Paper Info
Title
LawyerPAN: A Proficiency Assessment Network for Trial Lawyers
Abstract
ABSTRACTAssessing the proficiency of trial lawyers in different legal fields is of significant importance since a qualified lawyer or lawyer team can strive for his clients' best rights while ensuring the fairness of litigations. However, proficiency assessment for lawyers is very challenging due to many technical and domain challenges, such as the lack of unified evaluation standards, and the complex interactions between lawyers and cases in real legal systems. To this end, we propose a novel proficiency assessment network for trial lawyers (LawyerPAN) to quantify lawyer proficiency through online litigation records. Specifically, we first leverage the theories in psychological measurement for mapping the proficiency of lawyers in each field into a unified real number space. Meanwhile, the characteristics of cases (i.e., case difficulty and discrimination) are well modeled to ensure fairness when assessing lawyers in different cases and fields. Then, we model the interactions between lawyers and cases from two perspectives: the anticipatory perspective aims to measure the personal proficiency of anticipated strategy, and the adversarial perspective seeks to depict the gap of lawyers' proficiency between both sides (i.e., plaintiffs and defendants). Finally, we conduct extensive experiments on real-world data, and the results show the effectiveness and interpretability of our approaches on assessing the proficiency of trial lawyers.
Year
DOI
Venue
2021
10.1145/3447548.3467218
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Lawyer Proficiency Assessment, Legal Intelligence, Neural Network
Conference
0
PageRank 
References 
Authors
0.34
7
9
Name
Order
Citations
PageRank
Yanqing An101.01
Liu Qi21027106.48
Han Wu3253.54
Kai Zhang422.74
Linan Yue501.01
Mingyue Cheng601.69
Hongke Zhao7154.38
Senchao Yuan801.69
Enhong Chen92106165.57