Intelligence (AI) in eDiscovery: TAR & Beyond Practical

eDiscovery Webinar Series

Practical Applications and Pitfalls of Artificial Intelligence (AI) in eDiscovery: TAR & Beyond

A Guide for the Boutique Firm Handling Document Intensive Matters


eDiscovery Webinar SeriesLexbe Confidential

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● If you have technical issues or questions, please email [email protected]

● Lexbe webinars are available for viewing (streaming video), and downloadable as a PDF Presentation or an MP3 podcast.

● This Webinar and a complete listing of other onDemand webinars is part of the: Lexbe eDiscovery Webinar Series

● For notices of future live and on-Demand webinars as part of this series please email us at [email protected] or: Follow us on LinkedIN

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ABOUT LEXBE◼ Serving boutique law firms for more than 15 years

◼ Based in Austin TX

◼ The Industry’s Most Affordable and Full-Featured DIY cloud-based eDiscovery Platform

◼ The Industry’s Fastest eDiscovery Processing & Document Review Platform

◼ Delivers the Industry’s Fastest ROI

◼ Experienced eDiscovery Specialists and Expert Consultants who are ACED Certified

Gartner Reviews:

“Cost-effective eDiscovery” “Secure, easy-to-use and a great review tool for consideration”

“A powerful litigation document management service”

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● Chief Revenue Officer at Lexbe

● Over 25 years experience bringing emerging technologies to market; including 7 years in machine learning and artificial intelligence software

● Frequent speaker on eDiscovery and Forensics

● University of KansasB.A. & B.S.

Jeff FugittChief Revenue OfficerLexbe(512) [email protected]

GUEST SPEAKER


WILL AI REPLACE LAWYERS & PARALEGALS?

http://www.youtube.com/watch?v=HjJmO9NvKL8


The AI Landscape:

I believe most law departments will be using artificial intelligence for legal type work in the next three years:

13th Annual Law Department Operations Survey


The AI Landscape: DefinitionsArtificial intelligence (AI) is the field of developing computers to perform tasks that have typically required human intelligence. The

term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Machine learning (ML) is a subset of artificial intelligence and refers to computer algorithms that improve automatically through

experience and training. Machine learning algorithms are built on a model based on sample data, known as "training data", in order

to make predictions or decisions without being explicitly programmed to do so.

Responsive Nonresponsive

https://en.wikipedia.org/wiki/Training_data


The AI Landscape: Definitions

Neural networks are a series of algorithms that endeavor to recognize underlying relationships in a set of data through a process

that mimics the way the human brain operates.Neural networks learn (or are trained) by processing examples, each of which

contains a known "input" and "result," forming probability-weighted associations between the two, which are stored within the data

structure.

Deep learning is a class of machine learning algorithms that use multiple layers to progressively extract higher-level features from

the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts

relevant to a human such as digits or letters or faces.

https://en.wikipedia.org/wiki/Machine_learning

https://en.wikipedia.org/wiki/Algorithm

https://en.wikipedia.org/wiki/Image_processing


The AI Landscape: Definitions

Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

Technology Assisted Review (TAR) is a process of having computer software electronically classify documents based on input from

expert reviewers, in an effort to expedite the organization and prioritization of the document collection.

● Simple Passive Learning

● Simple Active Learning

● Continuous Active Learning

https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html


Pitfalls of AI: Dataset Shifting

Why do AI deployments fail in the real world?

Dataset shifting means there is enough of a differential between the type of data used to develop and

train the machine learning model and the data used in the real world application of the algorithm.

In this example, the AI algorithm performed poorly on the target dataset.

Responsive Responsive


Pitfalls of AI: Underspecification

Why do AI deployments in the real world fail?

In general, the solution to a problem is underspecified if there are many distinct solutions that solve the

problem equivalently. Small variations in the training process and model development that don’t

manifest issues during training can wreak havoc in real world deployments.

“Together, our findings indicate that underspecification can, and does, degrade the credibility of ML

predictors in applications, even in settings where the prediction problem is well-aligned with the goals

of an application.” - Underspecification Presents Challenges for Credibility in Modern Machine Learning, Nov. 24, 2020


Effective Applications of AI in eDiscovery


Law firms are leveraging technology to deliver legal services more effectively and cost effectively.

Our law firms are innovative

13th Annual Law Department Operations Survey


Entity Detection

AI powered entity detection is particularly useful in the early stages of a document intensive case. The

algorithm will identify entities like the following:

● Individuals● Groups of People● Nicknames● Fictional Characters● Titles

● Organizations● Government Entities● Companies● Religious Entities● Sports Teams

● Locations● Countries● Counties● Cities● Addresses● Buildings● Bodies of Water

● Dates● Partial Dates● Day● Month● Time● Time Zone

● Quantities● Amounts● Currency● Percentages● Numbers● Bytes


PII Identification

Machine learning algorithms are easily trained to identify and redact Personally Identifiable Information

(PII) and Protected Health Information (PHI) in order to comply data privacy regulations.

● Social Security Numbers● Passport Numbers● Driver’s License Numbers● User Names● Passwords● IP Addresses● Bank Account Numbers● Bank Routing Numbers● Credit Card Numbers● CVV Codes● Expiration Dates● PIN


Machine Language Translation

AI powered machine translation is fast, affordable and helps you maintain

control. Deep learning methods enable the algorithm to higher accuracy and

fluency.

● Hundreds of languages - pretrained

● Search keywords in multiple languages

● Analyze textual content across languages

● Maintain control versus outsourcing

● Fast, high quality and affordable

● Continuous improvement


Machine Transcription

AI is highly efficient at converting audio data into text for search and

analysis. Utilizing AI powered speech recognition can deliver fast and

affordable machine transcription for a fraction of the price of

traditional transcription services.

● Turn unstructured voice call data into searchable ESI

● Speaker diarization, punctuation and formatting

● Convert depositions, sworn statements and other proceedings

into searchable ESI


Machine Transcription


Video & Image Identification and Categorization

AI algorithms are capable of automating the analysis of videos and

images and help to streamline the review process.

● Identify and tag thousands of objects in images and videos

○ Face detection

○ Text detection

○ Personal protective equipment

● Capture video and image attributes

○ Gender

○ Age range

○ Eyes open

○ Glasses

○ Facial hair

○ Smiling

○ Emotive descriptors


Sentiment Analysis

AI is a great tool to assess the sentiment of documents in eDiscovery.

● Positive

● Negative

● Neutral

● Mixed

The algorithms typically generate a score (ex. 87% likelihood the document is positive) and enable you to

quickly sort by sentiment and custodian.

:-(


Technology Assisted Review (TAR)

AI can be a force multiplier for anyone dealing with document intensive cases. TAR utilizes machine

learning algorithms to make binary classification decisions regarding document responsiveness.

● Precision is a measure of how accurate the algorithm predicts a document to be responsive

● Recall is a measure of what percentage of the responsive documents in a dataset have been classified

correctly








The AI Advantage in Litigation

● Speed of building your case

● Find the needles in the haystacks - faster

● Maintain control of the review process

● Easily review multi-media

● Increase speed of document review

● Reduce case costs

● Improve top line revenue growth

● Safeguard privileged documents

● Be perceived as innovative and mission critical


END TO END E-DISCOVERY IN THE CLOUD

Lexbe Confidential

◼ Full-Featured◼ DIY◼ Infinitely Scalable◼ Accessible with a Browser


THE LEXBE UBER INDEXNative Characters

Translated Characters

All Characters From Native Files are Extracted and Included in the Uber Index.

All OCR Characters are Extracted and Included in the Uber Index.

All Images are OCR’d, Characters Extracted and Included in the Uber Index.

Lexbe’s Translation Engine Feeds the Uber Index All Translated Characters

OCR Characters

Image OCR Characters

◼ Multi-source concatenated singular index

◼ Lightning fast

◼ Seamlessly add documents without re-indexing

Lexbe Confidential


Audio Files Transcribed in the Lexbe eDiscovery Platform

Lexbe Confidential

Review transcription files and quickly identify and tag where on the audio track the evidence resides.

Lexbe’s AI Powered Transcription automatically identifies and designates each speaker.


ADVANCED APPLICATIONS OF AI

Lexbe Confidential

Audio & Video File Transcription

LanguageTranslation

ImageRecognition

SentimentAnalysis

◼ Lexbe delivers multiple applications of AI to drive eDiscovery efficiencies


INDUSTRY LEADING PRICING

Lexbe Confidential

Service Relativity LexbeProcessing $125 per GB $0

User Fees $95 per User $0

Technology Assisted Review $30 per GB* $0** (1X per case)

Near Duplication * Included with Relativity Analytics

$0**

Email Threading * Included with Relativity Analytics

$0**

Hosting $8 per GB per Mo. $5 per GB per Mo.*

AI Insights N/A $0**

** No charge for Subscription Hosting Accounts. Flex accounts will have additional charges. Subscription Hosting starts a plans @ $5/GB/m. Flex hosting is $15/GB/m. Prices subject to change at any time.


Polling Question:

Would you like a demonstration of the Lexbe eDiscovery Platform?

● Yes● No


● The Lexbe eDiscovery Platform, is our cloud-based processing, review and production tool. Designed for Attorneys/legal staff to be DIY and easy to use, with no users fees or case fees. Free standard loading with annual plans.

“We just got back our verdict of nearly $9 million in the case we tried against Greenberg Traurig and Hollingsworth Law Firm (voted #1 complex litigation firm in the world). There is no way our team of 3 lawyers could have competed with

those heavy hitters without LEXBE. Thanks to your team!”

1-800-401-7809 x22 | [email protected]

‘Cost-effective eDiscovery’

“A powerful litigation document management service”

“Because of the Lexbe software, the entire playing field has been leveled for my firm.”

‘Lexbe cost advantages, SaaS convenience and search capabilities appeal to many small firms

“Lexbe is the easiest eDiscovery software I have ever used’

‘Secure, easy-to-use and a great review tool for consideration’

LEARN MORE ABOUT LEXBE


We’ll be making the following available to webinar attendees:

● A recorded streaming version● MP3 podcast● PDF

Please let us know if you have any questions or comments about this webinar or suggestions for future topics. This webinar is part of the Lexbe eDiscovery Webinar Series. For notices of future live and on-Demand webinars as part of this series please email us at [email protected] or Follow us on LinkedIN.

Presenter: Jeff [email protected](512) 653-8295

Moderator: Gene [email protected](512) 686-3460

Webinar Questions: [email protected]

THANK YOU FOR ATTENDING

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Intelligence (AI) in eDiscovery: TAR & Beyond Practical