Relativity TAR and Technology Assisted Review: A Practical Guide for Modern eDiscovery
Technology Assisted Review (TAR) has reshaped the way legal teams handle large document collections. When paired with Relativity TAR, organizations gain a scalable, defensible approach to identify relevant content while minimizing cost and manual labor. This guide explains what TAR is, how Relativity TAR works, and best practices to maximize accuracy, speed, and defensibility in real-world investigations and litigations.
What is TAR and why Relativity TAR stands out
Technology Assisted Review, sometimes called predictive coding, uses machine learning to classify documents as relevant or non-relevant based on a labeled training set. In Relativity TAR, teams begin by creating a seed set of documents that human reviewers judge for relevance. The model then learns from these judgments and applies its predictions to the rest of the collection. As reviewers validate results and provide additional labels, the model improves in a process called iterative training. Relativity TAR integrates this capability into the broader Relativity platform, offering workflow controls, documentation, and audit trails that are essential for defensibility.
Key components of a Relativity TAR workflow
Understanding the typical TAR workflow helps teams plan efficiently and avoid common missteps. The core stages include preparation, training, evaluation, and review production. Each stage relies on careful decisions about data, labeling, and validation.
Preparation and scoping
- Define the matter’s goals, scope, and budget up front to guide sampling and training coverage.
- Identify data sources, custodians, and potential duplicates to inform processing and deduplication strategies.
- Assemble a qualified team of reviewers who can provide consistent labeling and context for the model.
Seed set creation and model training
- Build a representative seed set that captures the diversity of the collection, including various document types, time periods, and communication styles.
- Label seed documents as relevant or non-relevant, with clear criteria to minimize ambiguity.
- Run initial modeling rounds and review a sampling of results to assess whether the model captures key concepts.
Evaluation and stabilization
- Evaluate model performance using human review on a validation set and, if possible, historical benchmarks or gold standards.
- Continue iterative training until the model’s predictions stabilize and reviewer consensus increases.
- Guard against overfitting by testing the model on unseen data and ensuring coverage across the full dataset.
Review, export, and defensibility
- Proceed with the prioritized review according to the model’s ranking, integrating human judgment where needed for edge cases.
- Document all steps, including seed selection, labeling decisions, model versions, and validation results for court-ready defensibility.
- Prepare an auditable export, including production-ready metadata and hashing information, to support disclosure obligations.
Benefits of using Relativity TAR for eDiscovery
Relativity TAR offers tangible advantages that align with the demands of modern litigation and investigations. Users frequently report faster results, lower costs, and clearer documentation trails. The combination of machine-assisted screening and a robust platform helps legal teams scale reviews without sacrificing accuracy or compliance.
- Cost control: By focusing human effort on highly relevant documents, teams can lower review labor and associated costs.
- Consistency and reproducibility: A transparent, repeatable workflow delivers consistent outcomes across large datasets.
- Defensibility: Detailed documentation and controlled processes support legal standards and court rules for TAR methodologies.
To maximize the value of Relativity TAR, teams should align technology choices with governance, quality control, and stakeholder expectations. The following considerations help ensure a smooth, defensible implementation.
Governance and policy
- Establish a TAR governance plan that defines roles, approvals, and review thresholds for model changes.
- Clarify the criteria for seed set adequacy and model stabilization before proceeding to production.
- Ensure adherence to jurisdictional disclosure requirements and preserve chain-of-custody documentation.
Data quality and preparation
- De-duplicate and normalize data to reduce noise and minimize redundancy in training.
- Address near-duplicate content and non-text formats through appropriate processing settings.
- Identify privileged or sensitive material and establish safeguards within the TAR workflow.
Validation and bias mitigation
- Use diverse seed sets and periodically test the model against new samples to detect drift or bias.
- Incorporate an independent quality control step to verify model performance before full production.
- Document any limitations or assumptions embedded in the model’s design.
Common challenges and how to counter them
Despite its strengths, TAR is not a silver bullet. Teams should anticipate and address typical challenges to maintain confidence in results.
A narrow seed set can skew the model. Counter this by deliberately including edge cases, different document types, and communications across time frames. - Over-reliance on automation: TAR should complement human judgment, not replace it entirely. Maintain critical review for high-stakes decisions.
- Inadequate documentation: Without a clear audit trail, defensibility suffers. Capture every modeling iteration, labels, and rationale.
- Complex data landscapes: Multilingual content, scanned images, and unusual file types require careful processing and, when needed, targeted manual review.
Use cases and practical tips for teams
Relativity TAR is versatile across several eDiscovery scenarios. Here are common use cases and practical tips to maximize outcomes.
Deploy TAR to quickly identify relevant communications, contracts, and directives while keeping the review focused on material issues. - Internal investigations: Use TAR to surface potentially problematic patterns and summarize findings for investigators and leadership.
- Long-tail data projects: For collections spanning years and multiple custodians, TAR helps manage scope creep by maintaining consistent relevance criteria.
Best practices checklist for Relativity TAR success
Adopting a structured approach can help teams realize the full value of TAR without compromising quality.
- Define scope, goals, and success criteria at the outset.
- Assemble a qualified reviewer panel and establish labeling guidelines.
- Design a representative seed set that reflects the matter’s factual and legal nuances.
- Iterate training with ongoing validation to ensure stability and coverage.
- Maintain a thorough, auditable trail of decisions and model versions.
- Plan for review handoffs, export formats, and production metadata to support downstream processes.
Conclusion: making TAR work in today’s legal landscape
Relativity TAR combines the strengths of machine-assisted learning with the rigor and governance demanded by modern legal practice. When applied thoughtfully, it can shorten discovery timelines, lower costs, and deliver defensible results that stand up to scrutiny. The key to success lies in careful preparation, vigilant validation, and disciplined documentation. By embracing a practical TAR workflow—rooted in seed quality, iterative training, and transparent reporting—legal teams can navigate complex investigations with confidence, clarity, and measurable impact.
