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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: 


                    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

   * 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

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