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.
Contents:
Introduction to the Research Handbook on Big Data Law 1
Roland Vogl
1 The accuracy, equity, and jurisprudence of criminal risk assessment 9
Sharad Goel, Ravi Shroff, Jennifer Skeem and Christopher Slobogin
2 The many faces of facial recognition 29
Stephen Caines
3 Artificially intelligent government: A review and agenda 57
David Freeman Engstrom and Daniel E. Ho
4 Big data and copyright law 87
Daniel Seng
5 Big data analytics, online terms of service and privacy policies 115
Przemysław Pałka and Marco Lippi
6 Data analytics and tax law 135
Benjamin Alarie, Anthony Niblett and Albert Yoon
7 Experience of big data anti-corruption in China 150
Ran Wang
8 Machine learning and law: An overview 171
Harry Surden
9 SCOTUS outcome prediction: A new machine learning approach 185
Ashkon Farhangi and Ajay Sohmshetty
10 Legal information retrieval 198
Ashraf Bah Rabiou
11 LexNLP: Natural language processing and information extraction for
legal and regulatory texts 216
Michael J. Bommarito II, Daniel Martin Katz and Eric M. Detterman
12 Quantitative legal research in Germany 228
Dirk Hartung
13 Big data analytics for e-discovery 252
Johannes C. Scholtes and Hendrik Jacob van den Herik
14 Generalizability: Machine learning and humans-in-the-loop 284
John Nay and Katherine J. Strandburg
15 The VICTOR Project: Applying artificial intelligence to Brazil’s
Supreme Federal Court 303
Ricardo Vieira de Carvalho Fernandes, Danilo Barros Mendes, Gustavo
Henrique T.A. Carvalho and Hugo Honda Ferreira
16 Explainable artificial intelligence 317
Mary-Anne Williams
17 Explainability and transparency of machine learning in ADM systems 340
Bernhard Waltl
18 Certifying artificial intelligence systems 356
Florian Möslein and Roberto V. Zicari
19 Rules, cases and arguments in artificial intelligence and law 373
Heng Zheng and Bart Verheij
20 Artificial intelligence and the zealous litigator 388
James Yoon
21 Evaluating legal services: The need for a quality movement and
standard measures of quality and value 403
Daniel W. Linna Jr.
22 Machine learning and EU data-sharing practices: Legal aspects of
machine learning training datasets for AI systems 431
Mauritz Kop
23 AI-driven contract review: A product development journey 453
Shlomit Labin and Uri Segal
24 Practical guide to artificial intelligence and contract review 466
Andrew Antos and Nischal Nadhamuni
25 Legal marketplaces using machine learning techniques 481
Verónica Sorin and Martí Manent
Index
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