Removing limitations, redefi ning discoveryEY Technology Assisted Review: compelling, transparent and defensible

1

Finding the needle in the haystack Too much information — that’s the primary discovery challenge facing counsel today. Despite that daunting reality, you’re tasked with distilling insight from information. And while you have the intelligence and the ability, you’re limited by those ever-present constraints: time and money. If unhindered, you could examine every ledger from the fi rst page to the last. If unhindered, you could pour through every report from front to back. If unhindered, you could read every email from subject line to signature.

But you’re not, and you can’t.

And additional challenges arise — harsh deadlines and non-standard validation processes to name a few — accentuating the problem of increasingly too much information.

Meeting these (and other) matters head on takes more than technology: it’s as much about how you approach your work as it is the tools used to do it.

Yet, you need technology, but at what cost? You have to process the voluminous data, but do so intelligently. The right automation should extend your intelligence, and supercharge your ability. It should serve you — rather than replace you. EY Technology Assisted Review (EY_TAR) is here to help you by providing the professional support required.

Single addition, many gains

Incorporating EY_TAR into your discovery processes offers several benefi ts no matter the case size or project scope. It’s a tool that provides multiple advantages. EY_TAR can:

Save time

• Voluminous data. Limited time. Too much of the former and not enough of the latter. EY_TAR solves that problem. Leveraging the decisions of counsel, EY_TAR is able to process large amounts of data, fi nding the relevant information and producing it quickly and accurately.

Reduce cost

• Growing data has spurred the growth of litigation costs over the last decade. EY_TAR can help address that trend. Time is money. More to the point, saving time reduces expenses. EY_TAR saves you a substantial amount of both. Compared to traditional linear human review alone, fi rst-pass reviews take about half the time and typically cost at least 50% less.

Provide knowledge

• The data you need is in that haystack; it’s a matter of fi nding it. When your team faces a mass of documents to review, human limitations prevent a thorough review of every document. But EY_TAR isn’t bound by such restrictions, yielding knowledge that could otherwise be missed.

Produce reliable, defensible results

• Introducing automation into your discovery efforts may call control of the process into question. However, EY_TAR is attorney-driven computing. Also, the quality of the results is verifi ed, enabling the counsel to measure the results of the process by the standards already established.

What is EY_TAR?

EY Technology Assisted Review (EY_TAR) is a proven, systematic method of leveraging and extending professional judgment through technology, linguistics, analytics and statistics.

3

Technology has assisted counsel in review of large volumes of information for more than 20 years. As was the case with Optical Character Recognition and full text indexing, TAR is going to dramatically change how documents are reviewed by litigants. TAR is not a one-size-fi ts-all choice — technologies and applications are varied. The marketplace offers many options, as illustrated below.

There are many ways technology aids document review. Starting from the far right-hand side of the above chart, technology can help sort documents prior to review — keyword searching, clustering — making the review more effective.

Moving to the left, we see technologies that can be leveraged to make some or all document-classifi cation decisions. Therein is a key point: computers aren’t making decisions, but rather modeling human decision-making. They extend those decisions to documents that human reviewers haven’t yet assessed. It’s similar to a keyword search in that automation identifi es documents with certain word combinations as potentially relevant. It’s different in that you don’t need to search for the exact words.

TAR: many choices

Linear reviewHumans classify each document

Review

TARTechnology is used to assist the human reviewers.

Predictive codingTechnology is used to make some or all document classifi cations.

Machine learning classifi ersA machine-learning algorithm is used to build a predictive model that projects the initial human document classifi cations across the population in a way that creates a model of the document set.

Manually built classifi ersA predictive model structure is built manually (e.g., using ontology-based searches) to project the initial human document classifi cations across the population.

HybridTechnology is used to make some document classifi cations while humans make other document decisions (e.g., removal of “spam” or personal email from the collection prior to review by humans).

Accelerated reviewTechnology is used to “sort” or fi lter the documents prior to review by humans (e.g., keyword searching, concept clustering).

Linear reviewHumans classify each document.

4

5

Man or machine?

Technology shouldn’t come at the expense of counsel’s judgment. Applied correctly, it should enable counsel to allocate their expertise when and where it’s most needed. Enlisting technology can signifi cantly enhance your discovery process, and a basic understanding of that technology adds to its potential benefi t.

Depending on your chosen technology — and the people who guide its implementation — the control you give up in the process can be great. And your results can vary even more so. The most effective technology and processes are extensions of you, dynamic and capable of adjusting as the review takes place — “thinking” as you think, based on how you’ve thought.

As you’ll see, EY_TAR is such a technology, a methodology that supports you, instead of supplanting you.

There is an accelerating trend toward widening acceptance of TAR’s usage.

• Federal and state courts have approved — and in some cases urged — the use of TAR.

• Moore v. Publicis Groupe, 287 F.R.D. 182 (S.D.N.Y. 2012), Global Aerospace v. Landow Aviation, No. CL 61040 (Va. Cir. Ct. Apr. 23, 2012)

• Requesting parties have demanded that producing parties use it.

• Kleen Products, LLC v. Packaging Corp. of America, 2012 WL 4498465 (N.D.Ill.), In re Biomet M2a Magnum Hip Implant Products Liability Litigation, 2013 WL 1729682 (N.D.Ind.)

• Legal and scientifi c scholarship confi rms TAR’s superiority over traditional means of ESI review.

• Document Categorization in Legal E-Discovery: Computer Classifi cation vs. Manual Review by Kershaw, Roitblat and Oot, published in the Journal of the American Society for Information Science and Technology, 61(1): 70–80, 2010

• Technology-Assisted Review in E-Discovery Can Be More Effective and More Effi cient Than Exhaustive Manual, Maura R. Grossman & Gordon V. Cormack, Review, XVII RICH. J.L. & TECH. 11 (2011)

Carrying its own weight (in court)

6

Incorporating EY_TAR improves your discovery process. And while many TAR offerings may enhance your process and yield results otherwise unrealized, EY_TAR provides you with many unique benefi ts amongst our competition. The one methodology that produces the most compelling results, functions transparently throughout the process and remains defensible is EY_TAR.

This is not simply some licensed technology that EY puts into play — it’s our own, the result of accumulated knowledge, more than a decade of experience and patented technology (with ongoing development).

EY_TAR: adding insight to innovation

Model

3Predict 2

Resolve conflicts

4Measure

5Report6

Evaluate

1Sample

documents

• Agreement between predicted and human decisions

• Precision and recall metrics

• Statistical validation

• Inclusive vs. exclusive

• Fact pattern

• Foreign language identifi cation

• Multiple topic model creation

• NR segregation

• R prioritization

• Timelines

• Correlate to discovery requests

• Human awareness

• Decision tree

• Coding protocol

• Flexibility of multiple algorithms with voting schemes

• Newer algorithms: latent Dirichlet allocation topic models

• Context reports

• Discussions

• Entity extraction

• Social network analysis

• Alias normalization

• Targeted searches

• Emotive tone

• Ethical behavior

• Privilege tiers

Validate results

Educatereviewers

Setstrategy

Setobjectives

Accomplishobjectives

7

Our global presence is a catalyst for local capability. EY leverages LangID Automated Language Identifi cation, an advanced text analytics system that identifi es natural languages in your documents.

Highly accurate LangID offers advantages other programs may not have:

• Identifi es languages present in unstructured text automatically

• Determines language at the sentence level, enabling multiple-language identifi cation in each document

• Uses both character- and word-based features

• Leverages fi le type and size, and numerical fi ltering to target text-based fi les

• Targets language-rich sections of email, using email-segment fi ltering

• Identifi es 70+ languages, including some transliterated and diacritic-less variants like Russian written with a Latin character set

Global knowledge, spoken the world over

8

More than the sum of its parts

When you add EY_TAR into your discovery process, you get more out of it.

Proven

There is no guesswork regarding the effectiveness of the process or its rigid compliance with leading quality practices. Document review results are only as good as the confi dence they instill.

Experienced team

Our EY_TAR team brings 10 years of collaborative experience in TAR. Our personnel are experienced discovery project managers, data scientists and litigation-technology professionals accustomed to working closely with companies and outside counsel to consult and assist in the management of high-pressure and complex discovery engagements. We have developed standard EY discovery management and documentation protocols and adhere to them while maintaining the fl exibility to tailor our approach to the needs of the legal team as required.

Proprietary patented technology and proven methodology

EY_TAR is built on proven proprietary EY technology, and supported by subject matter resources in statistics, linguistics and the discovery process. EY_TAR enables sophisticated searches of large and complex data sets to incorporate the professional judgment of counsel in identifying relevant documents that are responsive to requests, as well as sensitive documents that are typically hidden within the data set.

For a given matter and document set, EY_TAR automatically evaluates the top four algorithms to identify the optimal combination, yielding demonstrably better results. Moreover, once applied, EY_TAR creates reusable knowledge stores with linguistic rules that allow for continuous and on-going improvement and can be leveraged in future litigation.

9

Transparency

With the incorporation of our state-of-the-art visual dashboards, discovery is back in the hands and judgment of counsel. Attorneys see documents in a comfortable interface with a familiar look and feel, accompanied by numerous dynamic dashboards to enable deep dives and rapid decision testing and refi nement. EY_TAR provides a transparent audit trail that traces every coding decision back to its origin. Nowhere in the EY_TAR is there a black box component that makes results impossible to defend.

Customizable, scalable, fl exible

Having designed and built EY_TAR ourselves, we are able to quickly customize the methodology and workfl ow to meet your specifi c needs. EY_TAR isn’t just a static platform solution that requires our clients to change their own processes to fi t into; it adapts quickly to address your individual requirements. Supporting this robust system is a big data infrastructure that is capable of scaling to virtually any volume of information and calibrating processing speed with data volume to safely accommodate your scheduling requirements.

End-to-end services

EY_TAR can be applied to every facet of your case: early data and case assessment, eDiscovery, depositions, evidentiary hearings and trial preparation.

10

Practice, not theoryThe applications of EY_TAR are many. We know, because we’ve applied it across a wide spectrum of uses, gaining deep experience along the way. Below is a sampling of how we’ve put EY_TAR into motion for our clients.

Hart-Scott-Rodino Second Request

A large telecommunications carrier, having extended a tender offer to a global provider in the cloud services market, was required to respond to a Hart-Scott-Rodino Second Request issued by the Department of Justice (DOJ). To formulate the response, we used EY_TAR to:

• Process several terabytes of forensic images

• Create a language model to accurately categorize the data in accordance with dozens of specifi cations as required by the DOJ

• Correlate each document with its relevant specifi cation when produced

• Prompt early termination of the regulatory action — due to timely and accurate compliance — despite an already aggressive schedule

Cross-border, multilingual investigation into fi nancial accounting issues

A global manufacturing company sought EY’s support in an investigation of possible accounting irregularities, spanning multiple countries with three primary languages. Through EY_TAR, we:

• Applied language-identifi cation capabilities to sort 2.39 million documents by principal language

• Executed our proprietary Automated Predictive Coding technology separately on each language subset

• Trained the Automated Predictive Coding system on a combination of random samples and specifi c searches focusing on key events and central actors, leading to the automated identifi cation of 45,000 potentially relevant documents

• Confi rmed statistically the remaining 2.345 million documents as non-relevant

Data mining and ontologies

EY_TAR can be put to use proactively, as demonstrated when a large multinational sought to monitor employees worldwide for potential ethical violations. Our application of EY_TAR spanned fraud to workplace discrimination to confl icts of interest, and included:

• Mining email and documents, and assembling corporate jargon, acronyms and code names

• Creating custom ontologies for each subsection of the client’s code of conduct

• Deploying ontologies across disparate offi ces’ email worldwide to identify situations warranting investigation

Social network analysis, language identifi cation and information consistency analysis

Supporting a fraud investigation focused on senior executives, we utilized EY_TAR to pinpoint opportunities that individuals had to conspire face to face. The project saw us:

• Deploy emotive tone and social network analysis to characterize social relationships among executives and identify other relevant social connections

• Apply language identifi cation to multilingual suspects’ emails to identify possible language-switching used as part of a secrecy strategy

• Extract time, date, location and actor information from unstructured and structured data sources — T&E reports, browser history and personal email — creating a comprehensive map of each suspect’s location over time

Custom categorization and sampling

EY_TAR was brought to bear in assuring a large energy company’s accurate classifi cation for document production in a multidistrict environmental case. The litigation centered on subtle jurisdictional distinctions between both courts and regulators. Working closely with a leading law fi rm, we:

• Developed custom categorization queries to address 10 responsive topics, paying particular attention to how query results interacted with nuanced, yet critical, jurisdictional issues

• Subjected the resulting potential production to a blind sampling test, testing recall of responsive topics

12

Disclaimer analysis

Counsel conducting a linear privilege review enlisted EY’s help when they discovered false positives in 80% of documents identifi ed as potentially privileged. Applying EY_TAR, we removed a large fraction of responsive documents by:

• Building a statistical model of disclaimers and automatically processing 120,000 documents, determining that boiler plate privilege disclaimers were the only indicator of privilege for 80,000

• Separating privilege language from the false positive disclaimer contexts within each document

• Delivering a reduced set of documents containing only true positive privileged language

• Withstanding extensive client sampling of the disclaimer-only set with 100% accuracy, producing no results that regarded as truly privileged

13

EY | Assurance | Tax | Transactions | Advisory

About EYEY is a global leader in assurance, tax, transaction and advisory services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. For more information about our organization, please visit ey.com.

About Ernst & Young’s Forensic Technology & Discovery ServicesEY’s Forensic Technology & Discovery Services (FTDS) practice, part of Fraud Investigation & Dispute Services (FIDS), provides a full range of eDiscovery, data analytics and cybercrime investigation capabilities on a global basis. Our multidisciplinary teams can deploy advanced technologies and professional services anywhere in the world, responding with speed and precision to complex regulatory, fact-finding and cybersecurity risks.

1310-1152208

© 2013 EYGM Limited. All Rights Reserved.

ED 0114

EYG no. BB2652

ey.com