WHITE PAPER

Choosing the righttechnology-assisted review
protocol to meet objectives
When used correctly, TAR has the potential to offer
tremendous savings, both in review time and cost,without sacrificing the quality of results

Contents
1. What is technology-assisted review?

3

2. TAR protocols and the progression from TAR 1.0 to TAR 2.0

4

3. TAR 1.0: One-time training 

5

4. TAR 2.0: Continuous active learning 

7

5. Key differences between TAR 1.0 and TAR 2.0 

8

6. Choosing the right protocol: Start with the end goal in mind 

97. When should TAR 2.0 be used? 

98. When should TAR 1.0 be used? 

11

9. Adapting to conditions: Combining aspects of TAR 1.0 and TAR 2.0

11

10. Additional considerations when choosing a TAR methodology 

12

11. Conclusion

12

About OpenText

13

Connect with us

13

Choosing the right technology-assisted review protocol to meet objectives

2/13

Using any technology-assisted review (TAR) protocol will undoubtedly reduce thetime and expense of reviewing electronic stored information (ESI) over traditional
linear review. But, getting the best results will depend on carefully matching
project objectives and constraints with the inherent strengths and weaknesses ofpredominant TAR techniques and, in some instances, combining TAR protocols.
This white paper provides the necessary background and identifies the pertinentconsiderations to facilitate selection of the appropriate TAR protocol for typical use
cases across the legal landscape.

1. What is technology-assisted review?
TAR, also known as predictive coding or computer assisted review, is a process
whereby humans leverage technology to efficiently identify specific documents
in a vast and disorganized corpus. Every TAR system encompasses human review
for a portion of a document collection to train computers that, in turn, extrapolate
those human judgments to the balance of the collection, enabling faster and morecost-effective review.
The Grossman-Cormack Glossary of Technology Assisted Review defines TAR as:“A process for prioritizing or coding a collection of documents using a computerized
system that harnesses human judgments of one or more Subject Matter Expert(s) ona smaller set of documents and then extrapolates those judgments to the remaining
document collection.”1
What exactly does this mean? Think of modern TAR systems as a music app for
documents. A music app’s goal is to find and play music that the listener likes,
interspersing songs from favorite artists or genres with new songs that share key
characteristics, known as “features.” While the music app has millions of songs in its
archive to choose from, it does not initially have any ability to guess what the listener
wants to hear—until it learns to do so.
It learns by extrapolating from as little as a single artist, song or genre identified as afavorite. Based on that fairly generic starting point, it then begins to choose additional
songs that have certain similarities. The reviewer provides what is known as relevance
feedback, grading its selections by clicking a “thumbs up” or “thumbs down” button.
Based on this training, the app’s algorithm analyzes a complex array of features, suchas melody, harmony, rhythm, form, composition, style and vocalist, to differentiate
the songs the reviewer likes from those he or she disliked. The more feedback thereviewer provides, the smarter the system gets. Eventually, a customized station willplay mostly music the reviewer enjoys, with only an occasional miscalculation.
The modern TAR process works similarly. The TAR algorithm learns, fromits human partner’s feedback, which documents are relevant, with algorithmic
judgments improving over a period of time. With TAR, a human reviews a document
and tags it as relevant or not relevant. While other tags are possible for otherapplications, for simplicity this section only discusses relevance searches.
In the background, a computer algorithm continuously observes the assigned tags
and uses that input, together with the features (typically, words and phrases) to makecomparisons between the tagged documents and the remaining documents in its set.
The algorithm then ranks every document in what it calculates to be the likelihood of
relevance, shuffling documents that are most likely to be relevant (e.g., the highest
ranked documents) to the top of the pile for human review, just as the music app
shuffles the songs it expects the listener will enjoy to the top of the playlist.

Choosing the right technology-assisted review protocol to meet objectives

3/13

This iterative process continues, cycling through review, analysis and ranking, until thereview is discontinued. The objective of the review determines how long the process
will continue, a decision that is made by the human review team, not the computer.
Of course, the objectives of TAR are considerably more serious than those of amusic app, so review teams must consider a variety of options, techniques andstrategies based on the goal.
When used correctly, TAR has the potential to offer tremendous savings, both inreview time and cost, without sacrificing the quality of results. With TAR, review
teams can work faster and process documents that are most likely to be relevant
first. A relatively simple sampling process within TAR, showing the percentage of
relevant documents found, can also give the review team a reasonable, defensible
basis for concluding a review when the search objectives have been satisfied.

2. TAR protocols and the progression from TAR 1.0 to TAR 2.0
There are three basic TAR protocols. Simple passive learning (SPL) and simple
active learning (SAL) are typically associated with early versions of TAR, now known
as TAR 1.0. With simple learning, the algorithm is trained by a human reviewer until
it develops a model of responsive documents that either stabilizes or reaches anacceptable level of quality. From that point on, the algorithm ceases learning anduses the information it gained in training to either classify or rank document sets.
SPL and SAL are differentiated by the set of documents they use for training. SPL
typically uses randomly selected documents to train the algorithm. SAL usually starts
with a set of clearly relevant and clearly non-relevant documents, often called the“seed set.” From there, an SAL protocol actively selects the “gray area” documentsin the collection for training, the ones that are most difficult to classify. This is called“uncertainty sampling.” For both of these protocols, all training is completed by
the SME at the beginning of the process, before review can begin in earnest. Oncethe algorithm stabilizes, training is complete, the review size is fixed and additional
review is not required to improve the model.
A newer protocol, continuous active learning (CAL), is central to the second
generation of TAR protocols, known as TAR 2.0. With CAL, the algorithm learns andimproves continuously throughout the review process. Instead of a preliminary
training phase, the human review team simply begins review while the algorithm
observes those decisions and adjusts its criteria for determining relevance. Everyreview decision, from the first to the last, is used to train and improve the algorithm,
ensuring that the most likely relevant documents are being ranked toward the top of
the list, so they can be preferentially made available to reviewers.
The market has largely shifted toward adopting TAR 2.0 due to a variety ofadvantages. In particular, CAL has been shown to reach higher levels of recall,
identifying a greater number of relevant documents more quickly and with lesshuman review effort than either of the TAR 1.0 methodologies.2 This allows
organizations to meet tight production timelines, leverage a limited staff of humanreviewers and minimize the bottleneck caused by the algorithm training process.
CAL can also readily accommodate both changes in the scope of discovery androlling data productions, since it continues training throughout the life of the review
process. Its benefits have inspired CAL applications that extend beyond outboundproductions, as discussed below.
But the rise of TAR 2.0 does not spell the end of TAR 1.0, nor does it eliminate
combining aspects of both protocols to achieve certain goals. Determining which
protocol may be the best fit for a particular matter depends on objectives and requiresa more detailed understanding of the various methodologies and preferred use cases.
Choosing the right technology-assisted review protocol to meet objectives

4/13

Both TAR 1.0 and TAR 2.0 operate through an iterative cycle of reviewing documents,
analyzing the results and managing the remaining documents. But, there are anumber of specific differences, all of which stem from one critical distinction. A TAR
1.0 algorithm stops training when it stabilizes, regardless of how many documents
are subsequently reviewed, whereas a TAR 2.0 algorithm is trained by every coding
decision until the review stops. As a side note, the reader may see reference tofuture generations of TAR, such as TAR 3.0 or even predictive coding 4.0 systems,
but they actually fall under the TAR 2.0 ambit. They are all based on a CAL protocol,
discussed below, and modified to accommodate different training techniques. Neither
is discussed in this white paper.
This white paper will next take a closer look at the workflows for TAR 1.0 and TAR 2.0.

3. TAR 1.0: One-time training
Figure 1 below is a diagram of a typical TAR 1.0 process, from the collection of the
document set through the final review.

Control set

SME

Train

Test

Rank all documents
and establish cutoff

Transfer toreview platform

Seed set

Collect/receive

This is how a typical TAR 1.0 process works:
1. Collection. The first step in the protocol is to amass and process the entire
collection of documents subject to review. From the TAR perspective, processing
entails breaking each document into features (most often words or phrases) thatwill be used by the TAR algorithm to compare and rank or classify the documents
for review purposes. And, as discussed below, because most TAR 1.0 systems
depend upon a control set, it is critical to amass the entire collection before
review begins. Otherwise, it may be necessary to re-initiate the entire TAR
1.0 process, particularly when new documents addressing new concepts areadded to the collection, such as engineering documents added to a collection ofprimarily sales documents.
2. Control set. The next step in the protocol is to draw a random sample, typically500 or more documents, that will be set aside and used as a control set to monitor
progress and will not be used to train the algorithm. Before anything else canbe done, the control set needs to be reviewed and coded by a subject matter
expert (SME), usually a senior lawyer on the case. It is particularly important tohave an SME review the control set, because it operates as the answer key or“gold standard” against which the algorithmic model will be compared to evaluate
progress throughout the TAR process. This means it needs to correctly reflect
the appropriate notions of relevance. And, to be effective, the control set must be
representative of the entire collection of documents being reviewed, which is why
the collection needs to be complete at the outset.
Choosing the right technology-assisted review protocol to meet objectives

5/13

3.Seed set. The need for a seed set in a TAR 1.0 process depends upon whether it
follows an SAL or SPL protocol. As an SPL protocol depends only upon randomlyselected documents to train the algorithm, there is no need for a seed set toinitiate training. SPL, on the other hand, uses uncertainty sampling techniques to
identify appropriate training documents. Before an SPL algorithm can find that
uncertainty boundary, it needs to have some idea of what is considered relevant
and what is considered non-relevant. That information comes from the review
and coding of a seed set that provides good examples of both relevant and nonrelevant documents. Typical SAL algorithms perform better, with roughly 50
relevant and 50 non-relevant examples in the seed set. As with the control set,
the seed set needs to be coded by an SME to ensure accurate decisions and, inturn, appropriate selection of training documents.
4.Training. Once the control set, and perhaps the seed set, have been reviewed
and coded, the SME continues the training process by reviewing batches of
documents selected by the TAR engine, either randomly (SPL) or throughuncertainty sampling (SAL). Each document is tagged as relevant or nonrelevant. The training rounds typically involve review of between 1,500 and 5,000
documents. This training takes time. Assuming a reasonable review rate of 60
documents per hour, it will likely take the SME more than 65 hours just to stabilize
the algorithm before review can start in earnest.
5. Ranking and testing. Periodically throughout the training process, the TAR
algorithm analyzes the SME’s tags and modifies and improves its relevancemodel. The algorithm typically tests the model by applying it to the documents inthe control set to see how well it matched the SME’s judgments.
6. Stability. Training, ranking or classification and testing continue until thealgorithm’s model is “stable.” That means it no longer improves identifying
relevant documents in the control set. For example, say the model correctly
identified 75 of the 87 relevant documents in the control set. Over a few morerounds of training, the results do not improve, which generally means that, evenwith additional training, the algorithm will not get any better at finding relevant
documents in the control set and, presumably, will be as good as possible whenapplied to the collection.
7. Rank or classify the remaining documents. When training is complete, the next stepis to run the model against the entire document population. Doing so can take several
hours depending on the system, or it may need to run overnight. This is a one-timeranking or classification based on SME training. Once the algorithm finishes ranking
or classifying the collection, the algorithm is not given any more documents fortraining and can no longer improve based on further tagging by the review team.
8. Generate and validate the presumptively relevant set. Once the algorithm
is applied to the entire collection, it will be split into two subsets: one that ispresumptively relevant and one that is presumptively non-relevant. The documentsthat are presumptively non-relevant, called the null set, will generally be discarded
and will not be reviewed any further. The presumptively relevant set may or may not
be reviewed, as discussed below. There are two predominant methods for checking
to validate the presumptively relevant set, ensuring that it has a sufficient numberof responsive documents to meet any recall objectives. Often, the control set isused to set a cutoff. For example, if the user wanted to produce 80 percent of therelevant documents, they must find the rank in the control set where 80 percentof the relevant documents were located and simply produce everything abovethat rank. Otherwise, and particularly for classification algorithms, the user cantake a random sample of both the presumptively relevant set and null set anddetermine the fraction of the total number of relevant documents found.
Choosing the right technology-assisted review protocol to meet objectives

6/13

9. Conduct the review. Once complete, the review team may be directed to lookat the presumptively relevant documents or decide to produce those documents
without further review. The user can also do a prioritized review, where theteam looks at all of the documents collected based on their relevance ranking.
That accomplishes two goals. First, if relevant documents are pushed to the topof the ranking, the team will see documents that are more likely to be relevant
first. Second, once the team runs out of relevant documents, it can move quickly
through the nonrelevant ones without fear of missing something important.

4. TAR 2.0: Continuous active learning
As pointed out in Figure 2 below, continuous active learning (CAL) is the hallmark
of a TAR 2.0 protocol. A CAL system continually learns as the review progresses
and regularly re-ranks the document population based on what it has learned tomove the most likely relevant documents to the top. As a result, the algorithm getssmarter and the team reaches its goal sooner, reviewing fewer documents than would
otherwise be the case with one-time training.

Rank
ECA/analysis

Continuous
active learning
Train/review

Pre-production
Review/train

Post-production
and more
OutputCollect/receive

Test

Here is how the TAR 2.0 protocol works:
1. Collection. As with TAR 1.0, the first step in the TAR 2.0 protocol is to amass
and process a collection of documents, making the features of the documents
available to the TAR algorithm. However, because CAL continuously ranks theentire document collection and training takes place throughout the review, it is
not necessary to gather the entire collection before review begins. Engineering
documents will simply be folded into the collection of sales documents and ranked
based on the features of every document coded to that point in time. And, if they
are relevant, the engineering documents will eventually be ranked near the top of
the list and come up for review in due course.
2. No control set required. A control set is not necessary and not used in a
TAR 2.0 protocol.
3.Initial seeding. The user can initiate a TAR 2.0 protocol with as many, or as few,
documents as desired. One of the best ways to initiate ranking is to start byfinding as many relevant documents as possible and feed them to the system tohelp train the algorithm or create a synthetic document to use as an initial seed.
The user can even begin without any seed documents—just start reviewing, andthe algorithm will learn based on every relevant and non-relevant document theuser codes. Random sampling is generally not recommended for the purpose of
initial training, since it is it is not necessarily an efficient means of finding relevant
documents and is particularly problematic for low richness collections.

Choosing the right technology-assisted review protocol to meet objectives

7/13

4.Begin review. The review team can start immediately; there is no need for a
subject matter expert to review any documents whatsoever. Reviewers willquickly begin seeing batches containing mostly relevant documents.
5. Quality control. As the review progresses, the subject matter expert, such as
the senior attorney, can cull a small percentage of the documents to ensure thatthe reviewers are aligned with the proper scope of relevance. An effective TAR
system will include a quality control algorithm that locates and presents those
documents that are most likely tagged incorrectly.
6. Finish. The user continues until the desired recall rate is reached. They can trackprogress as the review progresses to see when it is time to stop.
The user can demonstrate success through a random sample of the unseen
documents, called an “elusion sample.” It will show how many relevant documents
the user may have missed, from which recall can be calculated, as well as where
one is in the review and, where appropriate, how many more documents are needed
to reach the goal.
The process is flexible. Users can start with as many training seeds as they like orcreate a synthetic document. After the initial ranking, the team can get going on thereview. As they complete batches, the ranking engine takes their new judgments
into account and keeps getting smarter.

5. Key differences between TAR 1.0 and TAR 2.0
The TAR 1.0 process comes with a number of practical problems that limits
its effectiveness.
TAR 1.0 requires SMEs for training. With TAR 1.0, to make certain that the algorithm
correctly reflects the pertinent characteristics, an SME must review thousands of
documents before the algorithm is ready to use. This creates a bottleneck where
discovery cannot proceed until the SME—whose time is likely both limited andexpensive—has spent 60 or more hours training the TAR system.
The SME must train the TAR 1.0 algorithm until it stabilizes. In document
collections with low richness or numerous distinct issues, this may require the SME
to review thousands of additional documents.
You only get “one bite at the apple.” The better the algorithm is trained, the moreaccurately it can identify the most likely relevant documents and classify them, or
rank them highly, so they ultimately end up in the presumptively relevant set. Yet,with TAR 1.0, the training period is limited, so the algorithm cannot incorporate
additional feedback or continue to improve. Because the bulk of review occurs after
the TAR 1.0 algorithm has evaluated and segregated the collection, lessons learned
during review cannot inform the algorithm’s operation.
TAR 1.0 is not flexible or adaptive. Because the TAR 1.0 algorithm is fully trained before
review begins, it does not accommodate changes to the scope of discovery that occur
during review, such as the addition of documents in a rolling production. It also cannot
adapt to an evolving understanding of the case or the legal issues involved.
The structure of TAR 1.0 invites legal challenges. Opponents may object to theseed set or the protocol that was used to train the algorithm. Because training
is a limited process, weaknesses in its foundation are both readily apparent andpotentially fatal.

Choosing the right technology-assisted review protocol to meet objectives

8/13

TAR 2.0 solves many of these challenges
Rather than using an SME, a control set or a seed set, with CAL the human review
team simply begins review while the algorithm learns in the background, analyzingtags and developing its sense of which documents may be relevant. The morecomprehensive the initial relevant documents, the faster the algorithm will learn. The
algorithm continuously ranks the entire document set, feeding more and more relevant
documents to the human review team and continuously learns, adjusting and adapting
throughout the entire review process.
TAR 2.0 eliminates the bottleneck caused by SME training, freeing senior attorneys
to focus on finding relevant documents for training using other analytics andrunning quality control checks. CAL’s continuous ranking and learning also obviate
the problems of low data richness, rolling productions and changes to the scopeof discovery. Finally, because there is no defined seed set to evaluate, TAR 2.0
minimizes the degree to which the algorithm training protocol can be challenged.
Instead, all review is training and all training is review.
There is a characteristic dichotomy in the operation of both TAR protocols. A TAR
1.0 protocol will “train” more quickly than a TAR 2.0 protocol. In other words, if theuser did the initial training of a TAR 1.0 algorithm and a TAR 2.0 algorithm with thesame fixed number of documents and then just quit training, it would be necessary
to review more documents using the TAR 2.0 ranking than the TAR 1.0 ranking.
However, since a TAR 2.0 system never really stops training, it will eventually bemore efficient than a TAR 1.0 algorithm. So, while TAR 1.0 may train more quickly
than TAR 2.0, TAR 2.0 will ultimately be more efficient.

6. Choosing the right protocol: Start with the end goal in mind
Given this dichotomy, when starting a TAR review, ask: “Do I intend to review every
document that will be produced?” If the answer is yes, the review team will review
fewer documents using a TAR 2.0 protocol. If the answer is no, the team will review
fewer documents using a TAR 1.0 protocol. Indeed, the user will only review thecontrol set, possibly the seed set and enough documents to train the algorithm tostability, but undoubtedly more non-responsive documents will be produced.

7. When should TAR 2.0 be used?
The short answer is that TAR 2.0 should be used for the majority of review andproduction tasks. The success of any review can be measured by balancing the recall
or completeness of the results (the percentage of relevant documents identified)with their precision or purity (the percentage of retrieved documents that are actually
relevant). Typically, a technique that increases one of these metrics will decrease
the other, so it helps to be explicit about the goals of review from the outset. TAR
2.0 using CAL can be deployed rapidly to maximize either recall or precision, whichmakes it amenable to a wide range of use cases.
Classification tasks
The most familiar application of TAR is still the classification of an outboundproduction for eDiscovery. While this review should strive for reasonably highpercentages of recall and precision, it is guided by the principles of reasonablenessand proportionality, not perfection. Recall is valued more highly than precision, but
a modest target of 80 percent recall is a common standard, enabling the cost andeffort of the search to remain proportionate to the value of the case. TAR 2.0 excels inclassification tasks due to the rapid results it produces and its ability to immediately
focus human review efforts on the relevant documents. As mentioned above, CAL is
Choosing the right technology-assisted review protocol to meet objectives

9/13

particularly useful in outbound production instances where there will be rolling uploads,
or where the scope of discovery is anticipated to be complex or evolving.
Knowledge generation tasks
In investigations, early case assessment (ECA) and review of received production sets,
the goal is knowledge generation rather than classification and time is of the essence.
Knowledge generation tasks seek the best documents, such as those with the mostinteresting content and essential portions of the story. Precision is therefore key,
while recall is relatively unimportant. In other words, users want to avoid reviewing
documents that are not relevant wherever possible. Because TAR 2.0 skips thelaborious and time-consuming training process or, more accurately, subsumes
training into review, TAR 2.0 starts providing insights much more quickly than TAR 1.0.
This allows reviewers to discover useful information and start discerning stories within
the data almost immediately. By comparison, a TAR 1.0 SAL protocol focuses almost
exclusively on documents for which relevance is uncertain, limiting the number of truly
relevant documents that are available for review until the system has been fully trained.
One note though, all TAR methodologies rank documents according to how likely
they are to be relevant, not how inherently interesting they are. While an unusual
or atypical document may be highly valuable for reconstructing the story of thecase, it might not be recognized by a TAR algorithm because it is so different fromanything else that has been reviewed. That is especially true for a TAR protocolthat relies on simple learning. Consequently, TAR 2.0 vastly outperforms TAR 1.0 forknowledge generation tasks.
Investigations present additional challenges that overwhelm TAR 1.0’s capabilities.
Unlike litigation, there are no fact-laden complaints to focus an investigative searchand no broad seed sets to aid in training. This paucity of exemplars is not a problem
for CAL algorithms, which can begin locating the majority of pertinent documents
based on a single positive seed document. That single document may even be asynthetic seed, such as a recitation of known facts or a string of keywords generated
to reflect the key language and concepts sought in the search.
When reviewing opposing party productions, the objective is to weed through thecollection efficiently to identify particularly relevant documents. Rapidly surfacing
those “hot” documents is another precision-oriented task that suits a CAL algorithm.3
Since CAL can be initiated from a synthetic seed laced with the most critical detailsof the information that is being sought from the opposing party production, a TAR 2.0
algorithm will quickly recognize the features that make a document “hot” and elevate
those for prompt review. And, if additional issues are discovered during review,
the TAR 2.0 algorithm, unlike a 1.0 system, will seamlessly incorporate those into its
relevance calculation.
Protection tasks
By contrast to other document review tasks, the objective of protection tasks is theabsolute identification and protection from disclosure of certain types of information,such as privilege, trade secrets or confidential information. This essentially demands
100 percent recall, without exception, while precision is less important. The ability
to rapidly develop a CAL algorithm using privileged documents makes TAR 2.0 an
excellent option for conducting a privilege review when the bulk of documents will be
produced without eyes-on review, as in a second request or a subpoena response.
The TAR 2.0 review will quickly elevate likely privileged documents for withholding
and the privilege review can cease once it appears that the algorithm is not finding
any additional privileged documents.

Choosing the right technology-assisted review protocol to meet objectives

10/13

However, the best way to maximize recall, e.g., to protect sensitive documents fromdisclosure, is to stack different techniques, rather than relying on one technique, aseach methodology is prone to its own type of mistakes. Human reviewers, for example,
tend to make random mistakes on individual documents, while TAR systems often
make systematic errors, getting entire classifications of documents right or wrong.
Combining different approaches, by layering TAR 2.0 with other review methodologies,
such as keyword searching, eliminates the gaps inherent in each approach.

8. When should TAR 1.0 be used?
Although TAR 2.0 is the more efficient and appropriate solution for many review
scenarios, it should not be the only review tool in an organization’s arsenal. There
are times when a TAR 1.0 approach, or a hybrid approach that combines thebenefits of both SAL and CAL (discussed in the next section), is either preferable oressentially mandated. Organizations subject to a variety of eDiscovery obligations
for a wide range of requesting parties should keep multiple options at the ready.
In classification tasks requiring outbound production, TAR 1.0 works particularly
well for reasonable, cost-effective efforts to rapidly identify and produce requested
documents without requiring a high level of recall or precision or the need to review
all of the documents being produced. Because TAR 1.0 trains more quickly than TAR
2.0, TAR 1.0 is both cost-effective and efficient when the primary consideration is
not technical perfection, but rather compliance with an affirmative duty to makereasonable efforts to find and produce requested documents.
The typical scenarios for which TAR 1.0 might be particularly useful include HartScott-Rodino Second Requests and third-party subpoenas. When responding to agovernment second request, the documents sets are typically massive, with broad
responsiveness criteria and very tight deadlines that make reviewing the entire
production set impractical. In the third-party subpoena context, cost is the primaryconsideration, which means the review team wants to minimize the number of
documents that need to be reviewed. And there is typically little interest in the true
substance of the documents, since they are being produced in a litigation to which
they are not a party. In both situations, the review team will often not be able to,or even want to, examine every document. The ability to train a TAR 1.0 algorithm
quickly and inexpensively and generate a reasonable production set makes TAR 1.0
a particularly suitable alternative.
There are also situations where using TAR 1.0 may be inevitable, because of reviewing
party demands and the inability to effectively negotiate any alternatives. For
example, some regulatory and government agencies essentially “mandate” a TAR 1.0
approach, much like the approach taken by the Department of Justice in its publishedreview protocol.4 Typically, in those scenarios, the success of the TAR review is tied to
TAR 1.0 statistics, making it difficult to comply with a TAR 2.0 approach. Additionally,
although courts should not generally be involved in designing review methodologies,
some courts, as well as counsel, lack familiarity with CAL, which has the tendency tofocus the discussion of protocols on a TAR 1.0 approach.

9. Adapting to conditions: Combining aspects of TAR 1.0
and TAR 2.0
In some cases, legal teams need to remain flexible during a review project and pivot
workflows between TAR 1.0 and TAR 2.0 approaches. Circumstances might include
scope changes, risk tolerance and changing deadlines. Alternatively, users may
want to use elements of the TAR 1.0 workflow in a review tool that does not have it
built in. For example, a legal team may have originally believed that there was a fairly

Choosing the right technology-assisted review protocol to meet objectives

11/13

low risk in producing documents without human review and, as such, implemented a
TAR 1.0 workflow. After some review has taken place, the risk assessment may havechanged based on the documents found to date. With the flexibility to switch to a TAR
2.0 workflow, all work product done to date can be used without restarting the entire
process. Conversely, if a review team started a TAR 2.0 workflow based on the need toreview each document being produced, but timelines have changed to the extent that
it is no longer feasible, using elements of a TAR 1.0 workflow could make sense. Thismay be referred to as a TAR 1.5 workflow.

10. Additional considerations when choosing a
TAR methodology
Protocol negotiation. As mentioned above, a party may not always be in aposition to negotiate the TAR protocol used in a particular production. Dependingon the opponent or regulatory authority making the request and the strength of theuser’s position, a legal team may have to accede to a protocol that has been largely
determined by someone else.
Transparency obligations. Be mindful that the TAR protocol chosen—and thedegree of transparency about how it is being used—could expose an organization
to unexpected risk. Especially with TAR 1.0, when operating details of the training
and search protocols are shared with an opponent or court, any later adjustment ordivergence that the user makes from that plan might trigger a legal challenge. Clear
and accurate communication is always appropriate in eDiscovery, but users should
not feel compelled to overexplain their TAR methodology. After all, review processes
were largely confidential in traditional paper-based discovery—an approach thatsome courts have held is worth emulating with TAR.5

11. Conclusion
Vendors and proponents of TAR technology tend to present the choice between
TAR 1.0 and TAR 2.0 as a binary decision that must be made wholesale, across allcases, using distinct eDiscovery systems. This is no longer true. Legal teams today
can adopt bespoke methodologies and workflows that offer both TAR 1.0 and TAR
2.0 , even within the same eDiscovery platform platform.
Finally, whatever approach is chosen, remember that under both the ABA Model
Rules of Professional Conduct and most states’ ethical rules, attorneys havean ethical obligation to understand the risks and the benefits of all relevant
technology.6 By improving one’s understanding of the various TAR methodologies
and their suitable use cases, there is confidence in knowing ethical obligations in
the process have been satisfied.
OpenText™ provides proprietary technology-assisted review technology, bestpractices guidance and support for each client project. The OpenText InsighteDiscovery and investigations platform offers both Insight Predict, TAR 2.0 basedon CAL, and Cut Point Review, TAR 1.0, within the same UI for ease of use, validationand production. OpenText Axcelerate includes TAR 2.0 based on continuousmachine learning, with workflows that can incorporate both TAR 1.0 and 2.0 basedon a client’s objectives.
OpenText also provides supporting end-to-end managed document review services
leveraging technology-assisted review for the most expedient, accurate and costeffective review possible.

Choosing the right technology-assisted review protocol to meet objectives

12/13

Sources:
1. The Grossman-Cormack Glossary of Technology Assisted Review (Fed. Courts L.
Rev. 2013, Vol. 7, Issue 1).
2. Gordon V. Cormack and Maura R. Grossman, “Evaluation of machine-learning
protocols for technology-assisted review in electronic discovery” (2014).
3.OpenText Insight Predict case study, "Using TAR to Find Hot Docs for Depositions."
4.U.S. Department of Justice, Antitrust Division, Predictive Coding Model Agreement.
5. Dynamo Holdings Ltd. P’ship v. Comm’r of Internal Revenue, Nos. 2685-11,
8393-12, 143 T.C. No. 9 (Tax Sept. 17, 2014) (noting that “the Court is not normallyin the business of dictating to parties the process that they should use when
responding to discovery. If our focus were on paper discovery, we would not (forexample) be dictating to a party the manner in which it should review documents
for responsiveness or privilege”).
6. American Bar Association, “Rule 1.1 Competence - Comment.”

About OpenText
OpenText, The Information Company, enables organizations to gain insight throughmarket leading information management solutions, on-premises or in the cloud. For
more information about OpenText (NASDAQ: OTEX, TSX: OTEX) visit: opentext.com.

Connect with us:• OpenText CEO Mark Barrenechea’s blog
• Twitter | LinkedIn

opentext.com/contact
Copyright © 2021 Open Text. All Rights Reserved. Trademarks owned by Open Text.
For more information, visit: https://www.opentext.com/about/copyright-information • 12.21 | 19296.EN

13/13