Publisher: Springer-Verlag (Total: 2626 journals)

Similar Journals
 Artificial Intelligence and LawJournal Prestige (SJR): 0.937 Citation Impact (citeScore): 2Number of Followers: 13      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-8382 - ISSN (Online) 0924-8463 Published by Springer-Verlag  [2626 journals]
• Automatically running experiments on checking multi-party contracts
• Abstract: Abstract Contracts play an important role in business management where relationships among different parties are dictated by legal rules. Electronic contracts have emerged mostly due to technological advances and electronic trading between companies and customers. New challenges have then arisen to guarantee reliability among the stakeholders in electronic negotiations. In this scenario, automatic verification of electronic contracts appeared as an imperative support, specially the conflict detection task of multi-party contracts. The problem of checking contracts has been largely addressed in the literature, but there are few, if any, methods and practical tools that can deal with multi-party contracts using a contract language with deontic and dynamic aspects as well as relativizations, over the same formalism. In this work we present an automatic checker for finding conflicts on multi-party contracts modeled by an extended contract language with deontic operators and relativizations. Moreover a well-known case study of sales contract is modeled and automatically verified by our tool. Further, we performed practical experiments in order to evaluate the efficiency of our method and the practical tool.
PubDate: 2020-09-21

• Is hybrid formal theory of arguments, stories and criminal evidence well
suited for negative causation'
• Abstract: Abstract In this paper, I have two primary goals. First, I show that the causal-based story approach in A hybrid formal theory of arguments, stories and criminal evidence (or Hybrid Theory, for short) is ill suited to negative (or absence) causation. In the literature, the causal-based approach requires that hypothetical stories be causally linked to the explanandum. Many take these links to denote physical or psychological causation, or temporal precedence. However, understanding causality in those terms, as I will show, cannot capture cases of negative causation, which are of interest to the Law. In keeping with this, I also discuss some of the difficulties Hybrid Theory invites by remaining silent on the nature of the causal links. In my second aim, I sketch a way for Hybrid Theory to overcome this problem. By replacing the original, underlying causal structure with contrastive causation in the law, Hybrid Theory can represent reasoning in which the evidence that is appealed to is causally linked via negative causation to the explananda.
PubDate: 2020-09-01

• Appellate Court Modifications Extraction for Portuguese
• Abstract: Abstract Appellate Court Modifications Extraction consists of, given an Appellate Court decision, identifying the proposed modifications by the upper Court of the lower Court judge’s decision. In this work, we propose a system to extract Appellate Court Modifications for Portuguese. Information extraction for legal texts has been previously addressed using different techniques and for several languages. Our proposal differs from previous work in two ways: (1)  our corpus is composed of Brazilian Appellate Court decisions, in which we look for a set of modifications provided by the Court; and (2) to automatically extract the modifications, we use a traditional Machine Learning approach and a Deep Learning approach, both as alternative solutions and as a combined solution. We tackle the Appellate Court Modifications Extraction task, experimenting with a wide variety of methods. In order to train and evaluate the system, we have built the KauaneJunior corpus, using public data disclosed by the Appellate State Court of Rio de Janeiro jurisprudence database. Our best method, which is a Bidirectional Long Short-Term Memory network combined with Conditional Random Fields, obtained an $$F_{\beta = 1}$$ score of 94.79%.
PubDate: 2020-09-01

• Legal requirements on explainability in machine learning
• Abstract: Abstract 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.
PubDate: 2020-07-30

• The promise and pitfall of automated text-scaling techniques for the
analysis of jurisprudential change
• Abstract: Abstract 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 of computer-based jurisprudential estimates pose major methodological challenges, I conclude that automated text-scaling methods hold out great promise for legal research.
PubDate: 2020-07-11

• Scalable and explainable legal prediction
• Abstract: Abstract 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 text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed semi-supervised case annotation for legal explanations, exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value.
PubDate: 2020-06-24

• Populating legal ontologies using semantic role labeling
• Abstract: Abstract 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.
PubDate: 2020-06-24

• In memoriam Douglas N. Walton: the influence of Doug Walton on AI and law
• Abstract: Abstract Doug Walton, who died in January 2020, was a prolific author whose work in informal logic and argumentation had a profound influence on Artificial Intelligence, including Artificial Intelligence and Law. He was also very interested in interdisciplinary work, and a frequent and generous collaborator. In this paper seven leading researchers in AI and Law, all past programme chairs of the International Conference on AI and Law who have worked with him, describe his influence on their work.
PubDate: 2020-06-16

• Evaluating causes of algorithmic bias in juvenile criminal recidivism
• Abstract: Abstract In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning (ML) 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 (SAVRY), 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 the data with respect to two protected groups, foreigners and women. In particular, we look at (1) the differences in the prevalence of re-offense between protected groups and (2) the influence of protected group or correlated features in the prediction. Our experiments show that both can lead to disparity between groups on the considered group fairness metrics. We observe that methods to mitigate the influence of either cause do not guarantee fair outcomes. An analysis of feature importance using LIME, a machine learning interpretability method, shows that some mitigation methods can shift the set of features that ML techniques rely on away from demographics and criminal history which are highly correlated with sensitive features.
PubDate: 2020-06-07

• Taking stock of legal ontologies: a feature-based comparative analysis
• Abstract: Abstract Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated.
PubDate: 2020-06-01

• Using machine learning to predict decisions of the European Court of Human
Rights
• Abstract: Abstract When courts started publishing judgements, big data analysis (i.e. large-scale statistical analysis of case law and machine learning) within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict (future) judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our (relatively simple) approach highlights the potential of machine learning approaches in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance (average accuracy range from 58 to 68%). Furthermore, we demonstrate that we can achieve a relatively high classification performance (average accuracy of 65%) when predicting outcomes based only on the surnames of the judges that try the case.
PubDate: 2020-06-01

• ICAIL Doctoral Consortium, Montreal 2019
• Abstract: Abstract This is a report on the Doctoral Consortium co-located with the 17th International Conference on Artificial Intelligence and Law in Montreal.
PubDate: 2020-05-26

• Administrative due process when using automated decision-making in public
administration: some notes from a Finnish perspective
• Abstract: Abstract 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 due process have to be provided for by the national law-maker. It is suggested that all countries have a need to review their rules of administrative due process with a view to bringing them up to date regarding the requirements of automated decision-making. In whichever way the legislation is framed, the key issues are that persons who develop the algorithm and the code as well as persons who run or deal with the software within public authorities are aware of the preventive safeguards of legality in the context of automated decision-making, not only of the reactive safeguards constituted by the complaint procedures, and that legal mechanisms exist under which these persons can be held accountable and liable for decisions produced by automated decision-making. It is also argued that only rule-based systems of automatized decision-making are compatible with the rule of law and that there is a general interest in preventing a development into a rule of algorithm.
PubDate: 2020-05-22

• Artificial intelligence as law
• Abstract: Abstract Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI' One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever.
PubDate: 2020-05-14

• Correction to: Modeling law search as prediction
• Abstract: In the original publication of the article
PubDate: 2020-04-13

• Law and software agents: Are they “Agents” by the way'
• Abstract: Abstract 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 an unattended software, or whether it is done automatically by an attended software. Throughout this paper, it is argued that the introduction of “one size” regulation without sufficient consideration of the nature of software agents or the environments in which they communicate might lead to a divorce between the legal theory and technological practice. It is also concluded that it is incorrect to deal with software agents as if they were either legal persons or nothing without in any way accounting for the fact that there are various kinds of such agents endowed with different levels of autonomy, mobility, intelligence, and sophistication. However, this paper is not intended to provide the final answer to all problematic questions posed by the emergence of intelligent software agents, but is designed to provide some kind of temporary relief until such agents reach a more reliable and autonomous level whereby law begins to regard them, rather than their users, as the source of the relevant action.
PubDate: 2020-03-23

• Arguing about causes in law: a semi-formal framework for causal arguments
• Abstract: Abstract Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation (but-for, NESS, ‘actual causation’) to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law.
PubDate: 2020-03-01

• A new use case for argumentation support tools: supporting discussions of
Bayesian analyses of complex criminal cases
• Abstract: Abstract In this paper a new use case for legal argumentation support tools is considered: supporting discussions about analyses of complex criminal cases with the help of Bayesian probability theory. By way of a case study, two actual discussions between experts in court cases are analysed on their argumentation structure. In this study the usefulness of several recognised argument schemes is confirmed, a new argument scheme for arguments from statistics are proposed, and an analysis is given of debates between experts about the validity of their arguments. From a practical point of view the case study yields insights into the design of support software for discussions about Bayesian analyses of complex criminal cases.
PubDate: 2020-03-01

• Assessment criteria or standards of proof' An effort in clarification
• Abstract: Abstract The paper provides a conceptual distinction between evidence assessment criteria and standards of proof. Evidence must be assessed in order to check whether it satisfies a relevant standard of proof, and the assessment is operated with some criterion; so both criteria and standards are necessary for fact-finding. In addition to this conceptual point, the article addresses three main questions: (1) Why do some scholars and decision-makers take assessment criteria as standards of proof and vice versa' (2) Why do systems differ as to criteria and standards' (3) How can a system work if it neglects one of these things' The answers to the first and second question come from the historical and procedural differences between the systems. The answer to the third focuses on the functional connection between criteria and standards.
PubDate: 2020-03-01

• Evidence &amp; decision making in the law: theoretical, computational
and empirical approaches
• PubDate: 2019-06-22

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