuracy
(BePer)

6.
Demys&fying
Technology
Assisted
Review
Purpose
of
the
TAR
Technology
Itself
 Learn
from
case
expert
 Propagate
informaBon
to
enBre
data
set
 Provide
Forum
 Quality
Control
of
the
Training
 Quality
Control
of
the
PropagaBon.

7.
Demys&fying
Technology
Assisted
Review
Balancing
Act
 Technology
 Process
 People

8.
Demys&fying
Technology
Assisted
Review
Technology/Data
 TAR
Tools
 Data
Types
 Data
Size
 Types
of
Cases

9.
Demys&fying
Technology
Assisted
Review
TAR
Tools
 Seed
Set
 Training
Set
 Control
Set

10.
Demys&fying
Technology
Assisted
Review
Data
Types
 Text
based
 Meta
data
 Images
 Excel
files
 Power
point
 Funky
file
types
–
normalize
 Sun
 Mac
 Online
(IMs,
Facebook,
Sharepoint,
Wiki)

11.
Demys&fying
Technology
Assisted
Review
Data
Size
 #
of
GBs
 #
of
Files
 #
of
Each
File
Type
 Families

12.
Demys&fying
Technology
Assisted
Review
When
to
Consider
TAR?
 Timeline
Pressures
 2nd
Requests
 M&A
TransacBons
 Understanding
Your
Data
 InvesBgaBons
(Internal,
Government,
Regulatory)
 Advanced
Analysis
 AnBtrust
Cases,
Complex
LiBgaBon
 ProducBons
 Costs
 Mere
Compliance
Trumps
 Large
Data
Sets,
MulBple
Data
Sets
 ProporBonal,
Managed
Costs

13.
Demys&fying
Technology
Assisted
Review
Low
Risk
Case
Types
for
Gebng
Started
 Measure
Linear
Review
Accuracy
 Internal
InvesBgaBons
 ProducBons
 Opposing
ProducBons

14.
Demys&fying
Technology
Assisted
Review
Case
Type:
InvesBgaBons
 People
Involved
 Subject
MaPer
 Bad
Behavior

15.
Demys&fying
Technology
Assisted
Review
Case
Type:
ProducBons
 Time
is
of
the
Essence
 Volume
is
Ever
Expanding
 2
Case
Studies
 1.7
M
docs
–
narrow
review
set
 3
weeks

16.
Demys&fying
Technology
Assisted
Review
Case
Type:
ArbitraBon
-­‐
3
Weeks
366,960
Total
documents
108,750
docs
–
IteraBve
Keyword
Culling
92,067
to
Zoom
13,844
Reviewed
Aner
Zoom
258,210
sampled
16,683
to
Linear
Review
~1,000
+
families
~2,000
+
families
24,209
Docs
Reviewed
2,026
Docs
Produced
2,440
docs
trained

17.
Demys&fying
Technology
Assisted
Review
Case
Type:
ArbitraBon
(1.7m
Docs)
1,713,860
Total
documents
1,646,062
docs
to
Zoom
112,285
Junk
File
56,949
Junk
Domain
Analysis
137,000
Reviewed
Aner
Zoom
File
types,
size,
no
text
1,600
Docs
Trained
Ongoing
NegoBaBons
Ltd
Custodians
~1.1M
docs
Sampling
Below
the
Cutoff
–
500
docs
(95%
+/-­‐
4.38%)
~600K
docs
Above
the
Cutoff
–
Review
Priv
Screen
Docs
Only

18.
Demys&fying
Technology
Assisted
Review
Case
Type:
Opposing
ProducBons
 Time
is
of
the
Essence
 What
is
There?
 What
is
Missing?
 Re-­‐use
Training
 Your
ProducBon
Training

19.
Demys&fying
Technology
Assisted
Review
TAR
Workflow/Process
 PrioriBzed
Review
 Culling
(Responsive
v.
Not
Responsive)
 Privilege
Screen
 Quality
Control
Privilege
Screen
Non-­‐
relevant
Relevant

20.
Demys&fying
Technology
Assisted
Review
Equivio
Zoom/Relevance
Workflow
Machine
Categoriza&on
Random
Sample
Itera&on
Random
Sample
Expert
Reviews
Sample
Responsive
=
1
Non-­‐Responsive
=
0
Relevancy
Ranking
25-­‐50
Rounds
of
40
documents

21.
Demys&fying
Technology
Assisted
Review
How
to
Decide
What
to
Review,
Sample
or
Ignore?
Privilege
Screen
Privilege
Screen
Privilege
Screen
Non-­‐
relevant
Non-­‐
relevant
Non-­‐
relevant
Relevant
Relevant
Relevant
Review
Sample
Accuracy
MaPers
Cost
MaPers
Time
MaPers
Legend

22.
Demys&fying
Technology
Assisted
Review
Process:
PrioriBzed
Review
 Review
It
ALL
 0
v
1
 0
-­‐
>
1
 But
How?
 Priority
Batching
 Ranking/Scores
 Topics
 Email
Threads
 Deduplicated
Sets
 Why?

23.
Demys&fying
Technology
Assisted
Review
Process:
Culling
Non-­‐responsive
v
Responsive
Where
is
the
Cutoff
Point?

24.
Demys&fying
Technology
Assisted
Review
24
Ignoring,
Sampling,
or
Reviewing
Documents
that
are
highly
likely
to
NOT
be
relevant
Documents
that
could
go
either
way
Documents
that
are
highly
likely
to
be
relevant

25.
Demys&fying
Technology
Assisted
Review
Process:
Privilege
Screen
 How
much
data?
 How
is
it
PrioriBzed
for
Review
 Who
Reviews
this
Data?

26.
Demys&fying
Technology
Assisted
Review
Process:
Quality
Control
 What
was
Len
Behind?
 Review
 Sampling
 Nothing
 What
is
Moving
Forward?
 Produce
 Sample,
then
Produce
 Review,
then
Produce
Privilege
Screen
Non-­‐
relevant
Relevant

27.
Demys&fying
Technology
Assisted
Review
People
 Case
Expert
 Project
Manager
 Defensibility

28.
Demys&fying
Technology
Assisted
Review
Who
Trains
the
TAR
Tool?
v.

29.
Demys&fying
Technology
Assisted
Review
People:
Case
Expert
 Who
 Why
 What
Do
They
Do?
 How
Long
Does
It
Take?
 Benefits

30.
Demys&fying
Technology
Assisted
Review
Assessment
and
Training
View
in
Equivio
Document
List
Document
Contents
Ranking
PalePe
for
ranking
all
issues
at
once
Progress
status
Ranking
PalePe
for
ranking
issues
separately

31.
Demys&fying
Technology
Assisted
Review
People:
Project
Manager
 Who
 Why
 How
Involved
 Training
 ExperBse

32.
Demys&fying
Technology
Assisted
Review
People:
Defensibility
 Who
 ExperBse
 Why
 What
Do
They
Do?
 Reports
 Affidavits
 TesBmony

33.
Demys&fying
Technology
Assisted
Review
Defensibility
Report
 Document,
Document,
Document
 Transparency
 Workflow
 What
Was
Considered,
By
Whom?
 QC
Process
 Metrics

34.
Demys&fying
Technology
Assisted
Review
Delicate
Balance
 People
 Process
 Technology
Technology
SophisBcaBon
Level
Required
Subject
MaPer
ExperBse
Involvement
Elapsed
Time
Document
Review
Expenditure
Required
Human
Resources

35.
Demys&fying
Technology
Assisted
Review
Q&A - Thank you!
Sonya
L.
Sigler
Vice
President,
Product
Strategy
&
Consul&ng
SFL
Data
415-­‐321-­‐8385
sonya@sfldata.com
www.sfldata.com
Post
your
ques&ons
to
the
presenter
in
the
chat
secBon