Language and the use of law are predictive of judge gender and seniority

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lluc Font-Pomarol, Angelo Piga, Sergio Nasarre-Aznar, Marta Sales-Pardo, Roger Guimerà
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Abstract

There are examples of how unconscious bias can influence actions of people. In the judiciary, however, despite some examples there is no general theory on whether different demographic attributes such as gender, seniority or ethnicity affect case sentencing. We aim to gain insight into this issue by analyzing over 100k decisions of three different areas of law with the goal of understanding whether judge identity or judge attributes such as gender and seniority can be inferred from decision documents. We find that stylistic features of decisions are predictive of judge identities, their gender and their seniority, a finding that is aligned with results from analysis of written texts outside the judiciary. Surprisingly, we find that features based on legislation cited are also predictive of judge identities and attributes. While own content reuse by judges can explain our ability to predict judge identities, no specific reduced set of features can explain the differences we find in the legislation cited of decisions when we group judges by gender or seniority. Our findings open the door for further research on how these differences translate into how judges apply the law and, ultimately, to promote a more transparent and fair judiciary system.

Abstract Image

语言和法律的使用可预测法官的性别和资历
无意识的偏见会影响人们的行为,这方面的例子不胜枚举。然而,在司法领域,尽管有一些例子,但对于性别、资历或种族等不同的人口属性是否会影响案件判决,却没有普遍的理论。我们分析了三个不同法律领域的 10 多万份判决,旨在了解是否可以从判决文件中推断出法官身份或法官属性(如性别和资历),从而深入了解这一问题。我们发现,判决书的文体特征可以预测法官身份、性别和资历,这一发现与司法机构以外的书面文本分析结果一致。令人惊讶的是,我们发现基于所引用立法的特征也能预测法官的身份和属性。虽然法官重复使用自己的内容可以解释我们预测法官身份的能力,但当我们按性别或资历对法官进行分组时,没有一组特定的缩减特征可以解释我们发现的判决所引用立法的差异。我们的发现为进一步研究这些差异如何转化为法官如何适用法律打开了大门,并最终促进司法系统更加透明和公平。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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