论文标题
关于法律AI中“假”数据的公平性
On the Fairness of 'Fake' Data in Legal AI
论文作者
论文摘要
较小的预算和较大案件的经济学需要在法律程序中使用AI。我们研究了不同影响的概念以及训练数据中的偏见如何导致寻找更公平的AI。本文旨在开始关于在法律背景下对预处理方法的批评，就这种实施的实际情况。我们概述了如何使用预处理来纠正偏见的数据，然后研究有效改变案件的法律含义，以实现更公平的结果，包括黑匣子问题和缓慢侵犯法律先例。最后，我们提出了有关如何使用修改分类器或在最后一步中纠正输出的方法避免预处理数据的陷阱的建议。
The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.
