A pathbreaking article co-authored by David Hoffman, the William A. Schnader Professor of Law at the University of Pennsylvania Carey Law School, introduces a novel approach to estimating contractual meaning through the use of large language models (LLMs). “Generative Interpretation,” forthcoming in the New York University Law Review, positions artificial intelligence (AI) models as the future “workhorse of contractual interpretation” and shows that using them to interpret legal text “can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties’ agreements.”
Using grounded case studies of contracts appearing in well-known opinions, Hoffman and co-author Yonathan Arbel, associate professor at the University of Alabama, build the case that LLMs can effectively balance interests of cost and certainty with accuracy and fairness; their analysis shows that applying the technique often resulted in “the same answers at lower cost and with greater certainty” as that obtained by jurists using more traditional methods.
The authors begin with the examination of a controversial Fifth Circuit decision that pitted policyholders against insurance companies over the meaning of “flood” in the context of Hurricane Katrina damage; floods were excluded from many insurance policies. Plaintiffs maintained that “flood” didn’t include water damage caused by humans, which, if accepted as a proposition, would allow them to argue that their property damage—allegedly resulting from negligence by the Army’s Corps of Engineers in maintaining levees—was not contemplated within the contract’s exclusions. Defendants argued that “flood” was unambiguous and referred to any inundation of water, regardless of cause.
After expending “expensive and extensive efforts” to arrive at its decision, the court sided with the insurance companies. Hoffman and Arbel noted that the court consulted dictionaries, treatises, linguistic canons, out-of-jurisdiction caselaw, and an encyclopedia, among other resources. Still, the decision received criticism, the authors note, because, according to detractors, it “merely affirmed its pro-business priors.”
The authors present several additional in-depth case analyses to showcase how generative interpretation could be deployed to various ends, beginning with how LLM models can work alongside the doctrine of reasonable expectations. Hoffman and Arbel explain that judges’ and laypersons’ “reasonable expectations” can diverge dramatically, with each group strongly believing their interpretations are common. Ultimately, this “introspective interpretation” leads to uncertainty in results, they write.
Read more at Penn Carey Law.
