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  • Rahul Vijh

Unlocking the Power of Technology Assisted Review: Best Practices


In the digital age, vast amounts of information and data are generated and stored, creating a significant challenge for organizations when it comes to reviewing and analyzing documents for legal, regulatory, or investigative purposes. Traditional manual review methods often prove time-consuming, costly, and prone to human error. This is where Technology Assisted Review (TAR) comes into play, offering a more efficient and accurate approach to document review.

Technology Assisted Review, also known as Predictive Coding or Computer-Assisted Review, is a process that utilizes advanced machine learning algorithms and artificial intelligence (AI) to assist in the review and analysis of large volumes of electronic documents. TAR combines human expertise with the power of technology to streamline and enhance the document review process.

At its core, TAR employs sophisticated algorithms to categorize and prioritize documents based on their relevance to a particular case or investigation. Initially, human reviewers code a subset of documents, known as the "seed set," providing the system with examples of relevant and non-relevant documents. The TAR system then analyzes the characteristics of these documents, identifying patterns and learning to make predictions about the relevance of unseen documents. As the process continues, the system refines its predictions, and human reviewers validate and train the system by reviewing additional subsets of documents. This iterative feedback loop allows TAR to improve its accuracy over time.

The benefits of Technology Assisted Review are numerous. Firstly, TAR significantly reduces the time and effort required for document review. By automating the initial stages of document categorization and prioritization, TAR eliminates the need to manually review every single document, enabling reviewers to focus their attention on documents that are more likely to be relevant. This not only accelerates the review process but also reduces costs associated with manual labor.

Moreover, TAR enhances accuracy and consistency. Unlike manual review, which can be influenced by human biases and inconsistencies, TAR applies consistent algorithms that base their decisions on learned patterns and statistical analysis. By leveraging machine learning, TAR can achieve a level of precision that surpasses traditional methods, minimizing the risk of missing critical documents or including irrelevant ones.

Additionally, TAR offers defensibility and transparency. The system maintains a record of the decisions made during the review process, including the rationale behind them. This audit trail can be invaluable when demonstrating the reasonableness of the review methodology to courts or regulatory bodies, providing a transparent and defensible approach.

Best Practices for a successful TAR

Here are some key practices to keep in mind:

1. Understand the Technology: Gain a solid understanding of how TAR works, its capabilities, and limitations. Educate yourself and your team on the underlying algorithms, statistical models, and machine learning techniques employed by the TAR system. This knowledge will help you set realistic expectations, make informed decisions, and effectively communicate the process to stakeholders.

2. Develop a Comprehensive Plan: Before starting the TAR process, create a well-defined plan that outlines the objectives, scope, and timeline of the review project. Identify the key stakeholders, establish roles and responsibilities, and allocate necessary resources. Having a clear plan in place ensures a structured and organized approach throughout the TAR implementation.

3. Establish a Quality Control Framework: Implement a robust quality control framework to assess the accuracy and consistency of the TAR system's predictions. This involves regularly sampling and validating the system's output against a subset of documents manually reviewed by human experts. Continuously monitor and refine the TAR process based on feedback and ongoing quality control measures.

4. Use Experienced Reviewers: While TAR significantly reduces the volume of documents that need manual review, the expertise of human reviewers remains crucial. Select reviewers who are knowledgeable about the subject matter and possess the necessary skills to train and validate the TAR system effectively. Adequate training and ongoing communication with reviewers are essential to ensure consistent and accurate results.

5. Develop Appropriate Training Sets: The initial training sets, known as "seed sets," play a pivotal role in the TAR process. Carefully select documents that represent the relevant and non-relevant categories to train the system accurately. Ensure diversity in the training sets by including documents with various characteristics, complexities, and potential issues that may arise during the review.

6. Iterative Process and Continuous Learning: TAR is an iterative process that improves over time. It is crucial to embrace this iterative nature and continually refine the system based on feedback and new data. As the review progresses, periodically reassess and update the training sets to account for changes in the case, new document types, or emerging patterns that were not initially apparent.

7. Document and Document the Process: Maintain detailed documentation throughout the TAR process, recording key decisions, parameters, and modifications made during the review. This documentation serves as an audit trail and helps provide defensibility and transparency, particularly when explaining the TAR methodology to courts, regulators, or opposing parties.

8. Validate and Measure Effectiveness: Assess the effectiveness of the TAR process by comparing the results with established metrics and benchmarks. Consider metrics such as precision, recall, and F1 score to evaluate the system's performance. Regularly evaluate and validate the output to ensure that the TAR system is achieving the desired level of accuracy and meeting the project goals.

9. Consult Experts and Seek Guidance: If you are new to TAR or handling a complex case, it can be beneficial to seek guidance from experts or consult with experienced professionals in the field. Engaging consultants or partnering with eDiscovery service providers who specialize in TAR can help you navigate the challenges, optimize the process, and achieve the best possible outcomes.


To know more about Copperpod's TAR document review services, please contact us at info@copperpodip.com.


By adhering to these best practices, you can effectively implement Technology Assisted Review and leverage its capabilities to streamline document review, enhance accuracy, and achieve cost and time savings in various legal, regulatory, or investigative scenarios.

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