Timothy D. Lytton, associate dean for research and faculty development, Distinguished University Professor, and professor of law: The U.S. Food and Drug Administration and the U.S. Department of Agriculture conduct annual surveys regarding U.S. consumers’ consumption of foods, awareness of food safety risks, and safe handling practices.
A 2011 analysis of these surveys found that consumers’ level of risk awareness and care increased in two periods of heightened media attention to food safety: 1993-1998 and 2006-2010. As it happens, 1993 and 2006 are years in which major foodborne illness outbreaks occurred that gave rise to extended litigation.
My research in a variety of areas (e.g. gun litigation, lawsuits against the Catholic Church for clergy sexual abuse, and food safety outbreaks) suggests that litigation and media coverage are symbiotic and that, in particular, litigation increases the amount and duration of media coverage of an issue.
In collaboration with colleagues in the Legal Analytics Lab, I hope to determine whether tort litigation increased consumers’ awareness of food safety risk and increased consumers’ level of precaution by generating increased media coverage. We analyze media coverage in large new databases to determine whether litigation increased coverage during the two key periods.
Such a study would have larger implications for understanding the policy impact of tort litigation.
Anne Tucker, associate professor of law: We are incorporating machine learning into an empirical research project on impact investment contract terms. Integrating machine learning methodologies has helped us identify and test important relationships among the contract terms, particularly as we probe the balance between profit and purpose. I hope to also use text mining of mutual fund risk disclosures to determine their predictive relationship/value with litigation and SEC enforcement.
Ramsi Woodcock, assistant professor at the J. Mack Robinson College of Business, who has a secondary appointment at the College of Law: Legal documents, particularly court opinions, have long been considered the gold standard in predicting how judges will rule in individual cases. Lawyers, other judges, and law professors scrutinize important court opinions, for example, for evidence of how a particular court will rule on a particular issue. Indeed, some would say that the “rule of law” is no more than this practice of using legal documents as a basis for predicting legal outcomes, and altering behavior accordingly.
But do these documents really provide reliable guidance on how judges will rule in particular cases? Or do they, as some have argued, actually have the opposite effect, obscuring the true motivations behind legal outcomes?
Legal analytics is bringing us a step closer to answering that question, by making it possible to quantify the predictive power of the language used in legal documents. When combined with work in psychology that has identified words and phrases typically associated with obfuscation, analytics also promises to help us determine whether judges and lawyers are honest about their motivations in making certain legal arguments.
My work in legal analytics will focus on this problem of the predictive power of legal language, with a view to bringing more certainty to the interactions of businesses with the legal system, by improving our ability to predict the responses of judges to certain business practices.
Doug Yarn, professor of law and executive director of the Consortium on Negotiation and Conflict Resolution: I believe the primary function of law is to regulate behavior. I consider these emerging analytical tools a fairly exciting way to gain insight into human behavior. There are myriad applications in using this technology to analyze data. My particular interest is in the predictive uses in alternative dispute resolution, determining what data can tell us about what people do, and what they are likely to do, in a given legal context.
In the context of negotiation, I’m interested in how attorneys can use data to assess the risk of trial. People typically negotiate within the shadow of a possible trial, and much is affected by their assumptions about trial outcomes. It is easier to negotiate settlement if the parties can agree on the use of data about outcomes in similar past cases to reach a common prediction and risk assessment of going to trial.
In the context of arbitration, I hope to mine data to get a better sense of what goes on in the arbitration process. It’s difficult to access data in this area because arbitration is a private, voluntary process. Perhaps we can find a way to mine the data while still protecting the privacy of the arbitral process. Uncovering and analyzing data about arbitration proceedings will help people better understand its risks and allow them to make more informed decisions about whether to go to arbitration.
My current research is focused on how mediators use legal analytics to help parties resolve disputes. I’m trying to understand how experienced mediators go about helping the parties make risk assessments.
Law students need to learn how to use big data to represent their clients—how to gather data and make an analysis of risks based on that data. Most lawyers will never try a case and will never need big data for litigation, but they will need it for settling cases, which is what happens with most disputes. The new lab will be really advantageous in teaching students in that regard.
However, I think we also need to be really sensitive as to the wisdom of using these technologies. That will require experimenting to understand these tools better and taking a hard look to figure out what is useful and what isn’t. We need to be aware that big data isn’t going to cure the common cold. It could potentially be like laptops in the classroom—initially that was thought to be a great idea and it would contribute to learning, but now we are learning otherwise, that it’s horrible for notetaking, comprehension, and memory. Legal analytics is here and will be an integral part of the profession’s future, but we should be careful it not undermine the essential human touch.
Charlotte Alexander, director of the Legal Analytics Lab and associate professor of legal studies at the Robinson College of Business: Read more about her project for the U.S. Department of Labor to study federal district court misclassification, using data analytics to understand how the courts distinguish between employees and independent contractors, and the factors influencing their decisions.