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  5. Technology-Assisted Review (TAR)

Technology-Assisted Review (TAR)

A court-accepted eDiscovery methodology using machine learning to rank documents by relevance, reducing manual review volume; also called CAL or CAR.

Last reviewed: 2026/05/19

Definition

Why It Matters for Lawyers

How AI Tools Handle It

Frequently Asked Questions

Q: Do I need opposing counsel's agreement to use TAR?
Best practice is to disclose TAR use and negotiate protocols with opposing counsel. Courts have sometimes required disclosure even when parties did not initially agree. Unilateral TAR implementation without disclosure creates challenge risk. Consult applicable court rules and standing orders.
Q: How do I know when my TAR review is complete?
Completeness is validated through recall sampling — reviewing a random sample of documents coded not-relevant by the model and measuring how many are actually relevant. A recall rate above a negotiated threshold (commonly 70-80% in civil litigation) indicates adequate completeness. The appropriate threshold is case-dependent.
Q: Is TAR appropriate for privilege review?
Generally no. TAR is typically used for relevance review. Privilege review requires attorney judgment on legal standards, often with fact-specific analysis that does not train well in a TAR model. Many practitioners use TAR for relevance review and then conduct manual privilege review on the TAR-prioritized relevant population. --- *Last reviewed: 2026-05-19 by LawyerAI Editorial Team.*

Related Concepts

Capability

Predictive Coding (eDiscovery)

A TAR technique where the system learns from attorney-coded seed documents to predict relevance across the full document set; court acceptance depends on validation methodology.

Capability

Active Learning (eDiscovery)

An iterative ML approach in eDiscovery where the model continuously updates relevance predictions as reviewers code documents, prioritizing the most uncertain documents for review.

Security

Legal Hold (AI-Assisted)

Using AI to identify, notify custodians, and track preservation obligations when litigation or investigation triggers a duty to preserve electronically stored information.

Related Tools

  • DISCO

    AI-native legal technology platform for eDiscovery, case building, and legal holds used by Am Law 200 firms.

  • Logikcull

    Self-service eDiscovery platform designed for instant setup, used by solo firms through Fortune 500 legal teams.

Related Reading

  • How We Score Legal AI Tools: The 5-Dimension Methodology
  • AI Hallucination in Legal Research: A Practitioner's Guide

Last reviewed: 2026/05/19. Definitions are written by the LawyerAI Editorial team. We do not accept affiliate commissions; Featured placement is clearly labeled and does not influence editorial content.

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Editorially independent. Methodology open and versioned.
© 2026LawyerAI Editorial

Technology-assisted review (TAR) is a court-accepted eDiscovery methodology that uses machine learning algorithms to prioritize, rank, or classify documents by predicted relevance, substantially reducing the volume of documents requiring manual attorney review. Also referred to as computer-assisted review (CAR) or continuous active learning (CAL) when using iterative training methods, TAR learns from attorney coding decisions and applies those decisions to the broader document set. Courts have accepted TAR as an appropriate review methodology when parties agree on protocols and validate outcomes through recall and precision sampling.

Document review is the largest cost component of civil litigation, consuming 70-80% of eDiscovery budgets in large cases. A large commercial dispute may involve millions of documents; manual review of every document is economically prohibitive. TAR addresses this by focusing attorney review time on the documents most likely to be relevant, substantially reducing review volume without proportionally reducing recall.

The court acceptance history of TAR is well-established. The Da Silva Moore, Rio Tinto, and Progressive Casualty cases established federal court acceptance of TAR methodology in the early 2010s. Courts generally accept TAR when parties negotiate protocols in advance, document the training process, and validate outcomes through agreed sampling.

Lawyers using TAR must understand the methodology sufficiently to defend it. Opposing counsel may challenge TAR protocols — questioning seed set selection, training document quality, or recall validation sampling. The supervising attorney must be able to explain and defend the TAR process, not merely delegate it entirely to a vendor.

Relativity is the dominant eDiscovery platform for large-scale TAR, with its Active Learning module implementing continuous active learning that prioritizes documents for review as reviewers code, continuously updating predictions. DISCO integrates TAR within its cloud-native eDiscovery platform, with visualization tools that show review progress and estimated remaining relevant document populations.

Logikcull offers TAR functionality with a more accessible interface designed for smaller matters and less experienced users, reducing the technical barrier to TAR adoption in mid-market litigation.