A deep-learning system to alert companies before litigation

“The average cost per lawsuit: at least about $350,000”
October 27, 2016

(credit: Intraspexion, Inc.)

Imagine a world with less litigation.

That’s the promise of a deep-learning system developed by Intraspexion, Inc. that can alert company or government attorneys to forthcoming risks before getting hit with expensive litigation.

“These risks show up in internal communications such as emails,” said CEO Nick Brestoff. “In-house attorneys have been blind to these risks, so they are stuck with managing the lawsuits.”

Example of employment discrimination indicators buried in emails (credit: Intraspexion, Inc.)

Intraspexion’s first deep learning model has been trained to find the risks of employment discrimination. “What we can do with employment discrimination now we can do with other litigation categories, starting with breach of contract and fraud, and then scaling up to dozens more,” he said.

Brestoff claims that deep learning enables a huge paradigm shift for the legal profession. “We’re going straight after the behemoth of litigation. This shift doesn’t make attorneys better able to know the law; it makes them better able to know the facts, and to know them early enough to do something about them.”

And to prevent huge losses. “As I showed in my book, Preventing Litigation: An Early Warning System), using 10 years of cost (aggregated as $1.6 trillion) and caseload data (about 4 million lawsuits — federal and state — for that same time frame), the average cost per case was at least about $350,000,” Brestoff explained to KurzweilAI in an email.

Brestoff, who studied engineering at Cal Tech before attending law school at USC, will present Intraspexion’s deep learning system in a talk at the AI World Conference & Exposition 2016, November 7–9 in San Francisco.