Predictive capabilities created by big data and artificial intelligence increasingly allow parties to draft contracts that fill their own gaps and interpret their own standards without adjudication. With these contracts, parties can agree to broad objectives and let automated analytics fill in the specifics based on real-time contingencies. In a recent article, Self-Driving Contracts, we discuss how these contracts will develop and explore their likely impact on the law. 

A self-driving contract is one that writes its own terms or fills its own gaps. To be more precise, a self-driving contract has three key features. It is an agreement where 1) the parties set only broad ex ante objectives; but 2) the contract uses machine-driven analytics and artificial intelligence to translate the general ex ante objectives into a specific term or directive at the time of performance; where 3) those terms are based on information gathered after the parties execute the initial agreement. Just as a passenger in a self-driving car relies on the car to determine the optimal means (direction, speed, lane choice) to travel between two locations and to update its determination to account for real-time contingencies (traffic, weather, construction), the parties to a self-driving contract agree to a shared goal and trust in the contract to direct them on precisely how to achieve that goal in light of real-time contingencies. 

Stated in the abstract, the idea of a self-driving contract may sound like radical science fiction. But the first generation of these contracts already exists, primarily in the form of self-pricing contracts. The most familiar example can be found in the auto-insurance industry, where parties agree to price terms that adjust automatically based on computer-driven analytics. Similar pricing terms can be found in dental insurance, in short-term rental agreements, and in transportation services.

As technologies that allow for predictive accuracy and ubiquitous monitoring and communication advance, self-driving contracts will proliferate. Predictive technologies will provide increased information that allows parties to more precisely choose the actions that will benefit their mutual interests. Parties will have the ability to predict with high confidence that given scenario X action Y is the optimal course of action to achieve their agreed upon goal. Monitoring technologies will provide access to the information necessary to determine whether scenario X has in fact occurred. And communication technologies will transmit the directives resulting from the analysis to the relevant actors. These technologies pave the way for self-driving contracts. 

The emergence of this form of contracting will have a significant impact on the way we think and talk about contracts. First, it will reveal that the existing language of contract theory is deficient. Self-driving contracts blur the distinctions between rules and standards, between ex ante agreements and ex post dispute resolution, and even between complete and incomplete contracts. Is the algorithm stating a rule written in the contract or is it interpreting and arbitrating a vague standard left for future resolution? Are these contracts hopelessly incomplete or  perfectly complete? Each view is consistent with existing literature. This indeterminacy in the language of contract theory and law has led scholars (and will lead courts) to struggle in interpreting and enforcing these contracts. Notions of assent, definiteness, agreements to agree, unconscionability, mutual mistake, renegotiation, and even efficient breach cannot be cleanly transported to the world of self-driving contracts. Analyzing these questions through the language of self-driving contracts may provide important clarity that is currently missing.

Perhaps most importantly, with contracts being interpreted by their own internal software, contract law will have to focus on where that software comes from and how it operates. Markets will arise for third-party vendors who either certify or provide independent contract programming. In some cases, these will be new markets; in others, they will evolve from existing markets such as the market for contract arbitrators. Law will play a role in supporting and overseeing these markets. In our article, we explore that role and how it will differ in markets for contracts between sophisticated parties, on the one hand, and consumer contracts, on the other.

The big data revolution will drastically impact the way private parties order their affairs. Lawyers, lawmakers, and scholars will need to understand how contracting parties will facilitate trade using predictive analytics. The costs of transacting will fall as contracting parties take advantage of the availability of data. Indeed, the lawyers of the future may look back and marvel at how much time and energy was wasted negotiating, writing, and litigating contracts in the twentieth century. 

Anthony J. Casey is Assistant Professor of Law at the University of Chicago Law School and Anthony Niblett is Associate Professor and University of Toronto - Faculty of Law.