法律类案检索结果中性别偏置对用户认知的影响

doi:10.3778/j.issn.1002-8331.2311-0036

摘要/Abstract

摘要： 近年来，法类案检索已经成为了法律从业者工作的重要的组成部分。法律类案检索的过程中，搜索引擎从海量的数据中筛选出若干个相关案例，提供给用户。然而，这些案例是否具有性别偏置，该种偏置是否会对用户认知产生影响，却是一个未知数。设计了一个全新的用户实验框架，模拟法官进行类案检索时阅读相关案例的过程。邀请了72位具有法律背景的志愿者进行实验。志愿者被要求模拟法官的身份就指定的3个案例进行类案检索，并对案例中的被告人量刑。对任务和类案检索结果中被告人的性别均进行了编辑，以统计类案检索结果中的性别偏置对被试认知产生的影响。实验结果显示，类案检索结果中的性别偏置对法官的认知不产生显著的影响。

关键词: 法律类案检索, 用户实验, 公平性

Abstract: In recent years, legal case search has become an important part of a legal practitioner’s work. During legal case search, search engines retrieval a number of relevant cases from huge amounts of data and serve them to users. However, it is uncertain whether these cases are gender-biased and whether such bias has impact on user perceptions. this paper designs a new user experiment framework to simulate the judges’ reading of relevant cases. 72 participants with backgrounds in legal affairs are invited to conduct the experiment. Participants are asked to simulate the role of the judge in conducting a legal case search on 3 assigned cases and determine the term of the defendants in these cases. Gender of the defendants in both the task and relevant cases is edited to statistically measure the effect of gender bias in the legal case search results on participants’ perceptions. The results show that gender bias in the legal case search results has no significant effect on judges’ perceptions.

Key words: legal case search, user study, fairness

张瑞喆, 艾清遥, 刘奕群, 吴玥悦, 王贝宁. 法律类案检索结果中性别偏置对用户认知的影响[J]. 计算机工程与应用, 2025, 61(6): 341-348.

ZHANG Ruizhe, AI Qingyao, LIU Yiqun, WU Yueyue, WANG Beining. Gender Biased Legal Case Retrieval System on Users’ Decision Process[J]. Computer Engineering and Applications, 2025, 61(6): 341-348.

参考文献

[1] ROBERTSON S E, WALKER S, JONES S, et al. Okapi at TREC-3[C]//Proceedings of the Text Retrieval Conference, 1995: 109.
[2] ROBERTSON S, ZARAGOZA H. The probabilistic relevance framework: BM25 and beyond[J]. Foundations and Trends? in Information Retrieval, 2009, 3(4): 333-389.
[3] JONES S K. A statistical interpretation of term specificity and its application in retrieval[J]. Journal of Documentation, 1972, 28(1): 11-21.
[4] SALTON G, BUCKLEY C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5): 513-523.
[5] QIN T, LIU T Y, LI H. A general approximation framework for direct optimization of information retrieval measures[J]. Information Retrieval, 2010, 13: 375-397.
[6] PAHIKKALA T, TSIVTSIVADZE E, AIROLA A, et al. Learning to rank with pairwise regularized least-squares[C]// Proceedings of the SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, 2007: 27-33.
[7] LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time series[G]. The Handbook of Brain Theory and Neural Networks, 1995: 255-258.
[8] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
[9] ELMAN J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211.
[10] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv:1810.04805, 2018.
[11] LIU Y, OTT M, GOYAL N, et al. Roberta: a robustly optimized bert pretraining approach[J]. arXiv:1907.11692, 2019.
[12] BENCH-CAPON T, ARASZKIEWICZ M, ASHLEY K, et al. A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law[J]. Artificial Intelligence and Law, 2012, 20: 215-319.
[13] TURTLE H. Text retrieval in the legal world[J]. Artificial Intelligence and Law, 1995, 3: 5-54.
[14] VAN OPIJNEN M, SANTOS C. On the concept of relevance in legal information retrieval[J]. Artificial Intelligence and Law, 2017, 25: 65-87.
[15] LóPEZ MONROY A, CALVO H, GELBUKH A, et al. Link analysis for representing and retrieving legal information[C]//Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics. Berlin, Heidelberg: Springer, 2013.
[16] YU W, SUN Z, XU J, et al. Explainable legal case matching via inverse optimal transport-based rationale extraction[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 657-668.
[17] SHAO Y, MAO J, LIU Y, et al. BERT-PLI: modeling paragraph-level interactions for legal case retrieval[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), 2020: 3501-3507.
[18] ZHANG R, AI Q, WU Y, et al. Diverse legal case search[J]. arXiv:2301.12504, 2023 .
[19] KAMISHIMA T, AKAHO S, ASOH H, et al. Fairness-aware classifier with prejudice remover regularizer[C]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2012), 2012: 35-50.
[20] GEYIK S C, AMBLER S, KENTHAPADI K. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019: 2221-2231.
[21] FU Z, XIAN Y, GAO R, et al. Fairness-aware explainable recommendation over knowledge graphs[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: 69-78.
[22] SPOHN C, BEICHNER D. Is preferential treatment of female offenders a thing of the past? a multisite study of gender, race, and imprisonment[J]. Criminal Justice Policy Review, 2000, 11(2): 149-184.
[23] JOHNSON L, HARNER M M, CANTONE J A. Gender and securities law in the supreme court[R]. Women’s Rights Law Reporter, 2023.
[24] SONGER D R. Does judge gender matter? Decision making in state supreme courts[J]. Social Science Quarterly, 2000.
[25] JOHNSON S W, SONGER D R, JILANI N A. Judge gender, critical mass, and decision making in the appellate courts of canada[J]. Journal of Women Politics & Policy, 2011(3): 237-260.
[26] LOEBER R, FARRINGTON D P. Age-crime curve[M]//Encyclopedia of criminology and criminal justice. New York: Springer, 2014: 12-18.
[27] DEMUTH S, JOHNSON J K. The independent and joint effects of race/ethnicity, gender, and age on sentencing outcomes in U.S. Federal Courts[J]. Justice Quarterly, 2010, 27(1): 1-27.
[28] MA Y, SHAO Y, WU Y, et al. LeCaRD: a legal case retrieval dataset for Chinese law system[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 2342-2348.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	31

	来源	本网站

	次数	31
	比例	100%

摘要

最新录用	在线预览	正式出版

0	0	29

	来源	本网站

	次数	29
	比例	100%