TAR is crucial when reviewing documents, and accuracy is a must. Predictive technologies such as Technology Assisted Review or (TAR) are gaining immense popularity in the legal framework. This artificial intelligence system allows all litigators to streamline the process of reviewing all the documents involved.
These kinds of applications can quickly analyze data sets and offer statistics, categorization, and data reporting that is generally much quicker and more efficient than human review alone.
Litigators generally use such technology to carry out eDiscovery tasks and settlement evaluations properly.
Here’s what you should know about the cost and benefits associated with the TAR process.
What Do You Understand About It?
Technology Assisted Review refers to the approach regarding the document review phase of eDiscovery that leverages all the computer algorithms to identify or tag all potentially responsive documents. This helps expedite your document review procedure.
Significant Steps Involved in Its Framework
The process of designing technology assisted reviews is more challenging than it seems. It’s important to follow several steps to ensure optimal success.
Setting Protocol
TAR is the process of building any human coding rules that consider the usage of TAR. It can also be taught regarding document collection by having all the human reviewers submit various documents.
Therefore, creating any coding protocol that appropriately incorporates the case pattern alongside the training requirements of this specific review system may occur at this stage.
Reviews
The process to transfer the review information protocol regarding the human review process will help start the review process of TAR.
Coding the Documents
The process of reviews, especially the human ones, refers to the subjective coding decisions related to documents. It is done to train the TAR system effectively.
Predicting Results
The process involved in this system gets applied to the information on learned systems related to human reviewers. It also classifies selected document corpus related to predetermined labels.
Testing Results
The human reviewer’s procedure uses a validation process that typically involves statistical sampling. This is done to create meaningful metrics related to the TAR performance.
The metrics can be taken in many forms. For example, this may include estimates of defect counts related to the classified population. It also uses the information to retrieve metrics of various kinds.
Evaluating and Achieving Results
The review team decides whether or not the TAR system has received the anticipated goals. It also shows the process of ending the system, where its workflow moves to the next phase of the review lifecycle.
How Does It Work?
TAR works a lot like your Spotify playlist, which is continually guided per the listener’s preferences. The same goes for streaming platforms such as Netflix, which make recommendations based on your previous entertainment choices.
Similarly, TAR follows a judgment related to human subject matter experts for determining document responsiveness.
There are two variations of it:
- TAR 1.0, known as “predictive coding.”
- TAR 2.0, known as “continuous active learning.”
Both of these versions train algorithms and follow a workflow to make relevant decisions. There may be differences in how the algorithms are trained based on whether you’re using TAR 1.0 or TAR 2.0.
Wrapping Up
This technology-assisted system has bolstered the legal framework by streamlining the review process for all the pertinent documents. TAR enables litigators to focus more of their time on high-leverage tasks relative to their cases without having to waste as much time on mundane tasks.
Among all the eDiscovery approaches, TAR is among the most widely employed. As more electronic information continues to be processed and stored every day, in litigation, TAR’s role in the eDiscovery process only figures to further expand.
