Do You See a Time When You Can Predict Outcomes?

In this episode of ‘Law, disrupted,’ John is joined by Evyatar Ben Artzi, co-founder & CEO of Darrow – an Israel-based company that uses artificial intelligence to scan the internet and identify potential claims. Darrow does this by identifying instances where companies have broken the law or failed to comply with their own rules or policies.

The conversation begins with Evyatar discussing his background prior to starting Darrow, including his years as a combat officer in the Israeli Defense Forces, his legal education including his study of Law and Cognitive Science and his tenure as a Supreme Court clerk. Evyatar explains that while at the Supreme Court, he witnessed some of the challenges plaintiffs’ firms faced, inspiring him to start working on a data-driven way to find winning cases and bring them to law firms.

John and Evyatar then dive into how Darrow operates. Evyatar explains how Darrow’s “justice intelligence” helps lawyers find better cases, reducing due-diligence costs of finding those cases, and bringing them to court effectively. The process starts by studying successful cases from the past and developing a formula or algorithm to identify similar patterns from current real-world data. Darrow then searches public information, including news feeds, social media, administrative filings, environmental monitoring, HTML source codes, and legal data, including court dockets and legislation, to find other instances of the same patterns that have previously led to successful cases. The system then determines how many people were harmed and forecasts what the likely legal outcome would be to help set the value of the case. Instead of a lawyer looking to find a single “smoking gun,” the artificial intelligence system combines many data points across the internet to put together a complete claim that resembles a previous successful case. Then, Darrow finds a lawyer to examine whether the potential claim is actionable.

John and Evyatar discuss how this system is applied to various types of claims. They discuss how it works in data privacy cases where data from the source code of a company's website or app can be compared to the company’s legal documents, terms of service or privacy policies. They then explore recent cases where tech companies have faced trouble for sharing data with third parties, contrary to representations they have made to users. In particular, John and Evyatar discuss a recent case in which a Special Master was appointed to interview Facebook engineers to determine exactly what data Facebook has from its users. It has become commonplace that companies simply do not know what data they collect from users, where it is stored, and what uses are made of it. They then turn to the incentives companies have to comply with data privacy requirements including California’s statute imposing set penalties per user and recent high dollar class settlements.

The conversation then moves to how Darrow’s artificial intelligence can help identify environmental claims. John and Evyatar examine how complaints on social media can be matched with hard data from environmental sensors, as well as public information about factories in the area, including previous environmental claims, to develop a viable mass tort case.

They also discuss how online pricing data can be evaluated together with public data about product launches and sales to identify patterns that suggest cartel activity or other antitrust claims. They emphasize the need to carefully evaluate such situations to determine if there is evidence of a conspiracy or other illegal activity associated with the patterns in the data.

John

Podcast Link: Law-disrupted.fm
Host: John B. Quinn
Producer: Alexis Hyde
Music and Editing by: Alexander Rossi