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Relevant bibliographies by topics / Legal AI

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Academic literature on the topic 'Legal AI'

Author: Grafiati

Published: 25 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Legal AI"

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Kauffman, Marcos Eduardo, and Marcelo Negri Soares. "AI in legal services: new trends in AI-enabled legal services." Service Oriented Computing and Applications 14, no. 4 (October 18, 2020): 223–26. http://dx.doi.org/10.1007/s11761-020-00305-x.

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Горохова, Светлана. "ON SOME LEGAL APPROACHES TO DETERMINING THE LEGAL PERSONALITY OF ARTIFICIAL INTELLECTUAL SYSTEMS." Rule-of-law state: theory and practice 16, no. 4-1 (April 1, 2020): 31–42. http://dx.doi.org/10.33184/pravgos-2020.4.4.

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The issues of determining the place of artificial intellectual systems in the structure of new legal relations determined by the level of technological development are one of the most urgent tasks of modern Russian law today. The most important problem, determined by the requirements of law enforcement and the need to comply with the requirements of building of the rule-of-law state, is the study of fundamental positions regarding the conditions and procedure for bringing subjects to legal responsibility for torts involving artificial intelligence (AI). Purpose: to analyze approaches to the definition and content of legal personality, in particular, the possible legal personality of artificial intellectual systems, with an emphasis on the rules for imposing liability for torts involving them. Methods include dialectical and metaphysical methods of cognition (as the philosophical basis of the work performed); general scientific methods (analysis, synthesis, abstraction, hypotheses, etc.); as well as special scientific (comparative-legal, legal-dogmatic, cybernetic, interpretation) methods of scientific cognition. Results: the study, for the purpose of determining the legal personality of AI, justifies the greatest significance of its classification into: weak narrow-purpose AI (Narrow AI), strong general-purpose AI (General AI), and super-strong intelligence (Super AI). Based on the materials of scientific sources, the concept of «partial legal capacity» is formulated and the possibility of its application to strong and super-strong AI is scientifically justified. Fundamental theoretical positions (General rule and exceptions) regarding the conditions and procedure for bringing subjects to legal responsibility for torts involving AI are presented.

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Nitta, Hajime Yoshinoand Katsumi. "AI and Law (2)." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 1 (February 20, 1998): 1. http://dx.doi.org/10.20965/jaciii.1998.p0001.

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In the last issue (Vol.1, No.2), we introduced the Legal Expert System (LES) project led by Hajime Yoshino of Meiji Gakuin University, presenting six papers on the LES project. Those papers were mainly related to higher order reasoning systems such as ase-based reasoning, abductive and inductive logic programming, nonmonotonic reasoning, and analogical reasoning. The objective of the LES project was to develop a legal expert system effective for use by lawyers, so the project covers inference mechanisms, analysis of legal knowledge, and user interfaces. In this second special issue on the LES project, we present five more papers, mainly related to the analysis of legal knowledge, legal knowledge representation language, and legal reasoning system user interfaces. Hajime Yoshino analyzes the logical structure of contract law. To develop a knowledge base for the United Nations Convention on Contracts for the International Sale of Goods (CISG), he proposes a clear logical model of the contract law system, which treats relations between events and legal status such as rights and obligations. Yoshino demonstrates that legal metarules are effective in constructing deductive legal reasoning systems, and are appropriate from the viewpoint of jurisprudence. Seiichiro Sakurai discusses the logical features of the legal knowledge representation language, CPF, developed by Hajime Yoshino. CPF is a logic programming language that enhances the representation of complex data structures. CPF is a convenient tool for representing legal knowledge, yet lawyers often attempt to describe nonexecutable forms of CPF rules.Sakurai introduces a way to construct an executable knowledge base from lawyers' CPF rules. Masato Shibasaki and Katsumi Nitta introduce a new framework to formalize nonmonotonic reasoning with dynamic priorities. The several frameworks proposed thus far to model relationships among arguments do not treat complex arguments, composed of strict rules and default rules. They show that the new framework represents such relationships and analyze these relationships for this framework and others. Takashi Miyata and Yuji Matsumoto introduce LES natural language generation using a user interface for lawyers rather than computer scientists. They describe a sentence generation system that translates logical forms provided from an inference engine into natural-language sentences, and present the unification grammar, generation algorithm and graphical debugging tool. To develop a knowledge base, the lawyers of the LES project analyze and represents the relationships between requirements (actions or events) and consequences (legal status) of legal rules in the form of logical flowcharts. Once the appropriateness of a flowchart is confirmed, they convert it to a CPF rule in their knowledge base. Koji Miyagi, Motoki Miura and Jiro Tanaka developed a flowchart editor that makes legal flowcharting easier. To make it easier to decide where to locate flowchart components and draw linens between the components, the editor possesses several algorithms.

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Ho, Juin-Hao, Gwo-Guang Lee, and Ming-Tsang Lu. "Exploring the Implementation of a Legal AI Bot for Sustainable Development in Legal Advisory Institutions." Sustainability 12, no. 15 (July 25, 2020): 5991. http://dx.doi.org/10.3390/su12155991.

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This study explores the implementation of legal artificial intelligence (AI) robot issues for sustainable development related to legal advisory institutions. While a legal advisory AI Bot using the unique arithmetic method of AI offers rules of convenient legal definitions, it has not been established whether users are ready to use one at legal advisory institutions. This study applies the MCDM (multicriteria decision-making) model DEMATEL (decision-making trial and evaluation laboratory)-based Analytical Network Process (ANP) with a modified VIKOR, to explore user behavior on the implementation of a legal AI bot. We first apply DEMATEL-based ANP, called influence weightings of DANP (DEMATEL-based ANP), to set up the complex adoption strategies via systematics and then to employ an M-VIKOR method to determine how to reduce any performance gaps between the ideal values and the existing situation. Lastly, we conduct an empirical case to show the efficacy and usefulness of this recommended integrated MCDM model. The findings are useful for identifying the priorities to be considered in the implementation of a legal AI bot and the issues related to enhancing its implementation process. Moreover, this research offers an understanding of users’ behaviors and their actual needs regarding a legal AI bot at legal advisory institutions. This research obtains the following results: (1) It effectively assembles a decision network of technical improvements and applications of a legal AI bot at legal advisory institutions and explains the feedbacks and interdependences of aspects/factors in real-life issues. (2) It describes how to vary effective results from the current alternative performances and situations into ideal values in order to fit the existing environments at legal advisory institutions with legal AI bot implementation.

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Yoshino, Hajime, and Katsumi Nitta. "Special Issue on AI and Law." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 2 (December 20, 1997): 81–82. http://dx.doi.org/10.20965/jaciii.1997.p0081.

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Lawyers use a reasoning process known as legal reasoning to solve legal problems. Legal expert systems could potentially help lawyers solve legal problems more quick and adequately, enable students to study law at school or at home more easily, and help legal scholars and professionals analyze the law and legal systems more clearly and precisely.In 1992, Hajime Yoshino of Meiji Gakuin University started a “Legal Expert Systems” project. This “Legal Expert” project is funded by the Japanese Ministry of Education, Science and Culture and is scheduled to run from May 1992 to March 1998. Yoshino organized over 30 lawyers and computer scientists to clarify legal knowledge and develop legal expert systems.This project covers a wide range of technologies such as the analysis of legal knowledge, the analysis of legal rules on international trade (United Nations Convention on Contracts for International Sale of Goods (CISG)), legal knowledge representation, legal inference models, utility programs to develop legal knowledge bases, and user interfaces. This project, which ends in March 1998, will focus on developing comprehensive legal expert systems as the final product. In this issue, we present 12 papers written by “Legal Expert” project members.In this number, Hajime Yoshino gives are overview of the legal expert systems project, explaining its aims, objectives, and organization. Six papers that follow his introduction include three on case-based reasoning. Legal rules are given by ambiguous predicates, making it difficult sometimes to determine whether conditions for rules are satisfied by the facts given of an event. In such cases, lawyers often refer to old cases and generate hypotheses through analogical reasoning.Kaoru Hirota, Hajime Yoshino and Ming Qiang Xu apply fuzzy theory to case-based reasoning. A number of related systems have been developed, but most focus on qualitative similarities between old cases and the current case, and cannot measure quantitative similarities. Hirota et al. treat quantitative similarity by applying fuzzy theory, explaining their method using CISG examples.Ken Satoh developed a way to compute an interpretation of undefined propositions in a legal rule using adversarial case-based reasoning. He translated old cases giving possible interpretations for a proposition into clauses in abductive logic programming and introduced abducibles to reason dynamically about important factors in an old case to the interpretation suiting the user’s purpose.Yoshiaki Okubo and Makoto Haraguchi formalized a way of attacking legal argument. Assume that an opponent has constructed a legal argument by applying a statute with an analogical interpretation. From the viewpoint of legal stability, the same statue for similar cases should be applied with the same interpretation. We thereby create a hypothetical case similar to the case in question and examine whether the statue can be interpreted analogically. Such a hypothetically similar case is created with the help of a goal-dependent abstraction framework. If a precedent in which a statue has been applied to a case with a different interpretation – particularly complete interpretation – can be found, the opponent’s argument is attacked by pointing out the incoherence of its interpretation of the statue.Takashi Kanai and Susumu Kunifuji proposed a legal reasoning system using abductive logic programming that deals with ambiguities in described facts and exceptions not described in articles. They examined the problems to be solved to develop legal knowledge bases through abductive logic programming, e.g., how to select ambiguities to be treated in abductive reasoning, how to describe time relationships, and how to describe an exception in terms of the application of abductive logic programming to legal reasoning.Toshiko Wakaki, Ken Satoh, and Katsumi Nitta presented an approach of reasoning about dynamic preferences in the framework of circumscription based on logic programming. To treat dynamic preferences correctly is required in legal reasoning to handle metarules such as lex posterior. This has become a hotly discussed topic in legal reasoning and more general nonmonotic reasoning. Comparisons of their method, Brewka’s approach, and Prakken and Sartor’s approach are discussed.Hiroyuki Matsumoto proposed a general legal reasoning model and a way of describing legal knowledge systematically. He applied his method to Japanese Maritime Traffic Law.Six more papers are to be presented in the next number

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Krysanova, N. V. "ON THE ISSUE OF LEGAL PERSONALITY AND LEGAL DEVELOPMENT OF ARTIFICIAL INTELLIGENCE." Pravovedenie IAZH, no. 1 (2021): 23–31. http://dx.doi.org/10.31249/rgpravo/2021.01.02.

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We study the points of view of Russian and foreign scientists on the possibility of granting AI a certain form of legal personality of an individual or legal entity and solve the problem of the grounds for such an assignment. The article analyzes proposals on classification of forms of legal capacity of intellectual systems, on recognition of the concept of «quasi-personality», predicts potential options for changing the legal personality of AI.

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Dmitriev, Oleg N. "Conceptual idea to optimize institutional typological series of organization and legal forms of legal entities." Revista Amazonia Investiga 9, no. 26 (February 21, 2020): 432–41. http://dx.doi.org/10.34069/ai/2020.26.02.50.

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The concept of a legal entity’s organization and form is introduced in a meaningful aspect. The existing typology of Russian enterprises/firms as legal entities in its organization and forms’ context with an emphasis on commercial organizations is given. There are defined priority discriminatory aspects of the Russian enterprises’ organization and legal form, that means for commercial organization. An assessment is made regarding the applied intuitive-empirical method of these subjects of juridical relations’ organization and legal forms existing typology forming and, accordingly, its non-optimization by the set and institutional characteristics. The conceptual idea to transit in the direction to optimized institutional series of the enterprises’ organization and legal forms is formulated. A general methodological scheme for forming of the optimized institutional series of them is proposed.

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NITTA, Katsumi, and Ken SATOH. "AI Applications to the Law Domain in Japan." Asian Journal of Law and Society 7, no. 3 (October 2020): 471–94. http://dx.doi.org/10.1017/als.2020.35.

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AbstractArtificial intelligence (AI) and law is an AI research area that has a history spanning more than 50 years. In the early stages, several legal-expert systems were developed. Legal-expert systems are tools designed to realize fair judgments in court. In addition to this research, as information and communication technologies and AI technologies have progressed, AI and law has broadened its view from legal-expert systems to legal analytics and, recently, a lot of machine-learning and text-processing techniques have been employed to analyze legal information. The research trends are the same in Japan as well and not only people involved with legal-expert systems, but also those involved with natural language processing as well as lawyers have become interested in AI and law. This report introduces the history of and the research activities on applying AI to the legal domain in Japan.

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Latkovska, Tamara, Pavlo Latkovskyi, and Anastasiia Podolska. "Legal ways of double taxation resolving." Revista Amazonia Investiga 9, no. 26 (February 21, 2020): 365–71. http://dx.doi.org/10.34069/ai/2020.26.02.42.

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The purpose of the article is a comprehensive study of the problems of double taxation, which is at the intersection of categories of taxable entity and taxpayer. According to the purpose, it is substantiated that double taxation arises in the case of taxation of income received by residents abroad, or in the case of a mixed procedure of tax payment, or in the taxation of the distributed part of the profits of enterprises. It has been established that double taxation is also possible with the partial imposition of one object on another, and this can occur both within the same country and under different tax systems. The taxation of petroleum products with excise tax, which resulted from the legislative regulation that led to double taxation, was considered and analyzed. In the process of researching the topic of the article, the authors conclude that, starting from 2016, the legislator actually introduced double taxation with the same tax and accordingly replaced the ad valorem excise tax rate on retail sales of excisable goods with specific ones (Euro tax rate per unit of tax). It is stated that double taxation of one and the same taxpayer is a violation of Article 1 of the Protocol to the Convention for the Protection of Human Rights and Fundamental Freedoms of each natural or legal person, the right to peacefully own their property.

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Shablystyi, Volodymyr, Svitlana Obrusna, Yuriy Levchenko, Vitaliy Gluhoverya, and Viktoriia Rufanova. "Social and legal nature of bullying." Revista Amazonia Investiga 10, no. 37 (March 5, 2021): 78–85. http://dx.doi.org/10.34069/ai/2021.37.01.7.

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The aim of the article is to analyze the concepts of bullying and mobbing, to determine their social and legal nature, to formulate our own definition of these terms. The subject of the study is an analysis of the concepts of bullying and mobbing. The research methodology includes the following methods: system and structural method, formal and dogmatic method, historical method, clustering method, comparative and legal method, legal modeling method and others. The results of the study. The definition of bullying is comprehensively studied, its types are singled out, the phases of its development are determined. Practical implication. The difference between bullying and mobbing, as well as between bullying and conflict, is studied. Value / originality. Based on the research conducted the authors’ concepts of bulling and mobbing are proposed.

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Dissertations / Theses on the topic "Legal AI"

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Royles, Christopher Andrew. "Intelligent presentation and tailoring of online legal information." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343616.

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Daskalopulu, Aspassia-Kaliopi. "Logic-based tools for the analysis and representation of legal contracts." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312171.

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Andersson-Säll, Tim. "Transforming Legal Entity Recognition." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447240.

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Transformer-based architectures have in recent years advanced state-of-the-art performance in Natural Language Processing. Researchers have successfully adapted such models to downstream tasks within NLP in a domain-specific setting. This thesis examines the application of these models to the legal domain by doing Named Entity Recognition (NER) in a setting of scarce training data. Three different pre-trained BERT models are fine-tuned on a set of 101 court case documents, whereof one model is pre-trained on legal corpora and the other two on general corpora. Experiments are run to evaluate the models’ predictive performance given smaller or larger quantities of data to fine-tune on. Results show that BERT models work reasonably well for NER with legal data. Unlike many other domain-specific BERT models, the BERT model trained on legal corpora does not outperform the base models. Modest amounts of annotated data seem sufficient for reasonably good performance.

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Wenestam, Arvid. "Labelling factual information in legal cases using fine-tuned BERT models." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447230.

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Labelling factual information on the token level in legal cases requires legal expertise and is time-consuming. This thesis proposes transfer-learning and fine-tuning implementation of pre-trained state-of-the-art BERT models to perform this labelling task. Investigations are done to compare whether models pre-trained on solely legal corpus outperforms a generic corps trained BERT and the model’s behaviour as the number of cases in the training sample varies. This work showed that the models metric scores are stable and on par using 40-60 professionally annotated cases as opposed to using the full sample of 100 cases. Also, the generic-trained BERT model is a strong baseline, and a solely pre-trained BERT on legal corpus is not crucial for this task.

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Egil, Martinsson. "Kvantitativ Modellering av förmögenhetsrättsliga dispositiva tvistemål." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-224516.

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I den här uppsatsen beskrivs en ansats till att med hjälp av statistiska metoder förutse utfallet i förmögenhetsrättsliga dispositiva tvistemål. Logistiska- och multilogistiska regressionsmodeller skattades på data för 13299 tvistemål från 5 tingsrätter och användes till att förutse utfallet för 1522 tvistemål från 3 andra tingsrätter. Modellerna presterade bättre än slumpen vilket ger stöd för slutsatsen att man kan använda statistiska metoder för att förutse utfallet i denna typ av tvistemål.
BACKROUND: The idea of legal automatization is a controversial topic that's been discussed for hundreds of years, in modern times in the context of Law & Artificial Intelligence. Strangely, real world applications are very rare. Assuming that the judicial system is like any system that transforms inputs into outputs one would think that we should be able measure it and and gain insight into its inner workings and ultimately use these measurements to make predictions about its output. In this thesis, civil procedures on commercial matters amenable to out-of-court settlement (Förmögenhetsrättsliga Dispositiva Tvistemål) was devoted particular interest and the question was posed: Can we predict the outcome of civil procedures using Statistical Methods? METHOD: By analyzing procedural law and legal doctrin, the civil procedure was modeled in terms of a random variable with a discrete observable outcome. Some data for 14821 cases was extracted from eight different courts. Five of these courts (13299 cases) were used to train the models and three courts (1522 cases) were chosen randomly and kept untouched for validation. Most cases seemed to concern monetary claims (66%) and/or damages (12%). Binary- and Multinomial- logistic regression methods were used as classifiers. RESULTS: The models where found to be uncalibrated but they clearly outperformed random score assignment at separating classes and at a preset threshold gave accuracies significantly higher (p<<0.001) than that of random guessing and in identifying settlements or the correct type of verdict performance was significantly better (p<<0.003) than consequently guessing the most common outcome. CONCLUSION: Using data for cases from one set of courts can to some extent predict the outcomes of cases from another set of courts. The results from applying the models to new data concludes that the outcome in civil processes can be predicted using statistical methods.

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Rien, Alexander. "The art of AI : the impact of artificial intelligence on the merger and acquisition strategy." Master's thesis, 2018. http://hdl.handle.net/10400.14/26896.

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Based on a lack of studies in this specific field and the theory that manual as well as cognitive tasks can be replaced by machines, this study explores, using a qualitative research method, the impact of artificial intelligence on the Merger&Acquisition process. An analysis of multinational interviews with experts from different industries and a framework adapted to the Due Diligence process show that there is and will be an impact of Artificial Intelligence on the Due Diligence process as the most crucial process of the Merger& Acquisitions. Although the impact of Artificial Intelligence is nowadays the greatest on Legal Due Diligence and AI-based solutions are already offered, this study, however, states that within the next 5-10 years, even 96% of all tasks of the Due Diligence will be partially or fully substituted. Furthermore, the framework reinforces the underlying theory that both manual and cognitive tasks can already be replaced by machines. Among the reasons why AI has nowadays not yet been adopted in all Due Diligence are the fact that the target companies' data is too different to train a machine, that due diligence involves a lot of communication and that humans are not yet ready for this cultural change. Based on these findings, managers of companies conducting due diligences are advised to prepare their company and employees for the implementation of Artificial Intelligence by following the steps described in Kotter's 8-step change model.
Baseado na falta de estudos neste campo específico e na teoria de que tanto as tarefas manuais como as cognitivas podem ser substituídas por máquinas, este estudo explora, utilizando um método de pesquisa qualitativa, o impacto da inteligência artificial no processo de fusão e aquisição de empresas. Uma análise de entrevistas multinacionais com especialistas de diferentes indústrias e um quadro adaptado ao processo de Due Diligence mostram que existe e existirá um impacto da Inteligência Artificial no processo de Due Diligence como processo mais crucial do Merger& Acquisitions. Embora o impacto da Inteligência Artificial seja atualmente o maior em Due Diligence Legal e as soluções baseadas em IA já estejam disponíveis, este estudo, no entanto, afirma que nos próximos 5-10 anos, até 96% de todas as tarefas do Due Diligence serão parcial ou totalmente substituídas. Além disso, o framework reforça a teoria subjacente de que as tarefas manuais e cognitivas já podem ser substituídas por máquinas. Entre as razões pelas quais a IA ainda não foi adotada em todas as Due Diligences está o fato de que os dados das empresas-alvo são muito diferentes para ensinar uma máquina, que a due diligence envolve muita comunicação e que os humanos ainda não estão prontos para essa mudança cultural. Com base nessas descobertas, os gerentes de empresas que realizam as devidas diligências são orientados a preparar suas empresas e funcionários para a implementação da Inteligência Artificial, seguindo os passos descritos no modelo de mudança de 8 passos de Kotter.

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Books on the topic "Legal AI"

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Casanovas, Pompeu, Ugo Pagallo, Monica Palmirani, and Giovanni Sartor, eds. AI Approaches to the Complexity of Legal Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45960-7.

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Pagallo, Ugo, Monica Palmirani, Pompeu Casanovas, Giovanni Sartor, and Serena Villata, eds. AI Approaches to the Complexity of Legal Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00178-0.

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Diritti diversi: La legge negata ai gay. Milano: Bompiani, 2009.

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Ai zi bing yu fa lü. Beijing Shi: Zhongguo zheng fa da xue chu ban she, 2005.

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Da ai: Zou jin Zhongguo fa lü yuan zhu gong zuo zhe. Beijing Shi: Zhongguo guang bo dian shi chu ban she, 2009.

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Ai zi bing yu ren quan bao hu. Beijing: Zhonggu fa zhi chu ban she, 2008.

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Palmirani, Monica, Ugo Pagallo, Pompeu Casanovas, and Giovanni Sartor, eds. AI Approaches to the Complexity of Legal Systems. Models and Ethical Challenges for Legal Systems, Legal Language and Legal Ontologies, Argumentation and Software Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35731-2.

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Selleri, Gianni. Legislazione e handicappati: Guida ai diritti civili degli handicappati. Tirrenia (Pisa): Edizioni del Cerro, 2002.

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Ai zi bing yu ren quan: AIDS and human rights. Beijing Shi: Fa lü chu ban she, 2012.

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Ie ai kabane: Kindai Nihon no kazoku shisō. Tōkyō: Keisō Shobō, 1994.

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Book chapters on the topic "Legal AI"

1

Henning, Klaus. "The Ethical and Legal Implications." In Gamechanger AI, 99–105. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52897-3_12.

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Turner, Jacob. "Legal Personality for AI." In Robot Rules, 173–205. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96235-1_5.

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Zekos, Georgios I. "AI and Legal Issues." In Economics and Law of Artificial Intelligence, 401–60. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64254-9_10.

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Magnusson Sjöberg, Cecilia. "Legal Automation: AI and Law Revisited." In Legal Tech, Smart Contracts and Blockchain, 173–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6086-2_7.

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Nissan, Ephraim, and Daniel Rousseau. "Towards AI formalisms for legal evidence." In Lecture Notes in Computer Science, 328–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63614-5_32.

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Libal, Tomer, and Tereza Novotná. "Towards Transparent Legal Formalization." In Explainable and Transparent AI and Multi-Agent Systems, 296–313. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82017-6_18.

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Galgani, Filippo, and Achim Hoffmann. "LEXA: Towards Automatic Legal Citation Classification." In AI 2010: Advances in Artificial Intelligence, 445–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_45.

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Schneeberger, David, Karl Stöger, and Andreas Holzinger. "The European Legal Framework for Medical AI." In Lecture Notes in Computer Science, 209–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57321-8_12.

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Schröder, Wolfgang M. "Robots and Rights: Reviewing Recent Positions in Legal Philosophy and Ethics." In Robotics, AI, and Humanity, 191–203. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54173-6_16.

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AbstractControversies about the moral and legal status of robots and of humanoid robots in particular are among the top debates in recent practical philosophy and legal theory. As robots become increasingly sophisticated, and engineers make them combine properties of tools with seemingly psychological capacities that were thought to be reserved for humans, such considerations become pressing. While some are inclined to view humanoid robots as more than just tools, discussions are dominated by a clear divide: What some find appealing, others deem appalling, i.e. “robot rights” and “legal personhood” for AI systems. Obviously, we need to organize human–robot interactions according to ethical and juridical principles that optimize benefit and minimize mutual harm. Avoiding disrespectful treatment of robots can help to preserve a normative basic ethical continuum in the behaviour of humans. This insight can contribute to inspire an “overlapping consensus” as conceptualized by John Rawls in further discussions on responsibly coordinating human/robot interactions.

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Lipinsky, Dmitry A., Roman A. Romashov, Aleksandra A. Musatkina, Svetlana G. Golenok, and Elena A. Bryleva. "The Problems of Legal Regulation of AI: A Rather-Legal Research." In Artificial Intelligence: Anthropogenic Nature vs. Social Origin, 411–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39319-9_47.

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Conference papers on the topic "Legal AI"

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Laukyte, Migle. "AI as a Legal Person." In ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3322640.3326701.

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Bellucci, Emilia, and John Zeleznikow. "AI techniques for modelling legal negotiation." In the seventh international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/323706.323723.

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Buddarapu, Venkata Nagaraju, and Arunprasath Shankar. "Adapting Covariate Shift for Legal AI." In ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3322640.3326720.

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Broman, Morgan M., and Pamela Finckenberg-Broman. "Human-Robotics&AI interaction: The Robotics/AI legal entity (RAiLE©)." In 2017 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 2017. http://dx.doi.org/10.1109/istas.2017.8318980.

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Blass, Joseph A. "Legal, Ethical, Customizable Artificial Intelligence." In AIES '18: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3278721.3278793.

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Delgado, Fernando A. "Machine Learning in Legal Practice." In AIES '19: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3306618.3314324.

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Golbin, Ilana, Anand S. Rao, Ali Hadjarian, and Daniel Krittman. "Responsible AI: A Primer for the Legal Community." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377738.

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Wang, Shaoyan. "Application of Artificial Intelligence (ai) in Legal Imagination Technology." In 2020 International Conference on E-Commerce and Internet Technology (ECIT). IEEE, 2020. http://dx.doi.org/10.1109/ecit50008.2020.00057.

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"Appropriateness and feasibility of legal personhood for AI systems." In The International Conference on Robot Ethics and Standards (ICRES 2018). CLAWAR Association Ltd, UK, 2018. http://dx.doi.org/10.13180/icres.2018.20-21.08.017.

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Fernández Martínez, María del Carmen, and Alberto Fernández. "AI in Recruiting. Multi-agent Systems Architecture for Ethical and Legal Auditing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/903.

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Artificial Intelligence (AI) domain-specific applications may have different ethical and legal implications. One of the current questions of AI is the challenges behind the analysis of job video-interviews. There are pros and cons to using AI in recruitment processes, and potential consequences for candidates, companies and states. Furthermore, the deficit of regulation of these systems reinforces the need for external and neutral auditing of the types of analysis made in interviews. We, therefore, propose a Multi-agent system architecture for neutral auditing to guarantee an inclusive and accurate AI and to reduce the potential discrimination in the job market.

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Reports on the topic "Legal AI"

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Baker, James E. Ethics and Artificial Intelligence: A Policymaker's Introduction. Center for Security and Emerging Technology, April 2021. http://dx.doi.org/10.51593/20190022.

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The law plays a vital role in how artificial intelligence can be developed and used in ethical ways. But the law is not enough when it contains gaps due to lack of a federal nexus, interest, or the political will to legislate. And law may be too much if it imposes regulatory rigidity and burdens when flexibility and innovation are required. Sound ethical codes and principles concerning AI can help fill legal gaps. In this paper, CSET Distinguished Fellow James E. Baker offers a primer on the limits and promise of three mechanisms to help shape a regulatory regime that maximizes the benefits of AI and minimizes its potential harms.

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