Machine learning is helping lawyers sift through mountains of documents to find evidence.
What’s new: The legal technology company Everlaw launched a clustering feature that automatically organizes up to 25 million documents for lawyers gathering evidence to be used during a trial.
How it works: The new feature analyzes text documents via unsupervised density-based clustering to build a visual map of word clouds.
- The algorithm forms clusters of at least 35 documents by analyzing the text as well as email metadata like author, subject, title, sender, recipient, cc, and bcc fields. Users can create smaller clusters or regroup documents into new clusters manually.
- Users can scroll across word clouds and zoom in and out to browse documents.
- A feature called predictive coding learns to recognize documents relevant to a given case based on user behavior.
- The software also translates documents among 109 languages.
Making headlines: Prosecutors used Everlaw’s software during the high-profile trial of Theranos co-founder Elizabeth Holmes. Among 1 million documents, they found 40 that implicated her criminal intent to defraud investors.
Behind the news: AI increasingly contributes to legal proceedings.
- Lex Machina, a legal analytics platform, forecasts how a given judge will rule on a certain case, estimates trial length, and evaluates the opposing legal team’s record.
- AI assists in intellectual property cases nearly end-to-end: CorsearchNow finds registered properties and SmartShell aids in drafting lawsuits.
- Many U.S. states perform functions such as setting bail and determining sentence lengths based on predictions made by risk-assessment tools that estimate the likelihood that a defendant will re-offend or fail to appear in court. However, these tools have been shown to exhibit bias. For instance, a 2016 investigation into Florida’s recidivism risk system found evidence of racial bias.
Why it matters: Tools that streamline the mundane, high-stakes chore of sifting through documents could help lawyers and their aides  discover evidence they might otherwise overlook. This may be a boon especially for less-privileged plaintiffs and defendants, as some legal scholars have long held that the resource-intensive discovery process favors the wealthy.
We’re thinking: There’s a strong case for NLP in legal practice.