Part one of this blog post discussed how AI is transforming eDiscovery document review by improving efficiency and accuracy. We looked at how AI is used to review short message and image data, reducing review time and related costs. Part two will examine how AI can be applied to privilege identification.
Why Privilege Identification Matters
Privilege identification is a critical aspect of eDiscovery. However, identifying privileged actors can be daunting, especially when dealing with large data sets. In these matters, proportionality is a key consideration. Attorneys must weigh the risk and exposure of the case against the cost of performing an exhaustive privilege review. While clawback provisions provide a safeguard when privileged information is unintentionally disclosed, the opposing party’s attorneys cannot unsee what they have seen. Therefore, it is essential to accurately identify privileged documents as thoroughly as practical before production to avoid inadvertent disclosures.
Using AI for privilege identification not only improves efficiency but increases accuracy. When AI algorithms quickly identify potentially privileged documents, legal teams can focus on the most relevant producible data.
How AI Works for Privilege Identification
Lineal uses two types of machine learning to expedite privilege review. First, Lineal resolves the identity of all legal actors across the entire data set. Lineal’s system recognizes lawyers’ official firm-based or corporate email addresses and correlates those individuals and their corporate IDs to their other, often personal, email addresses, usernames and aliases. By doing so, Lineal’s system can accurately locate the legal actors within the data set and flag potentially privileged documents for further review. Second, Lineal recognizes privileged content within those flagged records. It then displays snippets of the relevant content of each flagged document, thereby allowing reviewers to quickly confirm privilege without hunting for the protected content within each record.
In issue review, the recall goal to meet the ”reasonable and proportional” requirement is less than 100 percent. However, legal teams strive to identify all privileged documents to protect sensitive information. And in addition to initial privilege identification, AI can help with final quality control (QC) post-review. AI algorithms compare all lawyer-identified privileged documents with the rest of the discovery universe, flagging potentially misclassified documents to ensure priv review accuracy better.
AI is the Present and the Future
AI has revolutionized eDiscovery document review by improving efficiency and accuracy over the past decade. Legal teams are now applying AI-driven workflows to expedite the review of images, short message data, and potentially privileged data. To effectively represent their clients, eDiscovery professionals must understand these use cases and apply them to future matters.
If you are interested in seeing these valuable AI tools in action, click here.