Kevin D. Ashley. January, 2018 1 CURRICULUM VITA
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AI4J – Artificial Intelligence for Justice
AI4J { Artificial Intelligence for Justice August 30, 2016 The Hague, The Netherlands Workshop at the 22nd European Conference on Artificial Intelligence (ECAI 2016) Workshop chairs Floris Bex Department of Information and Computing Sciences, Utrecht University Tom van Engers Leibniz Center for Law, Faculty of Law, University of Amsterdam Henry Prakken Faculty of Law, University of Groningen; Department of Information and Computing Sciences, Utrecht University Bart Verheij Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen Artificial intelligence is currently in the centre of attention of legal professionals. An abundance of startup companies explore the application of AI techniques in the domain of law, and there is even talk of artificially intelligent legal assistants disrupting the legal market space. Factors driving the increased attention for legal AI include: Technological breakthroughs in machine learning, natural language process- ing, 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). The aim of this workshop is to investigate opportunities and challenges -
Measuring, Monitoring, and Managing Legal Complexity
A5_RUHL&KATZ.DOCX (DO NOT DELETE) 10/16/2015 8:55 AM Measuring, Monitoring, and Managing Legal Complexity J.B. Ruhl & Daniel Martin Katz ABSTRACT: The American legal system is often accused of being “too complex.” For example, most Americans believe the Tax Code is too complex. But what does that mean, and how would one prove the Tax Code is too complex? Both the descriptive claim that an element of law is complex and the normative claim that it is too complex should be empirically testable hypotheses. Yet, in fact, very little is known about how to measure legal complexity, much less how to monitor and manage it. Legal scholars have begun to employ the science of complex adaptive systems, also known as complexity science, to probe these kinds of descriptive and normative questions about the legal system. This body of work has focused primarily on developing theories of legal complexity and positing reasons for, and ways of, managing it. Legal scholars thus have skipped the hard part— developing quantitative metrics and methods for measuring and monitoring law’s complexity. But the theory of legal complexity will remain stuck in theory until it moves to the empirical phase of study. Thinking about ways of managing legal complexity is pointless if there is no yardstick for deciding how complex the law should be. In short, the theory of legal complexity cannot be put to work without more robust empirical tools for identifying and tracking complexity in legal systems. This Article explores legal complexity at a depth not previously undertaken in legal scholarship. -
A Philosophy of Technology for Computational Law
© Mireille Hildebrandt, draft chapter for OUP’s The Philosophical Foundations of Information Technology Law, eds. David Mangan, Catherine Easton, Daithí Mac Síthigh A philosophy of technology for computational law Abstract: This chapter confronts the foundational challenges posed to legal theory and legal philosophy by the surge of computational law. Two types of computational law are at stake. On the one hand we have artificial intelligence in the legal realm that will be addressed as data-driven law, and on the other hand we have the coding of self-executing contracts and regulation in the blockchain, as well as other types of automated decision making (ADM), addressed as code-driven law. Data-driven law raises problems due to its autonomic operations and the ensuing opacity of its reasoning. Code-driven law presents us with a conflation of regulation, execution and adjudication. Though such implications are very different, both types of computational law share assumptions based on the calculability and computability of legal practice and legal research. Facing the assumptions and implications of data- and code-driven law the chapter will first investigate the affordances of current, text-driven law, and relate some of the core tenets of the Rule of Law to those affordances. This will lead to an enquiry into what computational law affords in terms of legal protection, assuming that one of the core functions of the law and the Rule of Law is to protect what is not computable. Keywords: positive law, rule of law, legal certainty, justice, -
Use of Metods of Algebraic Programming for the Formal Verification of Legal Acts
UDC 004.2, 004.4 USE OF METODS OF ALGEBRAIC PROGRAMMING FOR THE FORMAL VERIFICATION OF LEGAL ACTS Volodymyr Peschanenko, Maksim Poltorackiy This article briefly describes the programmable tool for the analysis of a normative legal document. A mechanism for checking legal requirements is presented. The model of the legal document is proposed in the form of a set of special rules. Verification is provided by means of algebraic programming and methods of symbolic transformation. This approach allows us to analyze the legislative base of structural and logical errors, check the contradictions, completeness and integrity of legal acts. Presently, the mechanism of claster analysis of text, which makes it possible to identify the frequency of occurrence of various vague language constructs. Key words: law, algebraic programming, legal requirements, algebra of behavior. У цій статті коротко описується програмний інструмент для аналізу нормативно-правового документа. Існує механізм перевірки законних вимог. Модель правового документа представлена у вигляді набору спеціальних правил. Верифікація забезпечується за допомогою алгебраїчного програмування та методів символьного програмування. Цей підхід дозволяє проаналізувати законодавчу базу на наявність структурно логічних помилок, перевіряти правові вимоги на протиріччя, повноту та цілісність. Ключові слова: закон, алгебраїчне програмування, юридичні вимоги, алгебра поведінки. В этой статье кратко описывается программный инструмент для анализа нормативного правового документа. Представлен механизм -
Text and Data Mining in Intellectual Property Law: Towards an Autonomous Classification of Computational Legal Methods 1
TEXTAND TEXT AND DATA DATAMINIMINING IN INTELLECTUAL NGININTELPROPERTY LAW TOWARDS AN AUTONOMOUS CLASSIFICATION OF LECTUALPCOMPUTATIONAL LEGAL METHODS ROPERTYLCREATe Working Paper 2020/1 AWTOWATHOMAS MARGONI RDSANAU T Text and Data Mining in Intellectual Property Law: Towards an Autonomous Classification of Computational Legal Methods 1 Thomas Margoni2 I. Introduction Text and Data Mining (TDM) can generally be defined as the “process of deriving high-quality information from text and data,”3 and as a “tool for harnessing the power of structured and unstructured content and data, by analysing them at multiple levels and in several dimensions in order to discover hidden and new knowledge.”4 In other words, TDM refers to a set of automated analytical tools and methods that have the goal of extracting new, often hidden, knowledge from existing information, such as textual information (text mining) or structured and unstructured data (data mining), and on this basis annotate, index, classify and visualise such knowledge. All this, which is made possible by the fast advancements in computational power, internet speed, and data availability has the potential to constitute, if not a revolution in the scientific field, certainly a major advancement in the speed of scientific development as well as in its direction. In particular, the impact that TDM may have in the direction of scientific enquiry is invaluable. This is because by identifying the correlations and patterns that are often concealed to the eye of a human observer due to the amount, complexity, or variety of data surveyed, TDM allows for the discovery of concepts or the formulation of correlations that would have otherwise remained concealed or undiscovered. -
Building and Testing the SHYSTER-MYCIN Hybrid Legal Expert System
TR-CS-03-01 Building and Testing the SHYSTER-MYCIN Hybrid Legal Expert System Thomas A. O’Callaghan, James Popple and Eric McCreath May 2003 Joint Computer Science Technical Report Series Department of Computer Science Faculty of Engineering and Information Technology Computer Sciences Laboratory Research School of Information Sciences and Engineering This technical report series is published jointly by the Department of Computer Science, Faculty of Engineering and Information Technology, and the Computer Sciences Laboratory, Research School of Information Sciences and Engineering, The Australian National University. Please direct correspondence regarding this series to: Technical Reports Department of Computer Science Faculty of Engineering and Information Technology The Australian National University Canberra ACT 0200 Australia or send email to: [email protected] A list of technical reports, including some abstracts and copies of some full reports may be found at: http://cs.anu.edu.au/techreports/ Recent reports in this series: TR-CS-02-06 Stephen M Blackburn and Kathryn S McKinley. Fast garbage collection without a long wait. November 2002. TR-CS-02-05 Peter Christen and Tim Churches. Febrl - freely extensible biomedical record linkage. October 2002. TR-CS-02-04 John N. Zigman and Ramesh Sankaranarayana. dJVM - a distributed JVM on a cluster. September 2002. TR-CS-02-03 Adam Czezowski and Peter Christen. How fast is -fast? Performance analysis of KDD applications using hardware performance counters on UltraSPARC-III. September 2002. TR-CS-02-02 Bill Clarke, Adam Czezowski, and Peter Strazdins. Implementation aspects of a SPARC V9 complete machine simulator. February 2002. TR-CS-02-01 Peter Christen and Adam Czezowski. -
Judgments As Data Report
JUDGMENTS AS DATA AUTOMATED OPEN!ACCESS ANALYTICS FOR DECISIONS OF COURTS AND TRIBUNALS IN NEW ZEALAND December 2020 Tom Barraclough | Curtis Barnes | Warren Forster Produced with funding from the New Zealand Law Foundation Information Law and Policy Project. December 2020, Auckland, New Zealand Cover image credit: Malte Baumann, Unsplash. 1 PREAMBLE ............................................................................................................................................. 5 ACKNOWLEDGEMENTS .......................................................................................................................... 5 EXECUTIVE SUMMARY........................................................................................................................... 6 A NOTE ON “STRUCTURED DATA” ......................................................................................................... 8 OVERVIEW OPEN ACCESS TO DIGITAL CASE LAW IN NEW ZEALAND ...................................................................... 10 PREVIOUS ACCESS TO JUSTICE RESEARCH (2015) ........................................................................................... 10 PARTNERSHIP WITH OPENLAW NZ (2019) ................................................................................................. 11 ACCESS TO DIGITAL CASE LAW IN NEW ZEALAND ........................................................................................... 13 SUMMARY OF OUR FINDINGS .................................................................................................................. -
Bottleneck Or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts
Article Bottleneck or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts Amedeo Santosuosso * and Giulia Pinotti Research Center ECLT, University of Pavia, 27100 Pavia PV, Italy; [email protected] * Correspondence: [email protected] Received: 30 July 2020; Accepted: 6 September 2020; Published: 9 September 2020 Abstract: So far, in the application of legal analytics to legal sources, the substantive legal knowledge employed by computational models has had to be extracted manually from legal sources. This is the bottleneck, described in the literature. The paper is an exploration of this obstacle, with a focus on quantitative legal prediction. The authors review the most important studies about quantitative legal prediction published in recent years and systematize the issue by dividing them in text-based approaches, metadata-based approaches, and mixed approaches to prediction. Then, they focus on the main theoretical issues, such as the relationship between legal prediction and certainty of law, isomorphism, the interaction between textual sources, information, representation, and models. The metaphor of a crossroad shows a descriptive utility both for the aspects inside the bottleneck and, surprisingly, for the wider scenario. In order to have an impact on the legal profession, the test bench for legal quantitative prediction is the analysis of case law from the lower courts. Finally, the authors outline a possible development in the Artificial Intelligence (henceforth AI) applied to ordinary judicial activity, in general and especially in Italy, stressing the opportunity the huge amount of data accumulated before lower courts in the online trials offers. Keywords: legal sources; prediction; legal analytics 1. -
Science and Technology Law Review
The Columbia SCIENCE AND TECHNOLOGY LAW REVIEW www.stlr.org THE VARIABLE DETERMINACY THESIS Harry Surden1 This Article proposes a novel technique for characterizing the relative determinacy of legal decision-making. I begin with the observation that the determinacy of legal outcomes varies from context to context within the law. To augment this intuition, I develop a theoretical model of determinate legal decision-making. This model aims to capture the essential features that are typically associated with the concept of legal determinacy. I then argue that we can use such an idealized model as a standard for expressing the relative determinacy or indeterminacy of decision-making in actual, observed legal contexts. From a legal theory standpoint, this approach – separating determinacy and indeterminacy into their constituent conceptual elements – helps us to more rigorously define these theoretical ideas. Ultimately, from a practical standpoint, I assert that this framework assists in understanding why legal outcomes in certain contexts are determinate enough to be amenable to resolution by computers. 1 Associate Professor, University of Colorado Law School. B.A. Cornell University; J.D. Stanford University. Many thanks to Stanford Law School for supporting this work through my fellowship with the Stanford Center for Computers and Law, as well as to the generous support of the University of Colorado Law School. I am grateful for the ideas and challenges of Michael Genesereth from the Stanford Computer Science Department whose tireless efforts inspired this work. Many thanks to Paul Ohm, Phil Weiser, Pierre Schlag, Andrew Schwartz, Alexia Brunet, Vic Fleischer, and the rest of my excellent colleagues at the University of Colorado Law School for their input. -
The Devil in the Detail: Mitigating the Constitutional & Rule of Law Risks
Florida State University Law Review Volume 47 Issue 1 In Tribute to Talbot "Sandy" D'Alemberte Article 6 Fall 2019 The Devil in the Detail: Mitigating the Constitutional & Rule of Law Risks Associated with the Use of Artificial Intelligence in the Legal Domain Catrina Denvir Monash Business School, Monash University Tristan Fletcher Center for Artificial Intelligence, University College London Jonathan Hay Judge Business School, University of Cambridge Pascoe Pleasence Centre for Empirical Legal Studies, University College London Follow this and additional works at: https://ir.law.fsu.edu/lr Part of the Computer Law Commons, Constitutional Law Commons, and the Rule of Law Commons Recommended Citation Catrina Denvir, Tristan Fletcher, Jonathan Hay & Pascoe Pleasence, The Devil in the Detail: Mitigating the Constitutional & Rule of Law Risks Associated with the Use of Artificial Intelligence in the Legal Domain, 47 Fla. St. U. L. Rev. 29 (2019) . https://ir.law.fsu.edu/lr/vol47/iss1/6 This Article is brought to you for free and open access by Scholarship Repository. It has been accepted for inclusion in Florida State University Law Review by an authorized editor of Scholarship Repository. For more information, please contact [email protected]. THE DEVIL IN THE DETAIL: MITIGATING THE CONSTITUTIONAL & RULE OF LAW RISKS ASSOCIATED WITH THE USE OF ARTIFICIAL INTELLIGENCE IN THE LEGAL DOMAIN DR. CATRINA DENVIR* DR. TRISTAN FLETCHER** MR. JONATHAN HAY*** PROFESSOR PASCOE PLEASENCE**** ABSTRACT Over the last decade increased emphasis has been placed on the role that artificial intelligence (AI) will play in disrupting the practice of law. Although considerable attention has been given to the practical task of designing a computer to “think like a lawyer,” a number of related issues merit further inquiry. -
Legislation As Code for New Zealand: Opportunities, Risks, and Recommendations
LEGISLATION AS CODE FOR NEW ZEALAND: OPPORTUNITIES, RISKS, AND RECOMMENDATIONS March 2021 Tom Barraclough | Hamish Fraser | Curtis Barnes ACKNOWLEDGEMENTS This report was generously funded by Te Manatū a Ture o Aotearoa, the New Zealand Law Foundation. Our thanks to the Law Foundation Board and to NZLF Executive Director Lynda Hagen. We also extend our thanks to the NZLF Information Law and PoliCy ProjeCt Advisory Review Committee and the project manager of ILAPP, RiChman Wee. Thank you to the University of Otago FaCulty of Law and the New Zealand Law Foundation Centre for Law and Emerging Technologies for providing access to library resources during this research. Thank you to the various people who have engaged with us or provided support in the course of producing this report. They have not seen the final report and any errors are attributable only to the authors. Our thanks to Nadia Webster, RiChard WallaCe, Pim Willemstein, NiCk Vaughan, Mariette Lokin, Meng Wong, Matthew Waddington, LaurenCe Diver, Dave Parry, Roopak Sinha, Joanna Pidgeon and Tim Jones for discussions during the course of this research. Thank you also to Paul MiChel, NiCk Nisbet, Jason Morris and Siobhan McCarthy for their responses to a case study shared during the research. ProduCed with funding from the New Zealand Law Foundation Information Law and PoliCy ProjeCt. MarCh 2021, AuCkland, New Zealand EXECUTIVE SUMMARY OVERVIEW AND PURPOSE This report aims to provide a basis for senior deCision-makers in New Zealand to CritiCally assess and aCt upon the potential of law-as-code initiatives. It was stimulated by the growing attention to the “Better Rules” programme, a “better rules approaCh”, and international “rules as code” efforts. -
Judgments As Data Automated Open!Access Analytics for Decisions of Courts and Tribunals in New Zealand
JUDGMENTS AS DATA AUTOMATED OPEN!ACCESS ANALYTICS FOR DECISIONS OF COURTS AND TRIBUNALS IN NEW ZEALAND December 2020 Tom Barraclough | Curtis Barnes | Warren Forster Produced with funding from the New Zealand Law Foundation Information Law and Policy Project. December 2020, Auckland, New Zealand Cover image credit: Malte Baumann, Unsplash. 1 PREAMBLE ............................................................................................................................................. 5 ACKNOWLEDGEMENTS .......................................................................................................................... 5 EXECUTIVE SUMMARY........................................................................................................................... 6 A NOTE ON “STRUCTURED DATA” ......................................................................................................... 8 OVERVIEW OPEN ACCESS TO DIGITAL CASE LAW IN NEW ZEALAND ...................................................................... 10 PREVIOUS ACCESS TO JUSTICE RESEARCH (2015) ........................................................................................... 10 PARTNERSHIP WITH OPENLAW NZ (2019) ................................................................................................. 11 ACCESS TO DIGITAL CASE LAW IN NEW ZEALAND ........................................................................................... 13 SUMMARY OF OUR FINDINGS ..................................................................................................................