Electronic discovery

Last updated

Electronic discovery (also ediscovery or e-discovery) refers to discovery in legal proceedings such as litigation, government investigations, or Freedom of Information Act requests, where the information sought is in electronic format (often referred to as electronically stored information or ESI). [1] Electronic discovery is subject to rules of civil procedure and agreed-upon processes, often involving review for privilege and relevance before data are turned over to the requesting party.

Contents

Electronic information is considered different from paper information because of its intangible form, volume, transience and persistence. Electronic information is usually accompanied by metadata that is not found in paper documents and that can play an important part as evidence (e.g. the date and time a document was written could be useful in a copyright case). The preservation of metadata from electronic documents creates special challenges to prevent spoliation.

In the United States, at the federal level, electronic discovery is governed by common law, case law and specific statutes, but primarily by the Federal Rules of Civil Procedure (FRCP), including amendments effective December 1, 2006, and December 1, 2015. [2] [3] In addition, state law and regulatory agencies increasingly also address issues relating to electronic discovery. In England and Wales, Part 31 of the Civil Procedure Rules [4] and Practice Direction 31B on Disclosure of Electronic Documents apply. [5] Other jurisdictions around the world also have rules relating to electronic discovery.

Stages of process

The Electronic Discovery Reference Model (EDRM) is an ubiquitous diagram that represents a conceptual view of these stages involved in the ediscovery process.

Identification

The identification phase is when potentially responsive documents are identified for further analysis and review. In the United States, in Zubulake v. UBS Warburg , Hon. Shira Scheindlin ruled that failure to issue a written legal hold notice whenever litigation is reasonably anticipated will be deemed grossly negligent. This holding brought additional focus to the concepts of legal holds, eDiscovery, and electronic preservation. [6] Custodians who are in possession of potentially relevant information or documents are identified. Data mapping techniques are often employed to ensure a complete identification of data sources. Since the scope of data can be overwhelming or uncertain in this phase, attempts are made to reasonably reduce the overall scope during this phase - such as limiting the identification of documents to a certain date range or custodians.

Preservation

A duty to preserve begins upon the reasonable anticipation of litigation. Data identified as potentially relevant during preservation is placed in a legal hold. This ensures that data cannot be destroyed. Care is taken to ensure this process is defensible, while the end goal is to reduce the possibility of data spoliation or destruction. Failure to preserve can lead to sanctions. Even if a court does not rule that the failure to preserve is negligence, they can force the accused to pay fines if the lost data puts the defense "at an undue disadvantage in establishing their defense." [7]

Collection

Once documents have been preserved, collection can begin. The collection is the transfer of data from a company to its legal counsel, who will determine the relevance and disposition of data. Some companies that deal with frequent litigation have software in place to quickly place legal holds on certain custodians when an event (such as legal notice) is triggered and begin the collection process immediately. [8] Other companies may need to call in a digital forensics expert to prevent the spoliation of data. The size and scale of this collection are determined by the identification phase.

Processing

During the processing phase, native files are prepared to be loaded into a document review platform. Often, this phase also involves the extraction of text and metadata from the native files. Various data culling techniques are employed during this phase, such as deduplication and de-NISTing. Sometimes native files will be converted to a petrified, paper-like format (such as PDF or TIFF) at this stage to allow for easier redaction and bates-labeling.

Modern processing tools can also employ advanced analytic tools to help document review attorneys more accurately identify potentially relevant documents.

Review

During the review phase, documents are reviewed for responsiveness to discovery requests and for privilege. Different document review platforms and services can assist in many tasks related to this process, including rapidly identifying potentially relevant documents and culling documents according to various criteria (such as keyword, date range, etc.). Most review tools also make it easy for large groups of document review attorneys to work on cases, featuring collaborative tools and batches to speed up the review process and eliminate work duplication.

Analysis

Qualitative analysis of the content discovered in the collection phase and after being reduced by the preprocessing phase. The Evidence is looked at in context. Correlation analysis or contextual analysis to extract structured information relevant to the case. Structuring like Timelineing or Clustering into Topics can be done. An example structure could be the analysis from a client-based perspective; here, each investigator looks at one agent included in the evidence additional Patterns like discussions or network analysis around people can be done.

Production

Documents are turned over to opposing counsel based on agreed-upon specifications. Often this production is accompanied by a load file, which is used to load documents into a document review platform. Documents can be produced either as native files or in a petrified format (such as PDF or TIFF) alongside metadata.

Presentation

Displaying and explaining evidence before audiences (at depositions, hearings, trials, etc.). The idea is that the audience understands the presentation, and non-professionals can follow the interpretation. Clarity and ease of understanding are the focus here. The native form of data needs to be abstracted, visualized, and broad into context for the presentation. The results of the analysis should be the subject of the presentation. The clear documentation should provide reproducibility.

Types of electronically stored information

Any data that is stored in an electronic form may be subject to production under common eDiscovery rules. This type of data has historically included email and office documents (spreadsheets, presentations, documents, PDFs, etc.) but can also include photos, video, instant messaging, collaboration tools, text (SMS), messaging apps, social media, ephemeral messaging, Internet of things (smart devices like Fitbits, smart watches, Alexa Alexa, Apple Siri, Nest), databases, and other file types.

Also included in ediscovery is "raw data", which forensic investigators can review for hidden evidence. The original file format is known as the "native" format. Litigators may review material from ediscovery in one of several formats: printed paper, "native file", or a petrified, paper-like format, such as PDF files or TIFF images. Modern document review platforms accommodate the use of native files and allow for them to be converted to TIFF and Bates-stamped for use in court.

Electronic messages

In 2006, the U.S. Supreme Court's amendments to the Federal Rules of Civil Procedure created a category for electronic records that, for the first time, explicitly named emails and instant message chats as likely records to be archived and produced when relevant.

One type of preservation problem arose during the Zubulake v. UBS Warburg LLC lawsuit. Throughout the case, the plaintiff claimed that the evidence needed to prove the case existed in emails stored on UBS' own computer systems. Because the emails requested were either never found or destroyed, the court found that they were more likely to exist than not. The court found that while the corporation's counsel directed that all potential discovery evidence, including emails, be preserved, the staff that the directive applied to did not follow through. This resulted in significant sanctions against UBS.

To establish authenticity, some archiving systems apply a unique code to each archived message or chat. The systems prevent alterations to original messages, messages cannot be deleted, and unauthorized persons cannot access the messages.

The formalized changes to the Federal Rules of Civil Procedure in December 2006 and 2007 effectively forced civil litigants into a compliance mode with respect to their proper retention and management of electronically stored information (ESI). Improper management of ESI can result in a finding of spoliation of evidence and the imposition of one or more sanctions, including adverse inference jury instructions, summary judgment, monetary fines, and other sanctions. In some cases, such as Qualcomm v. Broadcom, attorneys can be brought before the bar. [9]

Databases and other structured data

Structured data typically resides in databases or datasets. It is organized in tables with columns, rows, and defined data types. The most common are Relational Database Management Systems (RDBMS) that are capable of handling large volumes of data such as Oracle, IBM Db2, Microsoft SQL Server, Sybase, and Teradata. The structured data domain also includes spreadsheets (not all spreadsheets contain structured data, but those that have data organized in database-like tables), desktop databases like FileMaker Pro and Microsoft Access, structured flat files, XML files, data marts, data warehouses, etc.

Audio

Voicemail is often discoverable under electronic discovery rules. Employers may have a duty to retain voicemail if there is an anticipation of litigation involving that employee. Data from voice assistants like Amazon Alexa and Siri have been used in criminal cases. [10]

Reporting formats

Although petrifying documents to static image formats (TIFF & JPEG) had become the standard document review method for almost two decades, native format review has increased in popularity as a method for document review since around 2004. Because it requires the review of documents in their original file formats, applications and toolkits capable of opening multiple file formats have also become popular. This is also true in the ECM (Enterprise Content Management) storage markets, which converge quickly with ESI technologies.

Petrification involves the conversion of native files into an image format that does not require the use of native applications. This is useful in the redaction of privileged or sensitive information since redaction tools for images are traditionally more mature and easier to apply on uniform image types by non-technical people. Efforts to redact similarly petrified PDF files by incompetent personnel have removed redacted layers and exposed redacted information, such as social security numbers and other private information. [11] [12]

Traditionally, electronic discovery vendors had been contracted to convert native files into TIFF images (for example, 10 images for a 10-page Microsoft Word document) with a load file for use in image-based discovery review database applications. Increasingly, database review applications have embedded native file viewers with TIFF capabilities. With both native and image file capabilities, it could either increase or decrease the total necessary storage since there may be multiple formats and files associated with each individual native file. Deployment, storage, and best practices are becoming especially critical and necessary to maintain cost-effective strategies.

Structured data are most often produced in delimited text format. When the number of tables subject to discovery is large or relationships between the tables are of essence, the data are produced in native database format or as a database backup file. [13]

Common issues

A number of different people may be involved in an electronic discovery project: lawyers for both parties, forensic specialists, IT managers, and records managers, amongst others. Forensic examination often uses specialized terminology (for example, "image" refers to the acquisition of digital media), which can lead to confusion. [1]

While attorneys involved in case litigation try their best to understand the companies and organizations they represent, they may fail to understand the policies and practices that are in place in the company's IT department. As a result, some data may be destroyed after a legal hold has been issued by unknowing technicians performing their regular duties. Many companies are deploying software that properly preserves data across the network to combat this trend, preventing inadvertent data spoliation.

Given the complexities of modern litigation and the wide variety of information systems on the market, electronic discovery often requires IT professionals from both the attorney's office (or vendor) and the parties to the litigation to communicate directly to address technology incompatibilities and agree on production formats. Failure to get expert advice from knowledgeable personnel often leads to additional time and unforeseen costs in acquiring new technology or adapting existing technologies to accommodate the collected data.

Alternative collection methods

Currently the two main approaches for identifying responsive material on custodian machines are:

(1) where physical access to the organizations network is possible - agents are installed on each custodian machine which push large amounts of data for indexing across the network to one or more servers that have to be attached to the network or

(2) for instances where it is impossible or impractical to attend the physical location of the custodian system - storage devices are attached to custodian machines (or company servers) and then each collection instance is manually deployed.

In relation to the first approach there are several issues:

New technology is able to address problems created by the first approach by running an application entirely in memory on each custodian machine and only pushing responsive data across the network. This process has been patented [14] and embodied in a tool that has been the subject of a conference paper. [15]

In relation to the second approach, despite self-collection being a hot topic in eDiscovery, concerns are being addressed by limiting the involvement of the custodian to simply plugging in a device and running an application to create an encrypted container of responsive documents. [16]

Regardless of the method adopted to collect and process data there are few resources available for practitioners to evaluate the different tools. This is an issue due to the significant cost of eDiscovery solutions. Notwithstanding the limited options for obtaining trial licences for the tools, a significant barrier to the evaluation process is creating a suitable environment in which to test such tools. Adams suggests the use of the Microsoft Deployment Lab which automatically creates a small virtual network running under HyperV [17]

Technology-assisted review

Technology-assisted review (TAR)—also known as computer-assisted review or predictive coding—involves the application of supervised machine learning or rule-based approaches to infer the relevance (or responsiveness, privilege, or other categories of interest) of ESI. [18] Technology-assisted review has evolved rapidly since its inception circa 2005. [19] [20]

Following research studies indicating its effectiveness, [21] [22] TAR was first recognized by a U.S. court in 2012, [23] by an Irish court in 2015, [24] and by a U.K. court in 2016. [25]

Recently a U.S. court has declared that it is "black letter law that where the producing party wants to utilize TAR for document review, courts will permit it." [26] In a subsequent matter, [27] the same court stated,

To be clear, the Court believes that for most cases today, TAR is the best and most efficient search tool. That is particularly so, according to research studies (cited in Rio Tinto [26] ), where the TAR methodology uses continuous active learning ("CAL") [28] which eliminates issues about the seed set and stabilizing the TAR tool. The Court would have liked the City to use TAR in this case. But the Court cannot, and will not, force the City to do so. There may come a time when TAR is so widely used that it might be unreasonable for a party to decline to use TAR. We are not there yet. Thus, despite what the Court might want a responding party to do, Sedona Principle 6 [29] controls. Hyles' application to force the City to use TAR is DENIED.

Grossman and Cormack define TAR in Federal Courts Law Review 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) on a smaller set of Documents and then extrapolates those judgments to the remaining Document Collection. Some TAR methods use Machine Learning Algorithms to distinguish Relevant from Non-Relevant Documents, based on Training Examples Coded as Relevant or Non-Relevant by the Subject Matter Experts(s), while other TAR methods derive systematic Rules that emulate the expert(s)’ decision-making process. TAR processes generally incorporate Statistical Models and/or Sampling techniques to guide the process and to measure overall system effectiveness. [30]

Convergence with information governance

Anecdotal evidence for this emerging trend points to the business value of information governance (IG), defined by Gartner as "the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival, and deletion of information. It includes the processes, roles, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals."

As compared to eDiscovery, information governance as a discipline is relatively new. Yet, there is traction for convergence. eDiscovery—a multi-billion-dollar industry—is rapidly evolving, ready to embrace optimized solutions that strengthen cybersecurity (for cloud computing). Since the early 2000s, eDiscovery practitioners have developed skills and techniques that can be applied to information governance. Organizations can apply the lessons learned from eDiscovery to accelerate their path to a sophisticated information governance framework.

The Information Governance Reference Model (IGRM) illustrates the relationship between key stakeholders and the Information Lifecycle and highlights the transparency required to enable effective governance. The updated IGRM v3.0 emphasizes that Privacy & Security Officers are essential stakeholders. This topic is addressed in an article entitled "Better Ediscovery: Unified Governance and the IGRM," published by the American Bar Association. [31]

See also

Related Research Articles

<span class="mw-page-title-main">Discovery (law)</span> Pretrial procedure in common law countries for obtaining evidence

Discovery, in the law of common law jurisdictions, is a phase of pretrial procedure in a lawsuit in which each party, through the law of civil procedure, can obtain evidence from other parties by means of methods of discovery such as interrogatories, requests for production of documents, requests for admissions and depositions. Discovery can be obtained from nonparties using subpoenas. When a discovery request is objected to, the requesting party may seek the assistance of the court by filing a motion to compel discovery. Conversely, a party or nonparty resisting discovery can seek the assistance of the court by filing a motion for a protective order.

<span class="mw-page-title-main">Computer forensics</span> Branch of digital forensic science

Computer forensics is a branch of digital forensic science pertaining to evidence found in computers and digital storage media. The goal of computer forensics is to examine digital media in a forensically sound manner with the aim of identifying, preserving, recovering, analyzing and presenting facts and opinions about the digital information.

In evidence law, digital evidence or electronic evidence is any probative information stored or transmitted in digital form that a party to a court case may use at trial. Before accepting digital evidence a court will determine if the evidence is relevant, whether it is authentic, if it is hearsay and whether a copy is acceptable or the original is required.

<span class="mw-page-title-main">Digital forensics</span> Branch of forensic science

Digital forensics is a branch of forensic science encompassing the recovery, investigation, examination, and analysis of material found in digital devices, often in relation to mobile devices and computer crime. The term "digital forensics" was originally used as a synonym for computer forensics but has expanded to cover investigation of all devices capable of storing digital data. With roots in the personal computing revolution of the late 1970s and early 1980s, the discipline evolved in a haphazard manner during the 1990s, and it was not until the early 21st century that national policies emerged.

Information lifecycle management (ILM) refers to strategies for administering storage systems on computing devices.

The Electronic Filing System is the Singapore Judiciary's electronic platform for filing and service of documents within the litigation process. In addition, it provides the registries of the Supreme Court and the Subordinate Courts with an electronic registry and workflow system; and an electronic case file. Recent enhancements have added a module which facilitates the conduct of hearing using documents that have been electronically filed.

Email archiving is the act of preserving and making searchable all email to/from an individual. Email archiving solutions capture email content either directly from the email application itself or during transport. The messages are typically then stored on magnetic disk storage and indexed to simplify future searches. In addition to simply accumulating email messages, these applications index and provide quick, searchable access to archived messages independent of the users of the system using a couple of different technical methods of implementation. The reasons a company may opt to implement an email archiving solution include protection of mission critical data, to meet retention and supervision requirements of applicable regulations, and for e-discovery purposes. It is predicted that the email archiving market will grow from nearly $2.1 billion in 2009 to over $5.1 billion in 2013.

<span class="mw-page-title-main">EnCase</span>

EnCase is the shared technology within a suite of digital investigations products by Guidance Software. The software comes in several products designed for forensic, cyber security, security analytics, and e-discovery use. EnCase is traditionally used in forensics to recover evidence from seized hard drives. It allows the investigator to conduct in-depth analysis of user files to collect evidence such as documents, pictures, internet history and Windows Registry information.

Electronically stored information (ESI), for the purpose of the Federal Rules of Civil Procedure (FRCP) is information created, manipulated, communicated, stored, and best utilized in digital form, requiring the use of computer hardware and software.

Document review, in the context of legal proceedings, is the process whereby each party to a case sorts through and analyzes the documents and data they possess to determine which are sensitive or otherwise relevant to the case. Document Review is a valuable main staple of the type of work performed by attorneys for their clients, though it is increasingly common for the work to be performed by specialized document review attorneys.

Early case assessment refers to estimating risk to prosecute or defend a legal case. Global organizations deal with legal discovery and disclosure requests for electronically stored information "ESI" and paper documents on a regular basis.

<i>Zubulake v. UBS Warburg</i> US court case, 2003–2005

Zubulake v. UBS Warburg is a case heard between 2003 and 2005 in the United States District Court for the Southern District of New York. Judge Shira Scheindlin, presiding over the case, issued a series of groundbreaking opinions in the field of electronic discovery. Plaintiff Laura Zubulake filed suit against her former employer UBS, alleging sex discrimination, failure to promote, and retaliation. Judge Shira Scheindlin's rulings comprise some of the most often cited in the area of electronic discovery, and were made prior to the 2006 amendments to the Federal Rules of Civil Procedure. The relevant opinions in the field are known as Zubulake I, Zubulake III, Zubulake IV, and Zubulake V. In 2012, the plaintiff published a book about her e-discovery experiences titled Zubulake's e-Discovery: The Untold Story of my Quest for Justice.

Zubulake v. UBS Warburg is a landmark decision in the area of electronic discovery and the burden of costs for such discovery. It was released on May 13, 2003 and was written by Judge Shira A. Scheindlin of the United States District Court for the Southern District of New York. It is the first in a series of Zubulake judgements relating to discovery issues, and is also referred to as "Zubulake I". See section "Other Proceedings" for information on other Zubulake decisions.

<span class="mw-page-title-main">Digital forensic process</span>

The digital forensic process is a recognized scientific and forensic process used in digital forensics investigations. Forensics researcher Eoghan Casey defines it as a number of steps from the original incident alert through to reporting of findings. The process is predominantly used in computer and mobile forensic investigations and consists of three steps: acquisition, analysis and reporting.

Information Discovery is a term used in the legal and corporate industry which refers to the steps involved in distilling a corporation's data corpus down to the most pertinent evidence pertaining to a court-related matter or compliance directive. The major information discovery steps include: managing the entire data collection in a manner to identify all pertinent evidence associated with the matter, targeting that information for collection, processing and identification (culling) of relevant data, and processing for document hosting and legal document/information review.

Forensic search is an emerging field of computer forensics. Forensic search focuses on user created data such as email files, cell phone records, office documents, PDFs and other files that are easily interpreted by a person.

Gates Rubber Company v. Bando Chemical Industries, Ltd., et al. is a decision by the U.S. district court for the District of Colorado from May 1, 1996. It is considered a landmark decision in terms of expert witness court testimony in questions of electronic evidence and digital forensics.

<span class="mw-page-title-main">Legal technology</span> Technology and software to provide legal services

Legal technology, also known as Legal Tech, refers to the use of technology and software to provide legal services and support the legal industry. Legal Tech companies are often startups founded with the purpose of disrupting the traditionally conservative legal market.

The Sedona Canada Principles are a set of authoritative guidelines published by The Sedona Conference to aid members of the Canadian legal community involved in the identification, collection, preservation, review and production of electronically stored information (ESI). The principles were drafted by a small group of lawyers, judges and technologists called the Sedona Working Group 7 or Sedona Canada. Sedona Canada is an offshoot of The Sedona Conference which is an American “non-profit…research and educational institute dedicated to the advanced study of law and policy in the areas of antitrust law, complex litigation, and intellectual property rights.”

A machine-readable document is a document whose content can be readily processed by computers. Such documents are distinguished from more general machine-readable data by virtue of having further structure to provide the necessary context to support the business processes for which they are created.

References

  1. 1 2 Various (2009). Eoghan Casey (ed.). Handbook of Digital Forensics and Investigation. Academic Press. p. 567. ISBN   978-0-12-374267-4 . Retrieved 27 August 2010.
  2. ↑ "Federal Rules of Civil Procedure". LII / Legal Information Institute.
  3. ↑ "2015 Amendments" (PDF). Archived from the original (PDF) on 2017-06-12. Retrieved 2017-06-27.
  4. ↑ Ministry of Justice, PART 31 - DISCLOSURE AND INSPECTION OF DOCUMENTS, accessed 11 September 2022
  5. ↑ Ministry of Justice, PRACTICE DIRECTION 31B – DISCLOSURE OF ELECTRONIC DOCUMENTS, last updated 1 October 2020, accessed 11 September 2022
  6. ↑ "Judge Scheindlin Brought Great Insight and Leadership". March 28, 2016.
  7. ↑ "Case Law AJ Holdings v. IP Holdings". January 13, 2015.
  8. ↑ Logikcull. "Legal Hold and Data Preservation | Ultimate Guide to eDiscovery | Logikcull". Logikcull. Retrieved 2018-06-08.
  9. ↑ Qualcomm v. Broadcom: Implications for Electronic Discovery accessdate=2014-10-19
  10. ↑ Sullivan, Casey C. "How the IoT Is Solving Murders and Reshaping Discovery" . Retrieved 2018-06-08.
  11. ↑ Kincaid, Jason (February 11, 2009). "The AP Reveals Details of Facebook/ConnectU Settlement With Greatest Hack Ever". TechCrunch .
  12. ↑ Schneier, Bruce (June 26, 2006). "Yet Another Redacting Failure]. Schneier on Security". Schneier.com.
  13. ↑ "The Sedona Conference®". thesedonaconference.org.
  14. ↑ "Method and system for searching for, and collecting, electronically-stored information". Elliot Spencer, Samuel J. Baker, Erik Andersen, Perlustro LP. 2009-11-25.{{cite journal}}: Cite journal requires |journal= (help)CS1 maint: others (link)
  15. ↑ Richard, Adams; Graham, Mann; Valerie, Hobbs (2017). "ISEEK, a tool for high speed, concurrent, distributed forensic data acquisition". Research Online. doi:10.4225/75/5a838d3b1d27f.
  16. ↑ "Digital Forensics Services". www.ricoh-usa.com.
  17. ↑ https://espace.curtin.edu.au/bitstream/handle/20.500.11937/93974/Adams%20RB%202023%20Public.pdf?sequence=1&isAllowed=y
  18. ↑ Grossman, Maura R.; Cormack, Gordon V. (January 2013). "Grossman-Cormack glossary of technology-assisted review with foreword by John M. Facciola, U.S. Magistrate Judge" (PDF). Federal Courts Law Review. 7 (1). Stannardsville, Virginia: Federal Magistrate Judges Association: 6. Retrieved August 14, 2016.
  19. ↑ Gricks, Thomas C. III; Ambrogi, Robert J. (November 17, 2015). "A brief history of technology assisted review". Law Technology Today. Chicago, Illinois: American Bar Association . Retrieved August 14, 2016.
  20. ↑ Sedona Conference. TAR Case Law Primer Public Comment Version August 2016 Retrieved August 17, 2016
  21. ↑ Roitblat, Herbert; Kershaw, Anne. "Document categorization in legal electronic discovery: Computer classification vs. manual review". Journal of the Association for Information Science and Technology. 61 (1). Hoboken, New Jersey: Wiley-Blackwell: 1–10. Retrieved August 14, 2016.
  22. ↑ Richmond Journal of Law & Technology Technology-assisted review in ediscovery can be more effective and more efficient than manual review Retrieved August 14, 2016
  23. ↑ S.D.N.Y. (2012). Moore v. Publicis Archived 2013-03-10 at the Wayback Machine Retrieved August 13, 2016.
  24. ↑ High Court, Ireland (2015). Irish Bank Resolution Corporation Limited v. Sean Quinn Retrieved August 13, 2016
  25. ↑ High Court of Justice Chancery Division, U.K. (2016). Pyrrho Investments Ltd v. MWB Property Ltd Retrieved August 13, 2016
  26. 1 2 S.D.N.Y (2015). Rio Tinto v. Vale Retrieved August 14, 2016
  27. ↑ S.D.N.Y. (2016). Hyles v. New York City Retrieved August 14, 2016
  28. ↑ Practical Law Journal (2016). Continuous Active Learning for TAR Retrieved August 14, 2016
  29. ↑ Sedona Conference (2007). Best practices recommendations & principles for addressing electronic document production Archived 2016-07-06 at the Wayback Machine
  30. ↑ Grossman, Maura R., and Gordon V. Cormack. "A tour of technology-assisted review." Perspectives on Predictive Coding and Other Advanced Search and Review Technologies for the Legal Practitioner (ABA 2016) (2016).
  31. ↑ Article (2012). Ledergerber, Marcus (ed.). "Better Ediscovery: Unified Governance and the IGRM". American Bar Association. Archived from the original on 2016-10-11. Retrieved 2016-08-21.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.