Implementation Of Law: A Word Of Wisdom

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We have laws for just about everything from personal laws to commercial, contractual, e-discovery, and list goes on and on…

What’s important is to understand the ‘implementation’ of such laws! In my experience, as an attorney, I have come across hundreds of laws from all the way from legislation to the point when they are repealed! Laws have been around for a long time, yet we regularly find instances/cases where person suffered illegal detention, false imprisonment, and so forth.

The litigation hold in the process of e-discovery can be summed up in the following definition:

A litigation hold is a written directive advising custodians of certain documents and electronically-stored information (ESI) to preserve potentially relevant evidence in anticipation of future litigation

Well, easier said than done! In the NuVasive, Inc. v. Madsen Med., Inc., No. 13cv2077, 2015 WL 4479147 (S.D. Cal. July 22, 2015) case, simply implementing a legal hold was not enough to satisfy a party’s duty to preserve. Instead, the party must take affirmative steps to implement the hold, follow up with custodians to ensure data preservation, and also ensure that the hold covers all forms of data, including text messages and other emerging data formats.

Well, that seems like and, in fact, is a daunting task. In today’s computing scenario, where majority of the time the workforce is mobile, and not to mention the influx of mobile devices each of us have an use – then we try to remember “Where in the world did I save/store that document” – you get the point.

Within the realm of e-discovery, litigation can be reduced by providing an indispensable, seamless, and a fully collaborative platform/solution so that documents, text messages, and voice can be saved in repositories. Proactive approach towards data compliance will reduce costs in the long run for corporations!

The Information Governance Model (IGRM) Reference Guide at E.D.R.M does a fairly decent job at presenting a model.

While the future of e-discovery may rest on the foundation of information governance, a wise and proactive approach with special emphasis on building efficient processes, and more importantly automating those processes within the organization must be adopted to reduce legal complexities.

Here’s a sample tutorial of what SharePoint/Office 365 Compliance Center can help you achieve!

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How e-Discovery works in SharePoint 2013

SharePoint 2013 allows you to use eDiscovery and compliance features to manage and recover evidence used in civil litigations, as well as manage the records for your enterprise. Being such a powerful web based platform, you can create various sites (similar to web sites) within the SharePoint environment.

Before deploying SharePoint Server eDiscovery features, an important consideration, however, is to plan the search service application infrastructure for your organization. E-Discovery uses search service applications (SSAs) to crawl SharePoint farms. You can configure SSAs in many ways, but the most common way is to have a central search services farm that crawls multiple SharePoint farms. You can use this one search service to crawl all SharePoint content, or you can use it to crawl specific regions, for example, all SharePoint content in Europe.

The way it works is simple: To crawl a SharePoint farm, search first uses a service application proxy to connect to it. The eDiscovery Center uses the proxy connection to send preservations to SharePoint sites in other SharePoint farms.

Key features and APIs in eDiscovery include:

  • Case Manager, which enables records managers to create and manage enterprise-wide discovery projects, place potentially large amounts and various types of content on hold, and preserve a snapshot of content.
  • Enterprise-wide access, which includes the ability to put content on hold and to search for content from a central location. It also includes the ability to conduct searches, access SharePoint content, and place content on hold in any configured SharePoint location.
  • In-Place Holds, which enables an attorney to preserve a snapshot of content while ensuring that users can continue to make changes without disturbing the state of the content snapshot.
  • Analytics, which enable attorneys, administrators, and records managers to collect and analyze data about eDiscovery activity.

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e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

Predictive Coding In E-Discovery: The Game Of Convenience

Back in 2012, Magistrate Judge Andrew Peck’s decision in Da Silva Moore v. Publicis Groupe & MSL Group, 287 F.R.D. 182 (S.D.N.Y. 2012), officially gave the green signal to start utilizing TAR in e-Discovery. The same Judge recently issued an opinion in Rio Tinto PLC v. Vale S.A., 14 Civ. 3042, 2015 WL 872294 (S.D.N.Y. March 2, 2015), titled “Da Silva Moore Revisited”, and stipulated sharing of “seed sets” between parties.

Importantly, the opinion reiterates that “courts leave it to the parties to decide how best to respond to discovery requests” and that courts are “not normally in the business of dictating to parties the process that they should use”.

Importantly, Judge Peck instructed that requesting parties can utilize other means to help ensure TAR training, even without production of seed sets. For instance, the honorable Judge suggested statistical estimation of recall towards the end of the review to determine potential gaps in the production of documents.

Yet, in cases such as Biomet M2a Magnum Hip Implant Prods. Liab. Litig., NO. 3:12-MD-2391, 2013 WL 6405156 (N.D. Ind. Aug, 21, 2013), for example, the court declined to compel identification of seed set, however, encouraged cooperation between parties.

So, where are we going with TAR?

According to the Grossman-Cormack glossary of technology-assisted review with foreword by John M. Facciola, U.S. Magistrate Judge, seed set is “The initial Training Set provided to the learning Algorithm in an Active Learning process. The Documents in the Seed Set may be selected based on Random Sampling or Judgmental Sampling. Some commentators use the term more restrictively to refer only to Documents chosen using Judgmental Sampling. Other commentators use the term generally to mean any Training Set, including the final Training Set in Iterative Training, or the only Training Set in non-Iterative Training”. The important thing to know about seed sets is that they are how the computer learns. It is critical that a seed set is representative and reflects expert determinations.

With this in mind, in one of my articles back in April 2014 titled “E-Discovery Costs vs. Disseminating Justice – What’s Important?” I concluded that technology must strictly be used as a tool in aid to the due-process of law.

As an attorney, I love a good argument corroborated as well as substantiated by solid precedents. Use of TAR in e-Discovery invariably is becoming a matter of “convenience” between both parties in trying to resolve issues. Well, we have arbitration laws for that matter!

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e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

Tax Court Approves Predictive Coding for First-Pass Document Review

Invariably, the logical answer to coping up with Big Data with regards to eDiscovery is Predictive Coding. While definitions of predictive coding vary, but a common form includes uploading electronic documents to a server followed by taking representative samples, and ‘Seed Sets’ are created by attorneys who are familiar with the legal issues of the case. Attorneys, then, review the seed sets and code each document for responsiveness or other attributes, such as privilege or confidentiality. Utilizing a re-iterative approach, predictive coding software is tweaked and adjusted regarding how the computer will analyze future documents.

Recently, a U.S. Tax Court gave permission to use predictive coding in Dynamo Holdings, Ltd. vs. Commissioner, 143 T.C. No. 9 (September, 17, 2014) case, whereby permitting a taxpayer to use predictive coding as a first-pass review of a large set of documents, despite the. Apparently, the big idea is to reduce costs. While respondents in this case asserted predictive coding to be an ‘unproven technology’, the court completely disagreed justifying this by citing several precedents along with an expert testimony. Predictive coding contains two important elements known as ‘Recall’ and ‘Precision’ – I have detailed these concepts in my earlier post.  Inspite of this, the court’s opinion is important for taxpayers faced with requests for a substantial amount of ESI, and has the potential to reduce costs that may easily run into millions of dollars.

This reaffirms one thing for sure – IT, which was once considered a necessary evil, is now evolving to form a symbiotic relationship with the legal industry, and with other industries alike. Manual document review is certainly going to be obsolete in the near future – if not already! Analytics, predictive coding, machine learning products and technologies providing us with business intelligence (BI) to make informed decisions. For example, Microsoft’s newest products such as Delve, along with host of BI tools provide meanings to your data, while SharePoint e-Discovery center adheres to the regulatory compliance and standards. With this said, predictive coding technology is essentially replacing manual work, and tech savvy attorneys seem to have a ball with one!

The important aspect in this regard lies with determining the optimal values for ‘recall’ and ‘precision’ within the predictive coding software!

e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043


E-Discovery Costs vs. Disseminating Justice – What’s Important?

In e-Discovery, courts, attorneys, e-Discovery consultants, and other industry veterans emphatically deliberate proportionality and predictive coding as major apparatuses for reducing e-Discovery costs. First, Rule 26 – “duty to disclose; general provisions governing discovery” of FRCP encompasses, in entirety, matters relating to initial disclosure, time, scope and limits, pretrial disclosure, limitations, parties conference, sanctions, etc., In other words, the legislative intention behind Rule 26 is to ensure and streamline e-Discovery governance matters.

edrm

Secondly, e-Discovery costs can easily escalate to millions of dollars. For instance, on average a Gigabyte (GB) contains 15,000 documents. An average collection of 50 GB entails 750,000 documents which need to be sifted through for relevant details pertaining to specifics of case for defensibility purposes. To give you an idea in terms of costs, reviewing those documents could cost as high as $2 per document or 1.5 million dollars! If 60% were culled down using technology assisted review (TAR), costs would still be as high as $600,000 dollars! E-Discovery budget calculators can be found here.

Here’s the catch! These 750,000 documents are culled down in order to identify potentially relevant documents. The traditional e-Discovery approach is to process all data to TIFF or native for full linear review, whereas, newest and advanced method entails indexing, culling, legal first pass review, and process data for review. With the advent of ‘Big Data’ technology introduced (TAR) or predictive coding as a tool for handling e-Discovery in an efficient cost effective manner.

Statistics plays a pivotal role in TAR, and courts have endorsed usage of TAR in one way or other. However, there may be pitfalls as I explained in one of my earlier posts relating to the limitations of precision and recall in TAR.

Has our justice system become dependent on technology?

Technology is great, however, it must strictly be used as a tool in aid to the due-process of law. As an attorney, I would argue against our justice system’s inclination towards dependability on technology. There are other ways to reduce costs such as global talent acquisition, outsourcing, dual-shoring, offshoring etc., and numerous law firms and corporations have adopted such business models, documenting additional 60% reduction in e-Discovery costs. While reduction in e-Discovery costs are essential, the opportunity cost may undermine defensibility.


e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

7 Things E-Discovery Auditors Must Do

U.S. Federal Rules of Civil Procedure (FRCP) require organizations to look at the ability to respond in a legally defensible manner to discovery requests. Moreover, as organizations expand globally, they need to be ready at all times to provide information that could be requested as evidence in a legal proceeding. Internal or external auditors are in the best position to recommend policies and best practices that can prepare organizations to respond to a data discovery request. The auditors must:

  1. Determine the effectiveness of the e-Discovery communication plan

  2. Document the IT environment

  3. Regularly review backup, retention, and data destruction policies

  4. Review compliance with document destruction procedures, when a litigation hold is issued

  5. Document the steps that will be taken to respond to e-discovery requests

  6. During litigation, determine whether employees are preserving the integrity of relevant material

  7. Review existing backup controls, reports, and inventories of media stored off site

Failing to prepare for an e-discovery request can result in sanctions. Organizations need to have a litigation readiness policy and plan in place to effectively deal with lawsuits. Auditors play a pivotal role in managing litigation risks and help organizations take a proactive approach to e-Discovery by recommending strategies that address key data preservation, storage, destruction, and recovery disquiets. Microsoft SharePoint 2013 and M-Files, for instance, offer e-Discovery and content management solutions to cater to these needs.

e-Discovery auditor

e-Discovery auditor


e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

Corporate Social Responsibility (CSR) in E-Discovery Industry

Triple Bottom Line

Corporate Social Responsibility (CSR) is a management concept in which companies integrate social and environmental concerns in their business operations and interactions with their stakeholders. The basic definition according to Wiki:

“Corporate social responsibility is a form of corporate self-regulation integrated into a business model. CSR policy functions as a built-in, self-regulating mechanism whereby a business monitors and ensures its active compliance with the spirit of the law, ethical standards, and international norms”

CSR is best incorporated with the ‘Triple Bottom Line’ (TBL) approach, which is essentially an accounting framework incorporating three dimensions of performance: financial, social, and environmental.

Triple Bottom Line

Triple Bottom Line

A triple bottom line measures the company’s economic value, ‘people account’ – which measures the company’s degree of social responsibility and the company’s ‘planet account’ – which measures the company’s environmental responsibility. While CSR indoctrination within the e-Discovery industry may be prevalent, only a handful of companies may actually have developed and adopted CSR.

Adopting to a mindset of a good corporate citizen, at ClayDesk, we have initiated the development of a CSR program with a goal of embedding CSR practices in our business. The foremost area of focus for CSR initiatives are directed towards promotion of legal education, e-Discovery laws, Pro-Bono legal work, sponsor a student, and steps towards a paperless (go-green) environment. These steps will bring about positive change and improve the quality of life of members of the society.

Some of the core CSR issues relate to: environmental management, eco-efficiency, responsible sourcing, stakeholder engagement, labor standards and working conditions, employee and community relations, social equity, gender balance, human rights, good governance, and anti-corruption measures. Denmark, for instance, has CSR Law in place which mandates companies to report their CSR initiatives. Apart from providing charity and sponsorship, CSR concept goes beyond by allowing companies the opportunity to become a socially and ethically responsible corporate citizen.

CSR

ClayDesk’s committment to CSR


e-Discovery and | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

When Should E-Discovery Vendors Be Disqualified? Gordon V. Kaleida Health Case

Generally speaking, courts have inherent authority to disqualify parties, representatives, and consultants from participating in litigation.  Attorneys, expert witnesses, and litigation consultants may face disqualification motions in the event of a conflict of interest. With the rapid expansion of the eDiscovery industry, however, a new question has arisen: If an eDiscovery vendor has a potential conflict of interest, when should it be disqualified?  What standard should apply?

To put the problem in perspective, imagine that you manage discovery at a law firm representing the defendant in a contentious wage and hour dispute, and you recently hired an eDiscovery vendor to assist you in scanning and coding your client’s documents, at a cost of $50,000.  Two months later, you receive notice from your vendor that the plaintiff’s counsel has requested its services in connection with the same case.  How would you react?  Would you expect a court to disqualify the vendor if it accepted the engagement?  This scenario occurred in Gordon v. Kaleida Health, resulting in the first judicial order squarely addressing vendor disqualification.  The Kaleida Health court ultimately denied the defendant’s motion to disqualify, allowing the vendor to continue participating in the case.

Discussion of Gordon v. Kaleida Health

Kaleida Health arose out of a now commonplace dispute between a hospital and its hourly employees under the Fair Labor Standards Act (“FLSA”). The plaintiffs, a group of hourly employees, sued the defendant, Kaleida Health, a regional hospital system, claiming they were not paid for work time during meal breaks, shift preparation, and required training, in violation of FLSA.

Kaleida Health’s attorneys, Nixon Peabody, LLP (“Nixon”), hired D4 Discovery (“D4”), an eDiscovery vendor, to scan and code documents for use in the litigation. In connection with the work, Nixon and D4 executed a confidentiality agreement. D4 was to “objectively code” the documents using categories based on characteristics of the document, such as the author and the type of document. The coded documents would then be used by Nixon in preparing for upcoming depositions.

Two months later, plaintiffs’ counsel, Thomas & Solomon, LLP (“Thomas”), requested D4 to provide ESI consulting services to it in connection with the same case. D4 notified Nixon, who promptly objected based on the scanning and coding services D4 provided the defendant during the litigation. D4 then provided assurances that Kaleida Health’s documents would not be used in consulting the plaintiffs and that an entirely different group of employees would work with the plaintiffs’ counsel. Nixon, on behalf of Kaleida Health, persisted in its objection to D4 working for the plaintiffs and ultimately filed a motion to disqualify the vendor.

Magistrate Judge Foschio’s analysis began by outlining the standard governing the disqualification of experts and consultants.  According to the court, the entity sought to be disqualified must be an expert or a consultant, defined as a “‘source of information and opinions in technical, scientific, medical or other fields of knowledge’” or “one who gives professional advice or services” in that field. After the moving party makes this initial showing, it must meet two further requirements.  First, the party’s counsel must have had an “‘objectively reasonable’ belief that a confidential relationship existed with the expert or consultant.” Second, the moving party must also show “that . . . confidential information was ‘actually disclosed’ to the expert or consultant.”

Applying this standard, Judge Foschio ultimately found that because the scanning and objective coding services performed by D4 did not require specialized knowledge or skill and were of a “clerical nature,” D4 was not an “expert” or “consultant.” Further, the court determined that the defendant failed to prove that it provided confidential information to D4 because it did not show “any direct connection between the scanning and coding work . . . and Defendants’ production of [its] ESI.”

Rejecting Kaleida Health’s argument, the court declined to apply to D4 and other eDiscovery vendors the presumption of confidential communications, imputation of shared confidences, and vicarious disqualification applicable in the context of attorney disqualification when a party “switches sides.” The court— as an alternative basis to its finding that D4 did not act as an expert or consultant—held that disqualification was improper because no “prior confidential relationship” existed between Kaleida Health and D4.

Because Kaleida Health represents the first significant attempt at exploring the issues surrounding vendor disqualification, whether later courts should follow Kaleida Health’s lead in exclusively applying the disqualification rules for experts and consultants to vendors becomes the main issue in its wake.  To come to a conclusion on this point, one must first explore the different schemes that courts may apply when considering disqualification.

This above excerpt is a part of article originally written by Michael A. Cottone, a candidate for Doctor of Jurisprudence, The University of Tennessee College of Law, May 2014.

e-Discovery | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

Appellate Court

Appellate Court – Lahore

The trade-off between ‘Recall’ and ‘Precision’ in predictive coding (part 2 of 2)

This is the second part of the two-part series of posts relating to information retrieval by applying predictive coding analysis, and details out the trade-off between Recall and Precision. For part 1 of 2, click here.

To clarify further:

Precision (P) is the fraction of retrieved documents that are relevant, where Precision = (number of relevant items retrieved/number of retrieved items) = P (relevant | retrieved)

Recall (R) is the fraction of relevant documents that are retrieved, where Recall = (number of relevant items retrieved/number of relevant items = P (retrieved | relevant)

Recall and Precision are inversely related. A solid criticism of these two metrics is the aspect of biasness, where certain record may be relevant to a person, may not be relevant to another.

So how do you gain optimal values for Recall and Precision in a TAR platform?

Let’s consider a simple scenario:

• A database contains 80 records on a particular topic

• A search was conducted on that topic and 60 records were retrieved.

• Of the 60 records retrieved, 45 were relevant.

Calculate the precision and recall.

Solution:

Using the designations above:

• A = Number of relevant records retrieved,

• B = Number of relevant records not retrieved, and

• C = Number of irrelevant records retrieved.

In this example A = 45, B = 35 (80-45) and C = 15 (60-45).

Recall = (45 / (45 + 35)) * 100% => 45/80 * 100% = 56%

Precision = (45 / (45 + 15)) * 100% => 45/60 * 100% = 75%

So, essentially – the optimal result – high Recall with high Precision is difficult to achieve.

According to Cambridge University Press:

“The advantage of having the two numbers for precision and recall is that one is more important than the other in many circumstances. Typical web surfers would like every result on the first page to be relevant (high precision) but have not the slightest interest in knowing let alone looking at every document that is relevant. In contrast, various professional searchers such as paralegals and intelligence analysts are very concerned with trying to get as high recall as possible, and will tolerate fairly low precision results in order to get it. Individuals searching their hard disks are also often interested in high recall searches. Nevertheless, the two quantities clearly trade off against one another: you can always get a recall of 1 (but very low precision) by retrieving all documents for all queries! Recall is a non-decreasing function of the number of documents retrieved. On the other hand, in a good system, precision usually decreases as the number of documents retrieved is increased”


e-Discovery | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

Recall and Precision

Recall and Precision

The trade-off between ‘Recall’ and ‘Precision’ in predictive coding (part 1 of 2)

This is a two-part series of posts relating to information retrieval by applying predictive coding analysis, and details out the trade-off between Recall and Precision.

Predicting Coding – sometimes referred to as ‘Technology Assisted Review’ (TAR) is basically the integration of technology into human document review process. The two-fold benefit of using TAR is speeding up the review process and reducing costs. Sophisticated algorithms are utilized to produce relevant set of documents. The underlying process in TAR is based on concept of Statistics.

In TAR, a sample set of documents (seed-sets) are coded by subject matter experts, acting as the primary reference data to teach TAR machine recognition of relevant patterns in the larger data set. In simple terms, a ‘data sample’ is created based on chosen sampling strategies such as random, stratified, systematic, etc.

Remember, it is critical to ensure that seed-sets are prepared by subject matter experts. Based on seed-sets, the algorithm in TAR platform starts assigning predictions to the documents in the database. Through an iterative process, adjustments can be made on the fly to reach desired objectives. The two important metrics used to measure the efficacy of TAR are:

  1. Recall
  2. Precision

Recall is the fraction of the documents that are relevant to the query that are successfully retrieved, whereas, Precision is the fraction of retrieved documents that are relevant to the find. If the computer, in trying to identify relevant documents, identifies a set of 100,000 documents, and after human review, 75,000 out of the 100,000 are found to be relevant, the precision of that set is 75%.

In a given population of 200,000 documents, assume 30,000 documents are selected for review as the result of TAR. If 20,000 documents are ultimately found within the 30,000 to be responsive, the selected set has a 66% precision measure. But if another 5,000 relevant documents are found in the remaining 170,000 that were not selected for review, which means the set selected for review has a recall of 80% (20,000 / 25,000).

Click here to read part 2 of 2.


e-Discovery | cloud computing
New Jersey, USA | Lahore, PAK | Dubai, UAE
www.claydesk.com
(855) – 833 – 7775
(703) – 646 – 3043

CEO ClayDesk

Syed Raza