Insights Articles

What is Technology-Assisted Review?

Technology-Assisted Review is a category of tools that uses machine learning to help prioritize, categorize, and rank documents based on relevance. Often referred to as predictive coding, TAR learns from attorney decisions on a sample set of documents and applies that learning across the broader data population.

In practical terms, TAR supports AI legal review by identifying patterns in how reviewers label documents—responsive, non-responsive, privileged, or issue-specific—and predicting how similar documents should be treated.

• TAR is not new. Courts have accepted its use for years, and it has become a standard component of defensible eDiscovery workflows, particularly in large-scale matters where manual review alone is impractical.
  
  

What TAR Actually Does 

1. Prioritizes documents more intelligently

TAR excels at ranking documents by likely relevance. Instead of reviewing documents in a random or chronological order, legal teams can focus first on materials most likely to matter. This accelerates early case assessment and helps attorneys understand key facts sooner.

2. Reduces review volume and cost

By identifying low-value documents with high confidence, TAR allows teams to reduce the number of documents requiring full human review. Fewer eyes on fewer documents translates into lower review costs and faster timelines—without sacrificing defensibility.

3. Improves consistency across reviewers

Human reviewers vary. TAR applies learned decision patterns uniformly across the dataset, helping reduce inconsistencies that naturally occur in large review teams. This is particularly valuable in matters involving multiple reviewers or long review timelines.

4. Supports defensible workflows

When implemented correctly—with proper training, validation, and quality control—TAR workflows are highly defensible. Courts have repeatedly recognized TAR as an acceptable and often preferable alternative to exhaustive manual review.

What TAR Does Not Do

Despite its strengths, TAR has limitations—and misunderstanding them is where risk creeps in.

1. TAR does not replace legal judgment

TAR learns from humans; it does not think like one. Decisions about relevance, privilege, and strategy still require attorney oversight. AI legal review tools amplify expertise—they don’t substitute for it.

2. TAR does not eliminate the need for quality control

Even the most advanced predictive coding models require ongoing validation. Sampling, testing, and human review remain critical to ensuring accuracy and defensibility. TAR without QC is not a shortcut—it’s a liability.

3. TAR does not fix poor data or unclear objectives

If the underlying data is incomplete, poorly processed, or scoped incorrectly, TAR cannot compensate. Likewise, vague review criteria or inconsistent training decisions will produce unreliable results. Successful TAR starts with clear goals and clean inputs.

4. TAR does not mean “set it and forget it”

Effective TAR workflows are iterative. Models improve as they receive more feedback, and review strategies may evolve as a case develops. Treating TAR as a one-time exercise undermines its value.

Where Legal Teams See the Most Value

TAR offers value for legal teams of all sizes. It can streamline high-volume litigation, support internal investigations, handle regulatory responses, and help control review costs. Paired with experienced litigation support partners, TAR workflows can be tailored to each team’s priorities, delivering efficiency and defensibility without adding unnecessary complexity.

The Role of the Right Partner

The effectiveness of TAR depends not just on the technology, but on how it’s implemented. Successful AI legal review requires a combination of advanced tools, proven workflows, and experienced professionals who understand both the technology and the legal implications.

At Array, TAR is integrated into a broader, end-to-end litigation support approach that balances innovation with defensibility. Our teams work closely with law firms and in-house legal departments to design customizable review workflows that combine predictive coding, Continuous Active Learning (CAL), generative AI, and attorney oversight. This approach helps prioritize what matters most, accelerate review cycles, maintain rigorous quality control, and keep human judgment central to key decisions—ensuring efficiency, defensibility, and risk mitigation across every matter.

Final Thoughts

Technology-Assisted Review is neither magic nor menace. It is a powerful tool—when used with intention, expertise, and transparency. Understanding what TAR actually does (and doesn’t) empowers legal teams to make smarter decisions, manage discovery more effectively, and deliver better outcomes for clients.

In an era of growing data volumes and rising expectations, informed use of TAR isn’t just an advantage—it’s essential.