AI Use Cases for eDiscovery You May Not Know
Artificial Intelligence (AI) has become a buzzword not only in broadly speaking about technology, but also when it comes to eDiscovery. This leaves many legal professionals wondering how to differentiate which AI technology has been proven and practically applied to eDiscovery, and which is speculative and unproven. The good news is there are several practical applications of AI technology that have been proven to be effective in streamlining eDiscovery workflows for years.
Predictive Coding
When most people think of AI applied to eDiscovery workflows, they think of predictive coding, which is the application of machine learning technology to rank and categorize documents for review. Predictive coding is a widely known application of AI technology for eDiscovery that has been court approved for over a decade and industry approved for even longer. If you have worked on many eDiscovery projects, chances are you have used predictive coding on a case at some point.
Other AI Use Cases for eDiscovery
However, predictive coding is not the only proven, practical application of AI technology currently available today for eDiscovery professionals. There are several other applications of AI you can apply to streamline eDiscovery workflows, including:
Near-Duplicate Identification
Many documents can be similar in content and format, but not exact duplicates – for example, multiple revisions of a single document or a document that has been “printed” to PDF format (where the content may be the same, but the Hash value for deduplication will be different). AI technology can be used to automatically group textually-similar documents together, allowing for faster review of large amounts of records.
Email Thread Identification
Every email is a snapshot of the conversation up to that point, which means that portions of the conversation will appear repeatedly. AI technology enables you to group messages within an email chain to identify the most comprehensive versions of emails and navigate conversations more intuitively than in traditional review workflows.
Language Identification
Many eDiscovery projects involve international custodians, which means document collections often involve multiple languages. Some documents have more than one language within the document itself. AI technology enables your team to support multi-lingual data and increase review efficiency by automatically identifying primary and secondary languages on all documents in your data set, enabling you to direct those documents to the review teams best equipped to review in those languages.
Automatic Redaction
With increasingly stringent data privacy laws, identifying and redacting personally identifiable information (PII) is more important than ever and the need to identify and redact privileged and confidential information is constant. Applying those redactions manually is extremely time consuming and expensive. AI technology can help automate the application of redactions, with the ability to review the results and rollback redactions that are applied inaccurately, helping to streamline a notoriously labor-intensive process.
Natural Language Processing (NLP)
Every document collection has a unique collection of locations, events, key people and organizations, product names, and other characteristics which are the entities important to the case. Natural Language Processing (NLP) is language-based AI that enables you to gain insights into your document collection by analyzing it to identify the entity categories, then visually cluster documents referencing the same entities, allowing you to isolate and retrieve relevant information or filter non-relevant material quickly.
Review Workflow Automation
Review workflows are often complicated, requiring multiple steps, and the review requirements for each case are often unique. This can make managing the review workflow a full-time job to ensure documents are routed to the right review team at the right time, with mistakes being common. AI technology can automate the creation of customized workflows to support the routing and distribution of documents. This streamlines document review to maximize accuracy and defensibility while supporting the unique requirements of each case.
These use cases are practical applications of the AI technology available today. Not based on hype or speculation, these AI technologies have been proven in many projects and cases over the years to streamline eDiscovery, especially eDiscovery review, which has historically been the most expensive phase of the discovery lifecycle. If you are not already taking advantage of these proven AI capabilities, consider adding them to help streamline your workflows.
Visit KLDiscovery’s website to learn more about Nebula AI and its built-in eDiscovery AI that empowers legal teams to classify millions of documents with minimal human intervention, reducing costs and ensuring critical documents do not get missed.