This research handbook represents a scholarly, state_of-the-art overview of research and the scope of current thinking in the field of big data analytics and the law. It is for scholars, practitioners, and students from a variety of related disciplines who wish to survey the issues surrounding big data analytics in legal settings, as well as legal issues surrounding the application of big data techniques in different domains. This book introduction addresses the question, “What is Big Data Law?”, and explains the structure of the book for easy navigation, providing short overviews of the 25 chapters, authored by recognized international big data law experts, that make up this compendium.
Sharad Goel, Ravi Shroff, Jennifer Skeem and Christopher Slobogin
The increasing use of risk assessment instruments in the criminal justice system has given rise to several criticisms. The instruments are said to be no more accurate than clinical assessments, racially biased, lacking in transparency and, because of their quantitative nature, dehumanizing. This chapter critically examines a number of these concerns. It also highlights how the law has, and should, respond to these issues.
The development and adoption of biometric identification, specifically facial recognition systems, have exponentially increased in the last few years. Use cases include mobile device security, building entry systems, and tools in criminal investigations. The vast array of deployment happens both domestically and internationally, as well as in the private and public sectors. This chapter examines the technical and logistical components; recent developments and applications; and the unique legal, ethical, and regulatory concerns associated with the use of this technology. As the use of biometric systems rises, we must be proactive in engaging with these issues in order to capture the optimal societal benefit, while reducing the risk of potential harms.
David Freeman Engstrom and Daniel E. Ho
We offer a synthetic review of the distinct governance challenges raised by public sector adoption of AI. We illustrate how a new wave of AI technology is exhibiting early signs of transforming how government works, raising distinct governance challenges. First, public sector AI will grapple not only with widely covered constitutional law issues, but also with administrative law’s unique demands for transparency and explainability. Second, embedded technical expertise will be critical for narrowing the public-private sector technology gap and furthering “internal” due process. Third, we spell out the challenges of gameability, distributive effects, and legitimacy as the new AI-based governance technologies move closer to performing core government functions. We argue that the next generation of work will require more sustained attention to (a) the legal and institutional context and (b) the technological viability of use cases.
This chapter examines big data research methods as applied to copyright law. The formality free principle makes it difficult to maintain an authoritative database of copyright works, and complicates research into such works. Notwithstanding recent developments in the United States, traditional collective management organizations still face difficulties in identifying and licensing rightholders’ works. While new copyright management systems based around the automated detection of infringement provide rightholders with more monetisation options, these systems are opaque to research as they operate at the prerogative of the platform service providers. These limitations may however be overcome with careful application of big data research methods; some case studies involving previous researches using such methods follow. This chapter concludes with a review of research into defenses and fair use considerations in the online environment, and the impact of copyright filter systems proposed under the European Union's Directive on Copyright in the Digital Single Market.
Przemysław Pałka and Marco Lippi
Benjamin Alarie, Anthony Niblett and Albert Yoon
In this chapter we discuss how big data analytics and machine learning tools are being used to gain new and actionable insights in tax law. It proceeds in two parts. First, we discuss how big data analytics can help tax agencies and regulators (such as the IRS) better administer tax law. We argue that predictive analytics can be used by tax authorities to optimally allocate scarce resources and illustrate how enforcement efforts can be targeted with greater precision. We also take a broader approach and look at the insights that might be used by governments more generally to improve the content of tax policy. Second, we look at the insights that can help taxpayers in understanding tax law. Our focus, here, is on how taxpayers can use data analytics to more accurately determine their tax liability, especially in areas where the law is vague and unclear.
The National Commission of Supervision of P.R. China has expanded the scope of anti-corruption targets, paid more attention on preventing corruption rather than taking measures afterwards, and applied cutting-edge big data technologies. China has built anti-corruption experience with big data, from human-based anti-corruption efforts to primarily machine-based approaches; from reactive to predictive anti-corruption efforts; and from passive to active discovery of indicia of corruption. Chinese authorities have also developed effective ways to approach tough issues, involving questions around reliability of data sources, design of algorithms and legal analysis of the output of big data processes deployed in anti-corruption efforts. While China’s anti-corruption campaign has been effective, legal challenges have been launched against some of these efforts based on protection of personal information, the quality and reliability of data and algorithms, and due process. These challenges will also be examined.
Machine learning is an artificial intelligence (AI) approach that is widely used today for automation and prediction. This chapter explores machine learning’s nascent use within the legal domain. The first section highlights the central principles of machine learning. The subsequent discussion considers the relationship between machine learning and law. Machine learning operates by identifying patterns in data, and many aspects of the law can be viewed in terms of data. Such data-oriented features of the law are amenable to machine learning methods. This chapter then explores uses of machine learning technology in law, while recognizing both the technology’s capabilities and limits. It concludes by surveying some contemporary social controversies involving the use of machine learning in the legal context.