Document review is a fundamental process in the legal industry where parties in a lawsuit or investigation exchange information. This involves the systematic examination of high volumes of electronically stored information (ESI) to prepare for litigation, regulatory inquiry, or internal audit. It is a necessary step that ensures fairness and transparency by requiring the disclosure of relevant facts, allowing legal teams to build their case theories.
Defining Document Review in Legal Proceedings
Document review is the analytical stage where a vast repository of collected data is assessed for its legal significance in a specific matter. The primary goal is to convert raw information, which can include emails, spreadsheets, and text messages, into usable evidence or protected material. Reviewers methodically apply specific legal criteria to each item, determining if it must be produced to the opposing party, withheld, or redacted.
The outcome of this examination dictates which documents are shared with opposing counsel, logged as privileged, or retained solely for internal use. This stage helps legal teams understand the factual landscape of their case and comply with court-ordered discovery rules.
The E-Discovery Process and Document Review’s Role
Document review functions as the central and often most time-intensive stage within the larger framework of the Electronic Discovery Reference Model (EDRM). The EDRM outlines a linear sequence of steps that govern how electronically stored information (ESI) is managed from the moment a legal hold is issued.
Before the review begins, several preparatory steps must be completed to ensure the data is ready for analysis. These stages involve the Identification of potential data sources, Preservation to prevent modification, Collection from native locations, and Processing to convert disparate ESI into a standardized, searchable format. This preparatory work transforms raw data into structured documents uploaded to a review platform. Review is the point where human legal judgment is first applied to the processed ESI, consuming the largest share of the budget and time for most discovery projects.
Understanding Review Criteria (Responsiveness and Privilege)
The core intellectual labor of the reviewer involves applying two main substantive criteria to every document: responsiveness and privilege. Responsiveness refers to whether a document relates to the claims, defenses, or subject matter of the lawsuit or investigation. Documents deemed responsive are generally subject to production to the opposing party.
The concept of privilege allows a party to withhold documents from production, and it is primarily defined by the Attorney-Client Privilege and the Work Product Doctrine. The Attorney-Client Privilege protects confidential communications between an attorney and a client for the purpose of seeking legal advice. The Work Product Doctrine shields materials prepared in anticipation of litigation, protecting the lawyer’s mental impressions, case strategy, and legal theories from disclosure.
Reviewers must meticulously identify and log every document withheld based on privilege, creating a privilege log that describes the document, its author, recipient, date, and the specific privilege asserted. Documents may also require redaction if they contain confidential information, such as trade secrets or personally identifiable information (PII). Redaction obscures portions of a document that fall outside the scope of discovery, ensuring sensitive details are protected while the remainder is produced.
Technology Transforming Document Review
Modern document review is heavily reliant on specialized software platforms that manage, host, and facilitate the analysis of massive datasets. Industry-standard platforms, such as Relativity, Nuix, and Casepoint, provide the interface where reviewers examine documents and apply legal coding. The shift from paper-based review to these digital environments has fundamentally changed the speed and scale of discovery.
The most significant technological advancement is the integration of Artificial Intelligence (AI) and Machine Learning, specifically through Technology Assisted Review (TAR), also known as predictive coding. TAR systems are trained by human subject-matter experts who code a small sample of documents as relevant or non-relevant. The algorithm then analyzes the features of these coded documents and scores the remaining document population for its likelihood of relevance.
This process allows legal teams to prioritize the highest-scoring documents for human review, dramatically reducing the total volume of data that must be manually examined. TAR improves the consistency of coding decisions and significantly lowers the overall cost of discovery, offering a more efficient and defensible review process.
Document Review as a Career Path
The work of document review is primarily performed by contract attorneys, often licensed lawyers who work on a project-by-project basis. This type of employment is characterized by its temporary, project-based nature, with work fluctuating depending on the demand for large-scale litigation support. Many projects are now conducted remotely, though specialized review centers still operate to handle highly sensitive or classified data.
Compensation for contract document reviewers is typically structured as an hourly rate, which can range widely depending on location, required subject-matter expertise, and foreign language skills. Hourly rates often fall between $30 and $50 per hour, though higher rates are seen in major legal markets or for specialized work.
Beyond the contract reviewer, the field includes specialized roles such as E-Discovery Project Managers and Litigation Support Analysts. These managerial and specialist positions require a deep understanding of the EDRM process and the underlying review technology. The broader e-discovery field provides opportunities for career progression into technology consulting and information governance, offering a blend of legal analysis and technical expertise.
Conclusion
Document review represents a high-stakes function that underpins the fairness and factual development of the modern legal system. It serves as the gateway through which relevant information is extracted from massive stores of data. This field has transformed into a sophisticated, technologically-driven practice area, increasingly defined by the use of machine learning to manage data volumes.