This Essay explores the two holy grails of AI and the law: predicting court decisions and predicting contracts. While there is some overlap between the two, because in order to draft contracts one needs to know the law, both issues can be functionally distinguished. These two areas, and their importance in the context of increasing AI development, are explored more deeply within the context of corporate insolvency law.
Contract Law
Conventional wisdom portrays contracts as static distillations of parties’ shared intent at some discrete point in time. In reality, however, contract terms evolve in response to their environments, including new laws, legal interpretations, and economic shocks. While several legal scholars have offered stylized accounts of this evolutionary process, we still lack a coherent, general theory that broadly captures the dynamics of real-world contracting practice. This paper advances such a theory, in which the evolution of contract terms is a byproduct of several key features, including efficiency concerns, information, and sequential learning by attorneys who negotiate several deals over time.
Many of Richard Posner’s opinions boldly confront great questions. But equally important are those that, in the aggregate, illuminate discrete areas of the law and make them easier to understand.
Consumer contract theory is myopically focused on the unread fine print. Because consumers don’t read their contracts, firms can make “hidden” terms worse without lowering prices.