E-Discovery, the process of identifying, collecting, and producing electronic documents in response to legal matters, has become increasingly complex as the volume of electronically stored information (ESI) grows. One way to tackle this massive challenge is through predictive coding, which has revolutionized the industry.
For the uninitiated, predictive coding ediscovery, also known as technology-assisted review (TAR), is a sophisticated machine learning technique that automates the document review process. In short, algorithms analyze a sample set of documents that have already been manually reviewed by lawyers and use that information to predict which documents are likely to be relevant to a particular case.
The following points explore how this technique has made the e-discovery process easier and more efficient.
How It Works
These algorithms use statistical models to identify patterns in the language and metadata of documents. It identifies the most relevant words and phrases in the sample set and uses those words and phrases to create a predictive model. It then applies that model to the entire documentation set, scoring each piece of information for relevance.
The system uses feedback loops to improve its predictive assessment continuously. As lawyers review files, they provide feedback on the relevance of each. The design incorporates this feedback into its model, improving its accuracy over time.
Its Benefits in E-Discovery
Now that you know how the approach works, learn about its benefits to determine its usefulness.
Increased Efficiency
It significantly reduces the time and cost associated with manual document review, as in the traditional process, lawyers must manually review every document to determine relevance. Fortunately, predictive models streamline this process by identifying the most relevant review materials, reducing the amount of data humans must review. Besides saving time, it reduces the risk of missing relevant information.
Improved Consistency
Humans are subject to biases and inconsistencies in their review process, but predictive coding algorithms are not. Thereby, the system improves the consistency of document review and applies the same criteria to every documentation, reviewing each consistently and objectively.
Enhanced Accuracy
These algorithms have been shown to be more accurate than human reviewers. Experts believe they can identify relevant documents with an accuracy rate of over eighty percent, compared to a much lower accuracy rate for human reviewers. This means the technique is more efficient and accurate than manual review.
Reduces the Risk of Error
It reduces the risk of error in the review process. Human reviewers are prone to mistakes, such as misinterpreting the relevance of a file or missing a relevant document altogether. However, predictive algorithms are less likely to make these mistakes, reducing the risk of error in the overall process.
Pick a Reliable Solution
Choosing the right software for predictive coding in ediscovery will ensure the process is effective and efficient. Since many options are available, it is vital to research and choose one that meets the specific needs of your project. It should handle the volume of data being reviewed, have a user-friendly interface, and provide accurate results.
It is also essential to ensure the tool is compatible with the technology and systems being used by your legal team. These features will help you scale the solution on significant cases that require extensive documentation review. Finally, finding a trustworthy software provider will ensure access to a high-end tool, making your legal processes straightforward and smoother.
