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2019 Colum. Bus. L. Rev. 621 (2019)
Predictive Contracting

handle is hein.journals/colb2019 and id is 633 raw text is: PREDICTIVE CONTRACTING Spencer Williams* This Article examines how contract drafters can use data on contract outcomes to inform contract design. Building on recent developments in contract data collection and analysis, the Article proposes predictive contracting, a new method of contracting in which contract drafters can design contracts using a technology system that helps predict the connections between contract terms and outcomes. Predictive contracting will be powered by machine learning and draw on contract data obtained from integrated contract management systems, natural language processing, and computable contracts. The Article makes both theoretical and practical contributions to the contracts literature. On a theoretical level, predictive contracting can lead to greater customization, increased innovation, more complete contract design, more effective balancing of front-end and back-end costs, better risk assessment and allocation, and more accurate term pricing for negotiation. On a practical level, predictive contracting has the potential to significantly alter the role of transactional lawyers by providing them with access to previously unavailable information on the statistical connections between contract terms and outcomes. In addition to these theoretical and practical contributions, the Article also anticipates and addresses limitations and risks of predictive contracting, including technical constraints, concerns regarding data privacy and confidentiality, the regulation of the unauthorized practice of law and the potential for exacerbating information inequality. * Fellow and Lecturer in Law, Stanford Law School Program on Corporate Governance and Practice. The author would like to thank Afra Afsharipour, Hilary Allen, Jordan Barry, Abraham Cable, Eric Chaffee, John Crawford, Jared Ellias, Jill Fisch, George Georgiev, Colleen Honigsberg, Cathy Hwang, Michael Klausner, Elizabeth Pollman, Brian Quinn, Harry Surden, George Triantis, Samuel Weinstein and Andrew Winden.

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