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  1.  1
    The winter, the summer and the summer dream of artificial intelligence in law: Presidential address to the 18th International Conference on Artificial Intelligence and Law.Enrico Francesconi - 2022 - Artificial Intelligence and Law 30 (2):147-161.
    This paper reflects my address as IAAIL president at ICAIL 2021. It is aimed to give my vision of the status of the AI and Law discipline, and possible future perspectives. In this respect, I go through different seasons of AI research : from the Winter of AI, namely a period of mistrust in AI, to the Summer of AI, namely the current period of great interest in the discipline with lots of expectations. One of the results of the first (...)
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  2.  3
    Logical English meets legal English for swaps and derivatives.Robert Kowalski & Akber Datoo - 2022 - Artificial Intelligence and Law 30 (2):163-197.
    In this paper, we present an informal introduction to Logical English and illustrate its use to standardise the legal wording of the Automatic Early Termination clauses of International Swaps and Derivatives Association Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language, which is designed both to be computer-executable and to be readable by English speakers (...)
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  3.  9
    Legal information retrieval for understanding statutory terms.Jaromír Šavelka & Kevin D. Ashley - 2022 - Artificial Intelligence and Law 30 (2):245-289.
    In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts, specialized document types, (...)
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  4.  4
    Quantifying the genericness of trademarks using natural language processing: an introduction with suggested metrics.Cameron Shackell & Lance De Vine - 2022 - Artificial Intelligence and Law 30 (2):199-220.
    If a trademark becomes a generic term, it may be cancelled under trademark law, a process known as genericide. Typically, in genericide cases, consumer surveys are brought into evidence to establish a mark’s semantic status as generic or distinctive. Some drawbacks of surveys are cost, delay, small sample size, lack of reproducibility, and observer bias. Today, however, much discourse involving marks is online. As a potential complement to consumer surveys, therefore, we explore an artificial intelligence approach based chiefly on word (...)
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  5.  11
    Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset.Sinh Trong Vu, Minh Le Nguyen & Ken Satoh - 2022 - Artificial Intelligence and Law 30 (2):221-243.
    Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation, (...)
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  6.  25
    A review of predictive policing from the perspective of fairness. [REVIEW]Kiana Alikhademi, Emma Drobina, Diandra Prioleau, Brianna Richardson, Duncan Purves & Juan E. Gilbert - 2022 - Artificial Intelligence and Law 30 (1):1-17.
    Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there (...)
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  7.  7
    Correction To: A Review of Predictive Policing From the Perspective of Fairness. [REVIEW]Kiana Alikhademi, Emma Drobina, Diandra Prioleau, Brianna Richardson, Duncan Purves & Juan E. Gilbert - 2022 - Artificial Intelligence and Law 30 (1):19-20.
    An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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  8.  3
    Symbiosis with artificial intelligence via the prism of law, robots, and society.Stamatis Karnouskos - 2022 - Artificial Intelligence and Law 30 (1):93-115.
    The rapid advances in Artificial Intelligence and Robotics will have a profound impact on society as they will interfere with the people and their interactions. Intelligent autonomous robots, independent if they are humanoid/anthropomorphic or not, will have a physical presence, make autonomous decisions, and interact with all stakeholders in the society, in yet unforeseen manners. The symbiosis with such sophisticated robots may lead to a fundamental civilizational shift, with far-reaching effects as philosophical, legal, and societal questions on consciousness, citizenship, rights, (...)
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  9.  4
    A quantitative approach to ranking corporate law precedents in the Brazilian Superior Court of Justice.José Luiz Nunes & Ivar A. Hartmann - 2022 - Artificial Intelligence and Law 30 (1):117-145.
    This paper aims to contribute to the goal of finding influential legal precedents by quantitative methods. A lot of work has been made in this direction worldwide, especially in the context of common law jurisdictions. However, this type of work is extremely scarce in the Brazilian literature. In addition, our work also contributes to the research of network analysis and the law by applying these methods to unprecedented amount of data and narrowing our inquiry to a single law area, corporate (...)
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  10.  1
    Detecting and explaining unfairness in consumer contracts through memory networks.Federico Ruggeri, Francesca Lagioia, Marco Lippi & Paolo Torroni - 2022 - Artificial Intelligence and Law 30 (1):59-92.
    Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the (...)
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  11.  5
    Clustering of Brazilian legal judgments about failures in air transport service: an evaluation of different approaches.Isabela Cristina Sabo, Thiago Raulino Dal Pont, Pablo Ernesto Vigneaux Wilton, Aires José Rover & Jomi Fred Hübner - 2022 - Artificial Intelligence and Law 30 (1):21-57.
    The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina. The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo, K-means and Affinity Propagation. We evaluate the results based on the following criteria: entropy and purity; algorithm's ability in providing labels; legal expert’s (...)
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