Introduction to the special issue on Artificial Intelligence for Justice (AI4J)
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Technological breakthroughs in machine learning, natural language processing, ubiquitous computing, data science, and argumentation technology;
The changing attitude towards technology in the legal domain;
The much increased availability of legal data on the internet;
The recent success of AI applications in the private and public domain;
The success of technology supporting access to law, legal empowerment, and transparency;
The increased need for norms embedded in technology (autonomous driving and warfare, big data analysis for crime fighting and counterterrorism).
How can AI & Law research contribute to improving legal work in, for example, courts, law firms, public administration, police practice and businesses?
How should AI & Law research change in light of the recent research breakthroughs and technological developments? For example, how can traditional research on legal knowledge bases, legal reasoning and legal argument be combined with data science, machine learning and natural language processing?
In the invited paper Data-Centric and Logic-Based Models for Automated Legal Problem Solving, Karl Branting analyzes how logic-based approaches to legal problem solving can work together with data-centric techniques, depending on the legal task one aims to support.
In the paper Norms and Value Based Reasoning: Justifying Compliance and Violation, Trevor Bench-Capon and Sanjay Modgil argue that software agents should be able to reason about norms and values, especially when the rules sometimes should be broken.
In the paper On the Concept of Relevance in Legal Information Retrieval, Marc van Opijnen and Christiana Santos discuss a conceptual framework for relevance in information retrieval, tuned to the development and improvement of legal software tools.
In the paper Reading Agendas Between the Lines, an exercise, Giovanni Sileno, Alexander Boer and Tom van Engers discuss the operationalization of software agents in the setting of compliance checking, discussing tax frauds in real-estate transactions as an illustration.
In the paper Recognizing Cited Facts and Principles in Legal Judgements, Olga Shulayeva, Advaith Siddharthan and Adam Wyner investigate to what extent facts and principles can be identified in precedent cases, by studying agreement between human annotators and by supervised machine learning.
In the paper Proof With and Without Probabilities, Bart Verheij discusses correct evidential reasoning using arguments, scenarios and probabilities, focusing on connections between qualitative and quantitative analytic methods, and using Alfred Hitchcock’s film To Catch A Thief as an illustration.
The International Association for Artificial Intelligence and Law (IAAIL);
The Foundation for Legal Knowledge Based Systems (JURIX);
The BeNeLux Vereniging voor Kunstmatige Intelligentie (BNVKI);
The Institute for Artificial Intelligence and Cognitive Engineering, University of Groningen (ALICE).
Kevin Ashley, University of Pittsburgh, USA
Katie Atkinson, University of Liverpool, UK
Trevor Bench-Capon, University of Liverpool, UK
Karl Branting, The MITRE Corporation, USA
Pompeu Casanovas, Universitat Autnoma de Barcelona, Spain; Deakin University, Australia
Jack G. Conrad, Thomson Reuters, USA
Enrico Francesconi, ITTIG-CNR, Italy
Tom Gordon, Fraunhofer FOKUS, Germany
Guido Governatori, NICTA, Australia
Matthias Grabmair, Intelligent Systems Program, University of Pittsburgh, USA
Jeroen Keppens, Kings College London, UK
David Lewis, Chicago, USA
Monica Palmirani, CIRSFID, Italy
Dory Reiling, Court of Amsterdam, The Netherlands
Erich Schweighofer, University of Vienna, Austria
Jaap van den Herik, Leiden University, The Netherlands
Serena Villata, INRIA Sophia Antipolis, France
Radboud Winkels, University of Amsterdam, The Netherlands
Adam Wyner, University of Aberdeen, UK