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Phil C. Neal
No full textPhotograph courtesy of the University of Chicago Law School. For rights and permissions information for this photo, please contact [email protected]://chicagounbound.uchicago.edu/phil_neal_images/1004/thumbnail.jp
Phil C. Neal and Linda Thoren Neal
No full textPhotograph courtesy of the University of Chicago Law School. For rights and permissions information for this photo, please contact [email protected]://chicagounbound.uchicago.edu/phil_neal_images/1006/thumbnail.jp
Norval Morris
No full textPhotograph courtesy of the University of Chicago Law School. For rights and permissions information for this photo, please contact [email protected]://chicagounbound.uchicago.edu/norval_morris_images/1006/thumbnail.jp
Douglas G. Baird, Group 2
No full textDouglas G. Baird (right), the Harry A. Bigelow Professor of Law and dean of the Law School at the University of Chicago. He is pictured with Scott Turow, lawyer and author.
University of Chicago Photographic Archive, [apf1-11871], Hanna Holborn Gray Special Collections Research Center, University of Chicago Library.
View information about rights and permissions.https://chicagounbound.uchicago.edu/douglas_baird_images/1002/thumbnail.jp
Representative estimates of COVID-19 infection fatality rates from four locations in India: cross-sectional study,
No full textCompeting Algorithms for Law: Sentencing, Admissions, and Employment
Get PDFAlgorithms have found their way into courtrooms, college admission committees, and human resource departments. While defendants and other disappointed parties have challenged the use of algorithms on the basis of due process or similar objections, it should be expected that they will also challenge their accuracy and attempt to present algorithms of their own in order to contest the decisions of judges and other authorities. The problem with this approach is that people who can transparently see why they have been algorithmically denied rights or resources can manipulate an algorithm by retrofitting data. Demands for full algorithmic transparency by policy makers and legal scholars are therefore misguided. To overcome algorithmic manipulation, we present the novel solution of algorithmic competition. This approach, versions of which have been deployed in finance, would work well in law. We show how the state, a university, or an employer should set aside untested data in a lockbox. Parties to a decision then develop their respective algorithms and compete. The algorithm that performs best with the lockbox data wins. While this approach presents several complications that this Article discusses in detail, it is superior to full disclosure of data and algorithmic transparency