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  1.  11
    Legal requirements on explainability in machine learning.Adrien Bibal, Michael Lognoul, Alexandre de Streel & Benoît Frénay - 2021 - Artificial Intelligence and Law 29 (2):149-169.
    Deep learning and other black-box models are becoming more and more popular today. Despite their high performance, they may not be accepted ethically or legally because of their lack of explainability. This paper presents the increasing number of legal requirements on machine learning model interpretability and explainability in the context of private and public decision making. It then explains how those legal requirements can be implemented into machine-learning models and concludes with a call for more inter-disciplinary research on explainability.
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  2.  8
    Scalable and Explainable Legal Prediction.L. Karl Branting, Craig Pfeifer, Bradford Brown, Lisa Ferro, John Aberdeen, Brandy Weiss, Mark Pfaff & Bill Liao - 2021 - Artificial Intelligence and Law 29 (2):213-238.
    Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support—explainable outcome prediction—that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an attention network for prediction and attention weights to highlight salient case (...)
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  3.  4
    The Promise and Pitfall of Automated Text-Scaling Techniques for the Analysis of Jurisprudential Change.Arthur Dyevre - 2021 - Artificial Intelligence and Law 29 (2):239-269.
    I consider the potential of eight text-scaling methods for the analysis of jurisprudential change. I use a small corpus of well-documented German Federal Constitutional Court opinions on European integration to compare the machine-generated scores to scholarly accounts of the case law and legal expert ratings. Naive Bayes, Word2Vec, Correspondence Analysis and Latent Semantic Analysis appear to perform well. Less convincing are the performance of Wordscores, ML Affinity and lexicon-based sentiment analysis. While both the high-dimensionality of judicial texts and the validation (...)
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  4.  6
    Modifying the reason model.John Horty - 2021 - Artificial Intelligence and Law 29 (2):271-285.
    In previous work, I showed how the “reason model” of precedential constraint could naturally be generalized from the standard setting in which it was first developed to a richer setting in which dimensional information is represented as well. Surprisingly, it then turned out that, in this new dimensional setting, the reason model of constraint collapsed into the “result model,” which supports only a fortiori reasoning. The purpose of this note is to suggest a modification of the reason model of constraint (...)
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  5.  3
    Populating legal ontologies using semantic role labeling.Llio Humphreys, Guido Boella, Leendert van der Torre, Livio Robaldo, Luigi Di Caro, Sepideh Ghanavati & Robert Muthuri - 2021 - Artificial Intelligence and Law 29 (2):171-211.
    This article seeks to address the problem of the ‘resource consumption bottleneck’ of creating legal semantic technologies manually. It describes a semantic role labeling based information extraction system to extract definitions and norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system.
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  6.  8
    Evaluating causes of algorithmic bias in juvenile criminal recidivism.Marius Miron, Songül Tolan, Emilia Gómez & Carlos Castillo - 2021 - Artificial Intelligence and Law 29 (2):111-147.
    In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning algorithms. We show that in our dataset, containing hundreds of cases, ML models achieve better predictive power than a structured professional risk assessment tool, the Structured Assessment of Violence Risk in Youth, at the expense of not satisfying relevant group fairness metrics that SAVRY does satisfy. We explore in more detail two possible causes of this algorithmic bias that are related to biases in (...)
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  7.  2
    Correction To: Modeling Law Search as Prediction.Faraz Dadgostari, Mauricio Guim, Peter A. Beling, Michael A. Livermore & Daniel N. Rockmore - 2021 - Artificial Intelligence and Law 29 (1):1-1.
    In the original publication of the article.
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  8.  6
    Modeling law search as prediction.Faraz Dadgostari, Mauricio Guim, Peter A. Beling, Michael A. Livermore & Daniel N. Rockmore - 2021 - Artificial Intelligence and Law 29 (1):3-34.
    Law search is fundamental to legal reasoning and its articulation is an important challenge and open problem in the ongoing efforts to investigate legal reasoning as a formal process. This Article formulates a mathematical model that frames the behavioral and cognitive framework of law search as a sequential decision process. The model has two components: first, a model of the legal corpus as a search space and second, a model of the search process that is compatible with that environment. The (...)
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  9.  7
    Law and software agents: Are they “Agents” by the way?Emad Abdel Rahim Dahiyat - 2021 - Artificial Intelligence and Law 29 (1):59-86.
    Using intelligent software agents in the world of e-commerce may give rise to many difficulties especially with regard to the validity of agent-based contracts and the attribution of liability for the actions of such agents. This paper thus critically examines the main approaches that have been advanced to deal with software agents, and proposes the gradual approach as a way of overcoming the difficulties of such agents by adopting different standards of responsibility depending whether the action is done autonomously by (...)
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  10.  3
    Information extraction framework to build legislation network.Neda Sakhaee & Mark C. Wilson - 2021 - Artificial Intelligence and Law 29 (1):35-58.
    This paper concerns an information extraction process for building a dynamic legislation network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply information extraction methodologies by identifying distinct expressions in legal text in order to extract network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques to produce reliable network data-sets with more than 98% precision and recall. The applications and the complexity of (...)
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  11.  7
    Administrative due process when using automated decision-making in public administration: some notes from a Finnish perspective.Markku Suksi - 2021 - Artificial Intelligence and Law 29 (1):87-110.
    Various due process provisions designed for use by civil servants in administrative decision-making may become redundant when automated decision-making is taken into use in public administration. Problems with mechanisms of good government, responsibility and liability for automated decisions and the rule of law require attention of the law-maker in adapting legal provisions to this new form of decision-making. Although the general data protection regulation of the European Union is important in acknowledging automated decision-making, most of the legal safeguards within administrative (...)
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