当前位置: X-MOL 学术The University of Chicago Law Review › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Competing Algorithms for Law: Sentencing, Admissions, and Employment
The University of Chicago Law Review ( IF 1.9 ) Pub Date : 2021-06-01
Saul Levmore

Algorithms 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.



中文翻译:

法律竞争算法:量刑、录取和就业

算法已经进入法庭、大学招生委员会和人力资源部门。虽然被告和其他失望的当事人基于正当程序或类似反对意见对算法的使用提出质疑,但应该预料到,他们也会质疑算法的准确性,并试图提出自己的算法,以对法官的决定和其他当局。这种方法的问题在于,那些可以透明地看到为什么他们被算法拒绝权利或资源的人可以通过改造数据来操纵算法。因此,政策制定者和法律学者对算法完全透明的要求是错误的。为了克服算法操纵,我们提出了算法竞争的新解决方案。这种方法,已部署在金融领域的版本在法律上会很好用。我们展示了州、大学或雇主应该如何将未经测试的数据放在密码箱中。决策各方随后开发各自的算法并进行竞争。对密码箱数据表现最好的算法获胜。虽然这种方法提出了本文详细讨论的几个复杂问题,但它优于完全公开数据和算法透明度。

更新日期:2021-06-01
down
wechat
bug