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Research Handbook on Big Data Law
Vogl, Roland. Editor.; B. Alarie, A. Antos, M.J. Bommarito II, S. Caines, G.H.T.A. Carvalho, R.V. de Carvalho Fernandes, E.M. Detterman, D.F. Engstrom, A. Farhangi, H.H. Ferreira, S. Goel, D. Hartung, D.E. Ho, D.M. Katz, M. Kop, S. Labin, D.W. Linna Jr., M. Lippi, M. Manent, D.B. Mendes, F. Möslein, N. Nadhamuni, J. Nay, A. Niblett, P. Pałka, A.B. Rabiou, J.C. Scholtes, U. Segal, D. Seng, R. Shroff, J. Skeem, C. Slobogin, A. Sohmshetty, V. Sorin, K.J. Strandburg, H. Surden, H.J. van den Herik, B. Verheij, R. Vogl, B. Waltl, R. Wang, M.-A. Williams, A. Yoon, J. Yoon, H. Zheng, R.V. Zicari. Contributors.
2021
Details
Title
Research Handbook on Big Data Law
Author
Vogl, Roland. Editor.
B. Alarie, A. Antos, M.J. Bommarito II, S. Caines, G.H.T.A. Carvalho, R.V. de Carvalho Fernandes, E.M. Detterman, D.F. Engstrom, A. Farhangi, H.H. Ferreira, S. Goel, D. Hartung, D.E. Ho, D.M. Katz, M. Kop, S. Labin, D.W. Linna Jr., M. Lippi, M. Manent, D.B. Mendes, F. Möslein, N. Nadhamuni, J. Nay, A. Niblett, P. Pałka, A.B. Rabiou, J.C. Scholtes, U. Segal, D. Seng, R. Shroff, J. Skeem, C. Slobogin, A. Sohmshetty, V. Sorin, K.J. Strandburg, H. Surden, H.J. van den Herik, B. Verheij, R. Vogl, B. Waltl, R. Wang, M.-A. Williams, A. Yoon, J. Yoon, H. Zheng, R.V. Zicari. Contributors.
B. Alarie, A. Antos, M.J. Bommarito II, S. Caines, G.H.T.A. Carvalho, R.V. de Carvalho Fernandes, E.M. Detterman, D.F. Engstrom, A. Farhangi, H.H. Ferreira, S. Goel, D. Hartung, D.E. Ho, D.M. Katz, M. Kop, S. Labin, D.W. Linna Jr., M. Lippi, M. Manent, D.B. Mendes, F. Möslein, N. Nadhamuni, J. Nay, A. Niblett, P. Pałka, A.B. Rabiou, J.C. Scholtes, U. Segal, D. Seng, R. Shroff, J. Skeem, C. Slobogin, A. Sohmshetty, V. Sorin, K.J. Strandburg, H. Surden, H.J. van den Herik, B. Verheij, R. Vogl, B. Waltl, R. Wang, M.-A. Williams, A. Yoon, J. Yoon, H. Zheng, R.V. Zicari. Contributors.
Item Type
Book
Description
544 pages.
ISBN
9781788972826 eBook
9781788972819 Print
9781788972819 Print
Summary
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. Provided by publisher.
Note
Includes index.
Formatted Contents Note
Introduction to the Research Handbook on Big Data Law
1. The accuracy, equity, and jurisprudence of criminal risk assessment
2. The many faces of facial recognition
3. Artificially intelligent government: A review and agenda
4. Big data and copyright law
5. Big data analytics, online terms of service and privacy policies
6. Data analytics and tax law
7. Experience of big data anti-corruption in China
8. Machine learning and law: An overview
9. SCOTUS outcome prediction: A new machine learning approach
10. Legal information retrieval
11. LexNLP: Natural language processing and information extraction for legal and regulatory texts
12. Quantitative legal research in Germany
13. Big data analytics for e-discovery
14. Generalizability: Machine learning and humans-in-the-loop
15. The VICTOR Project: Applying artificial intelligence to Brazil’s Supreme Federal Court
16. Explainable artificial intelligence
17. Explainability and transparency of machine learning in ADM systems
18. Certifying artificial intelligence systems
19. Rules, cases and arguments in artificial intelligence and law
20. Artificial intelligence and the zealous litigator
21. Evaluating legal services: The need for a quality movement and standard measures of quality and value
22. Machine learning and EU data-sharing practices: Legal aspects of machine learning training datasets for AI systems
23. AI-driven contract review: A product development journey
24. Practical guide to artificial intelligence and contract review
25. Legal marketplaces using machine learning techniques
Index
1. The accuracy, equity, and jurisprudence of criminal risk assessment
2. The many faces of facial recognition
3. Artificially intelligent government: A review and agenda
4. Big data and copyright law
5. Big data analytics, online terms of service and privacy policies
6. Data analytics and tax law
7. Experience of big data anti-corruption in China
8. Machine learning and law: An overview
9. SCOTUS outcome prediction: A new machine learning approach
10. Legal information retrieval
11. LexNLP: Natural language processing and information extraction for legal and regulatory texts
12. Quantitative legal research in Germany
13. Big data analytics for e-discovery
14. Generalizability: Machine learning and humans-in-the-loop
15. The VICTOR Project: Applying artificial intelligence to Brazil’s Supreme Federal Court
16. Explainable artificial intelligence
17. Explainability and transparency of machine learning in ADM systems
18. Certifying artificial intelligence systems
19. Rules, cases and arguments in artificial intelligence and law
20. Artificial intelligence and the zealous litigator
21. Evaluating legal services: The need for a quality movement and standard measures of quality and value
22. Machine learning and EU data-sharing practices: Legal aspects of machine learning training datasets for AI systems
23. AI-driven contract review: A product development journey
24. Practical guide to artificial intelligence and contract review
25. Legal marketplaces using machine learning techniques
Index
Source of Description
Description based on print record.
Linked Resources
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Published
[Northampton, England] : Edward Elgar Publishing Ltd., 2021.
Language
English
Record Appears in
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