Research Handbook on Big Data Law

Description

This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains. Featuring contributions from a variety of expert scholars, this is an interdisciplinary dialogue addressing big data analytics, tools and techniques and the societal impact of the field. Chapters analyze both cases anchored in a particular legal system (such as anti-corruption in China) and big data law approaches relevant across multiple practice areas: including machine learning within law, legal information retrieval, natural language processing and e-discovery. It also offers original insights from industry project reports that use big data law techniques in interesting, new ways. Providing a unique and interdisciplinary blend of analysis, this Research Handbook will be a key resource for legal scholars and students researching in areas such as criminal, tax, copyright and administrative law. It will also prove useful for practicing lawyers wanting to get a sense of the legal practice of the future, as well as law-makers thinking about the use of big data law techniques in government policy. Dedication Contents List of contributors Acknowledgments Introduction to the Research Handbook on Big Data Law • Roland Vogl 1 The accuracy, equity, and jurisprudence of criminal risk assessment • Sharad Goel, Ravi Shroff, Jennifer Skeem and Christopher Slobogin 2 The many faces of facial recognition • Stephen Caines 3 Artificially intelligent government: A review and agenda • David Freeman Engstrom and Daniel E. Ho 4 Big data and copyright law • Daniel Seng 5 Big data analytics, online terms of service and privacy policies • Przemysław Pałka and Marco Lippi 6 Data analytics and tax law • Benjamin Alarie, Anthony Niblett and Albert Yoon 7 Experience of big data anti-corruption in China • Ran Wang 8 Machine learning and law: An overview • Harry Surden 9 SCOTUS outcome prediction: A new machine learning approach • Ashkon Farhangi and Ajay Sohmshetty 10 Legal information retrieval • Ashraf Bah Rabiou 11 LexNLP: Natural language processing and information extraction for legal and regulatory texts • Michael J. Bommarito II, Daniel Martin Katz and Eric M. Detterman 12 Quantitative legal research in Germany • Dirk Hartung 13 Big data analytics for e-discovery • Johannes C. Scholtes and Hendrik Jacob van den Herik 14 Generalizability: Machine learning and humans-in-the-loop • John Nay and Katherine J. Strandburg 15 The VICTOR Project: Applying artificial intelligence to Brazil’s Supreme Federal Court • Ricardo Vieira de Carvalho Fernandes, Danilo Barros Mendes, Gustavo Henrique T.A. Carvalho and Hugo Honda Ferreira 16 Explainable artificial intelligence • Mary-Anne Williams 17 Explainability and transparency of machine learning in ADM systems • Bernhard Waltl 18 Certifying artificial intelligence systems • Florian Möslein and Roberto V. Zicari 19 Rules, cases and arguments in artificial intelligence and law • Heng Zheng and Bart Verheij 20 Artificial intelligence and the zealous litigator • James Yoon 21 Evaluating legal services: The need for a quality movement and standard measures of quality and value • Daniel W. Linna Jr. 22 Machine learning and EU data-sharing practices: Legal aspects of machine learning training datasets for AI systems • Mauritz Kop 23 AI-driven contract review: A product development journey • Shlomit Labin and Uri Segal 24 Practical guide to artificial intelligence and contract review • Andrew Antos and Nischal Nadhamuni 25 Legal marketplaces using machine learning techniques • Verónica Sorin and Martí Manent Index