Originally published in Santa Barbara Lawyer. In this article, Nicole Clark explains how attorneys use verdict data and legal analytics to evaluate slip-and-fall cases, predict jury outcomes, and guide settlement decisions.

“Who hurt you?” shouts a billboard for the Razavi Law Group in Los Angeles. The city is awash with gigantic advertisements in this style, each hawking the services of a local personal injury attorney. Our skyline, visually invaded by a collection of stern-looking men in dark suits, highlights the saturated nature of personal injury law. “[A]t the end of the day,” explains Ardy Pirnia of the Pirnia Law Group, “if you want to generate leads, the only way to do it is to be in everyone’s face.”

There is, however, a curious whiplash that occurs whenever cases move from highway billboards to downtown courtrooms. As the business of personal injury law gives way to its practice, one preference is to keep the legal matter private. That is, to prevent the case from ever having to face a jury. This is particularly true for slips and falls, where blame-the-plaintiff biases can easily run rampant. “Of all the cases we handle as plaintiffs’ personal-injury lawyers,” begins Teresa Johnson, an attorney at Kramer Holcomb Sheik, “none are met with more eye rolls and a ‘give me a break’ than when the judge tells the potential panel ‘this is a slip and fall matter.’” 

And yet, something is missing between these two observations about showing face and hiding it. We are left wondering about what happens in the interim of a lawsuit. What is there to know about the events that unfold between the acquisition of a case and its settlement? And, more importantly, how are attorneys leveraging verdict analytics every step of the way to make these litigation decisions?

To read the entire article, click here to check it out on pages 11-12; 32.

The following piece was published by the National Employment Lawyers Association New York Chapter to their site on June 6, 2024.

I am Nicole Clark, CEO and co-founder of Trellis, and want to give you an example of how artificial intelligence is being implemented in the legal profession. On May 16, 2014, a technician contracted by EST Downtown received a work order from his supervisor requesting that he remove a bird’s nest from a gutter. When he arrived at the building, he climbed an 8-foot ladder, which shifted after a bird flew out from the nest. The technician fell to the ground, sustained serious injuries, and filed a lawsuit against EST Downtown, alleging violations of New York State Labor Law. A bird, and its nest, flung two parties into a courtroom, commencing one of the thousands of quotidian cases that fill the litigation landscape every single day. 
 
On its own, this case is unremarkable. EST Downtown, in its defense, insisted that the cause of action should be dismissed, claiming that the removal of a bird’s nest is not the kind of cleaning protected under New York State Labor Law. The court agreed. The matter concluded by reaffirming a decade-old rule concerning the definition of cleaning (Soto v. J. Crew, Inc. [2013]). 
 
But what happens if we place this case in its context? What if we feed a computer thousands of cases just like it? 
 
The simple act of uncovering this case required multiple levels of artificial intelligence (AI). I found it, along with its docket and associated documents, on Trellis. This legal analytics platform compiles state trial court records, building systems that can synthesize the raw data found in courthouses across the state, restructuring the information with machine learning technologies to create usable datasets. 
 
Most users treat the platform as a brief bank, a searchable repository of legal documents that can be used as models. Reading one memorandum after another, an attorney can tour the litigation landscape in a single glance, quickly forecasting how an opposing counsel might position a particular issue. The platform is filled with mirror images of our case study. It takes me minutes to learn that not all forms of cleaning are protected by New York State Labor Law, an insight that could shift the types of deposition testimony and discovery measures I might want to put into place. 
 
This brief bank is the building block through which the platform generates its legal analytics. On a single docket, anything is indexable. AI can correlate information across thousands of datasets, mapping out the litigation histories of parties, attorneys, and judicial officers. By clicking on the name of any individual, we are linked to an archive of cases touched by that person. We can watch as they move from one matter type to another, gaining quantitative and qualitative insights about businesses and their pending cases, attorneys and their place in the market, judges and their interpretation of case law. 
 
Attorneys can now grapple with millions of cases simultaneously. The magic of big numbers means we can finally see the litigation landscape in its entirety, shifting our focus away from singular cases in order to surface the underlying patterns that affect us all. 

To learn more about how Trellis helps give labor and employment state court litigators a competitive advantage, visit http://www.trellis.law today.