October 28, 2021, marked the conclusion of a landmark case—the first jury trial addressing insurance coverage responsibilities for COVID-19 business interruption losses in the United States. A federal jury in the Western District of Missouri issued a verdict in favor of the Cincinnati Insurance Company, an action that began when K.C. Hopps, the owner and operator of several bars and restaurants in Kansas City, filed a complaint alleging that its insurer failed to provide coverage under its commercial property policy during the COVID-19 pandemic.
At the time, there was nothing new about this issue. A number of courts across the country had already addressed pandemic-related business interruption claims on summary judgment, asserting that business losses resulting from stay-at-home orders did not involve “physical loss” or “physical damage” to property. But this case was different. The Hon. Stephen R. Bough of the United States District Court held that genuine questions of material fact existed, creating an opening wherein physical contamination from SARS-CoV-2 could constitute physical damage under an insurance policy. According to Bough, a jury should determine whether the virus was present on the premises and whether it actually caused a physical loss or physical damage to it.
The Unpredictable Jury: Fact or Myth?
How could anyone know what that jury in Missouri would decide? Most of the civil cases filed in the United States never reach a jury. This is partly because the jury is considered to be the least predictable of the decision-makers in the legal system. This uncertainty (or, the belief in this uncertainty) is often used strategically. Attorneys deploy the threat of a jury trial to pressure
their opposing counsel to drop or settle a case, often presenting high-low agreements as a tempting alternative to an unknown jury pool. Court mediators and trial judges are no exception. Both routinely caution parties against the unpredictability of juries. But is this really true? Are jurors more unpredictable than judges? Maybe. Over the past ten years, artificial intelligence and machine learning technologies have combed through millions of state trial court records, analyzing judicial rulings in ways that have rendered judicial decisions more transparent and more predictable. Litigators are now equipped with massive amounts of quantitative and qualitative information about individual judges, everything from their docket assignments to the prior experience to their ruling histories. Perhaps, then, judges are more predictable because judicial decision-making has been made more predictable. Can the same happen for juries? After all, recent research on legal decision-making suggests that group decision-making is more stable and more accurate than individual decision-making.
The Entry of Verdict Analytics
Outcome prediction is a key part of practicing law. Clients expect their attorneys to provide them with accurate assessments of the potential consequences of any major legal decision. These assessments, which typically take place at the beginning of the litigation process, allow clients to assess the viability of a pending legal action. By looking through similar cases from the past, clients and their attorneys can strategize how they would like to navigate through a specific legal matter, as the details of other cases can provide invaluable insight into how their case is likely to unfold.
The problem is that this information has been impossible to access, especially for attorneys at the state trial court level. Thankfully, a market of legal analytics platforms have emerged to solve this problem, harnessing the rudimentary tools of litigators and expanding their capabilities with artificial intelligence and machine learning. Trellis, for example, has completely remapped the ways in which attorneys conduct legal research.
Trellis began by following the logics of conventional research. It provided its users with the tools needed to conduct element-focused analyses. That is, users were positioned to better understand how likely their action would survive a motion to dismiss or a motion for summary judgment. Users were also presented with the tools necessary to ensure that their filings contained whatever might be required to survive these types of dispositive motions.
While useful, this kind of information tells us little about how a jury would respond to a specific type of action. Recognizing this limitation, Trellis has started integrating verdict data into its systems, mending its archives of case law, legal petitions, and judicial rulings to also include information related to case outcomes and settlement awards—particularly for cases where judicial officers never issued formal opinions. But what, exactly, can we learn by tracking case outcomes?
A Case in Point
As one of the top causes of unintentional injuries, slips and falls can result in astronomical expenses, ranging from medical bills to lost wages. Every slip and fall case is unique. Some will settle at the onset of litigation. Some will make it all the way to trial. And others will settle days—maybe even hours—before trial begins. With verdict data, attorneys can begin to map the trajectories of different settlement strategies, identifying the range of possible outcomes for each decision in the litigation process. All of this information is readily available through a simple verdict search with legal analytics.
To start, we can identify the monetary amounts at stake in the settlement process. By browsing through a random selection of ten slip and fall cases filed against the City of Los Angeles, we can quickly get a feel for the settlement amounts the municipality has been willing to offer in order to swiftly resolve these types of cases. These offers, which range from $2,500 to $100,000, represent the majority of case outcomes in our sample (7 out of 10). This information suggests some hesitancy on the part of the City of Los Angeles to bring matters to trial. It appears that the City of Los Angeles prefers to
settle legal actions out-of-court than to spend whatever it might take to defend itself through a trial.
These settlement figures can then be compared with the amounts actually awarded by local juries in slip and fall cases. This comparison allows plaintiffs to back any possible counter-offers with hard data about a case’s potential value. In our sample, we can see that one jury awarded an individual $3,094,972 for an ankle fracture caused by the city’s failure to repair a dangerous hole. However, we can also see how this information might push the City of Los Angeles to call a plaintiff’s bluff, rolling their dice on a jury that has, on more than one occasion, readily decided in its favor. In our sample,
million dollar jury verdicts against the City of Los Angeles in slip and fall cases are exceedingly rare, with two out of three cases concluding with a defense verdict on liability.
A Prism into the Future
While jury verdicts are few and far between, they are also incredibly important. Each year, juries at the state and federal level decide the outcomes of billions of dollars. They also set the standards that influence future legal behavior, as jury verdicts determine the value of legal disputes in ways that can affect the choices of plaintiffs, defendants, and their attorneys. Legal analytics backed by artificial intelligence is bringing new levels of transparency to the jury trial process. Attorneys and their clients are now in a position to identify the tendencies of their judges, their opposing
counsel, and their juries, sorting through massive quantities of information at the most granular levels in a matter of minutes.
We may never know with any degree of absolute certainty how a case will unfold in front of a jury box. Still, the seamless integration of verdict data with the archives of state trial court records is already uncovering the trends that hide behind the unknown. Whether it’s a routine slip and fall case in Los Angeles County or a COVID-19 business interruption lawsuit in Missouri, a case will never look the same after it has been viewed through the prism of verdict analytics.