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Interdisciplinary Research on Predictive Justice
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Interdisciplinary Research on Predictive Justice

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 19302

Special Issue Editor

Prof. Dr. Stéphane Mussard

E-Mail Website
Guest Editor
CHROME, University of Nîmes, Avenue du Dr. Georges Salan, 30000 Nimes, France
Interests: mathematical statistics; econometrics (Gini regressions); data analysis (on l1 norm and Gini metrics); machine learning; neural networks; stochastic dominance; inequality measurement; social choice; game theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of big data and particularly the access of court decisions creates new issues in jurimetrics. Although traditional tools employed in statistics and computer science may be used to predict judge decisions, the legal domain remains challenging for many reasons: A lot of information inherent to court decisions must be synthetized and classified, court decisions embrace many legal norms related to specific legal domains, and the language used by lawyers and judge is very specific and demands particular natural language processing. For those reasons, it is difficult to employ standard machine learning algorithms and neural networks without sharing knowledge with lawyers. The mixture of statistics, legal sciences, and computer sciences makes it possible to produce new techniques aimed at predicting the decisions of judges and at providing citizens with accessible information on their rights.

Prof. Stéphane Mussard
Guest Editor

Manuscript Submission Information

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Published Papers (4 papers)

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Research

17 pages, 454 KiB  
Article
Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach
by Gildas Tagny-Ngompé, Stéphane Mussard, Guillaume Zambrano, Sébastien Harispe and Jacky Montmain
Stats 2020, 3(4), 427-443; https://doi.org/10.3390/stats3040027 - 27 Sep 2020
Cited by 3 | Viewed by 3136
Abstract
This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics [...] Read more.
This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions. Full article
(This article belongs to the Special Issue Interdisciplinary Research on Predictive Justice)
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16 pages, 329 KiB  
Article
Neural Legal Outcome Prediction with Partial Least Squares Compression
by Charles Condevaux
Stats 2020, 3(3), 396-411; https://doi.org/10.3390/stats3030025 - 18 Sep 2020
Cited by 4 | Viewed by 2629
Abstract
Predicting the outcome of a case from a set of factual data is a common goal in legal knowledge discovery. In practice, solving this task is most of the time difficult due to the scarcity of labeled datasets. Additionally, processing long documents often [...] Read more.
Predicting the outcome of a case from a set of factual data is a common goal in legal knowledge discovery. In practice, solving this task is most of the time difficult due to the scarcity of labeled datasets. Additionally, processing long documents often leads to sparse data, which adds another layer of complexity. This paper presents a study focused on the french decisions of the European Court of Human Rights (ECtHR) for which we build various classification tasks. These tasks consist first of all in the prediction of the potential violation of an article of the convention, using extracted facts. A multiclass problem is also created, with the objective of determining whether an article is relevant to plead given some circumstances. We solve these tasks by comparing simple linear models to an attention-based neural network. We also take advantage of a modified partial least squares algorithm that we integrate in the aforementioned models, capable of effectively dealing with classification problems and scale with sparse inputs coming from natural language tasks. Full article
(This article belongs to the Special Issue Interdisciplinary Research on Predictive Justice)
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20 pages, 268 KiB  
Article
Bottleneck or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts
by Amedeo Santosuosso and Giulia Pinotti
Stats 2020, 3(3), 376-395; https://doi.org/10.3390/stats3030024 - 9 Sep 2020
Cited by 6 | Viewed by 2740
Abstract
So far, in the application of legal analytics to legal sources, the substantive legal knowledge employed by computational models has had to be extracted manually from legal sources. This is the bottleneck, described in the literature. The paper is an exploration of this [...] Read more.
So far, in the application of legal analytics to legal sources, the substantive legal knowledge employed by computational models has had to be extracted manually from legal sources. This is the bottleneck, described in the literature. The paper is an exploration of this obstacle, with a focus on quantitative legal prediction. The authors review the most important studies about quantitative legal prediction published in recent years and systematize the issue by dividing them in text-based approaches, metadata-based approaches, and mixed approaches to prediction. Then, they focus on the main theoretical issues, such as the relationship between legal prediction and certainty of law, isomorphism, the interaction between textual sources, information, representation, and models. The metaphor of a crossroad shows a descriptive utility both for the aspects inside the bottleneck and, surprisingly, for the wider scenario. In order to have an impact on the legal profession, the test bench for legal quantitative prediction is the analysis of case law from the lower courts. Finally, the authors outline a possible development in the Artificial Intelligence (henceforth AI) applied to ordinary judicial activity, in general and especially in Italy, stressing the opportunity the huge amount of data accumulated before lower courts in the online trials offers. Full article
(This article belongs to the Special Issue Interdisciplinary Research on Predictive Justice)
20 pages, 979 KiB  
Article
Improving Access to Justice with Legal Chatbots
by Marc Queudot, Éric Charton and Marie-Jean Meurs
Stats 2020, 3(3), 356-375; https://doi.org/10.3390/stats3030023 - 4 Sep 2020
Cited by 24 | Viewed by 10524
Abstract
On average, one in three Canadians will be affected by a legal problem over a three-year period. Unfortunately, whether it is legal representation or legal advice, the very high cost of these services excludes disadvantaged and most vulnerable people, forcing them to represent [...] Read more.
On average, one in three Canadians will be affected by a legal problem over a three-year period. Unfortunately, whether it is legal representation or legal advice, the very high cost of these services excludes disadvantaged and most vulnerable people, forcing them to represent themselves. For these people, accessing legal information is therefore critical. In this work, we attempt to tackle this problem by embedding legal data in a conversational interface. We introduce two dialog systems (chatbots) created to provide legal information. The first one, based on data from the Government of Canada, deals with immigration issues, while the second one informs bank employees about legal issues related to their job tasks. Both chatbots rely on various representations and classification algorithms, from mature techniques to novel advances in the field. The chatbot dedicated to immigration issues is shared with the research community as an open resource project. Full article
(This article belongs to the Special Issue Interdisciplinary Research on Predictive Justice)
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