LAW, NORMS & INFORMAL ORDER eJOURNAL
"Innovation's Hidden Externalities"
Brigham Young University Law Review, Forthcoming
BYU Law Research Paper No. 21-09
STEPHANIE PLAMONDON BAIR, Brigham Young University J. Reuben Clark Law School
When commentators discuss innovation’s externalities, they often classify them into one of two categories. On the positive externalities, or “spillovers” side, legal and economics scholars often speak of the benefits innovation confers on other innovators. Future innovators profit from past innovation as they “stand on the shoulders of giants” to develop progressively new and better innovation. Discussion of innovation’s negative externalities, on the other hand, has mainly focused on social harms not directly related to future innovation that particular advances impose on third parties—the classic example being pollution. Thus, the common understanding is that innovation’s spillovers positively impact innovation (among other things), while innovation’s negative externalities are only indirectly related to society’s collective capacity for further innovation, if at all.
This Article challenges that view, arguing that innovation does impose negative externalities on contemporary and future innovators, thereby making it more difficult for them to innovate. It discusses three mechanisms by which these negative externalities arise. The first is through path dependencies. Path dependencies in innovation can limit the innovative potential of other innovators by effectively foreclosing particular areas of study or by directing innovation along less productive paths. A second mechanism by which innovation imposes negative externalities on other innovators is through the workings of social norms. Social norms that become entrenched in innovative communities can lead innovators to adopt sub-optimal research agendas and methodologies. Third, particular innovations may work on those who adopt them at a psychological level, changing their cognition and thought processes in ways that negatively impact their future ability to innovate.
Uncovering innovation’s hidden externalities has profound implications for discussions of innovation policy. Currently, the conventional wisdom holds that innovation’s spillovers should be addressed through innovation subsidies, while innovation’s negative externalities can be addressed by taxing the externalities directly. Recognizing that innovation has both positive and negative externalities for contemporary and future innovators, however, challenges the view that the conversation about innovation subsidies (like intellectual property, tax breaks, grants, and prizes) should concern itself only with innovation’s spillovers, and not with its negative externalities.
"Compliance Ethnography: What Gets Lost in Compliance Measurement"
Van Rooij, Benjamin, Yunmei Wu, and Li Na. "Compliance Ethnography: What Gets Lost in Compliance Measurement." In Measuring Compliance: Assessing Corporate Crime and Misconduct Prevention, edited by Melissa Rorie and Benjamin Van Rooij. Cambridge, UK: Cambridge University Press, 2021 (in production)
UC Irvine School of Law Research Paper No. 2021-29
BENJAMIN VAN ROOIJ, University of California, Irvine School of Law, University of Amsterdam - Faculty of Law
YUNMEI WU, Yunnan Academy of Social Sciences (YASS), University of Amsterdam - Faculty of Law
LI NA, University of Amsterdam, Netherlands China Law Centre
Drawing on data from two ethnographies on organizational compliance in China, this chapter offers three important insights about what gets lost in traditional quantitative measures of organizational compliance. First, the studies show that compliance is muddled. A close-up view of the actual business responses to the law are hard to capture in binary or numerical terms (or even in more nuanced labeling such as motivational postures or levels of commitment); in everyday practice there are many instances of both rule obeying and rule violating behavior. Second, compliance is dynamic and varies at different points in time and in their situational contexts. Third, the studies show that compliance can be a non-linear process in which compliance occurs even when there is no chain of transmission from governmental regulators to compliance managers to individual workers. The chapter draws out what these insights mean for the study and practice of compliance measurement. Ultimately, there is a strong need for multi-method research that combines understanding complexity through in-depth case studies (combining participant observation with interviews) alongside statistical analysis in quantitative work.
"'The Court of the Conqueror': Colonialism, the Constitution, and the Time of Redemption"
Law’s Infamy: Understanding the Canon of Bad Law (eds. Austin Sarat, Lawrence Douglas, and Martha M. Umphrey) (NYU Press 2021).
SHERALLY MUNSHI, Georgetown University Law Center
Within the constitutional imaginary, colonialism is often represented as a regrettable prehistory to the founding of the nation, an event that conditioned the founding of the revolutionary republic but is not constitutive of it. Moreover, the constitution itself is invested with the faith that the United States will slowly but eventually overcome its founding sins. But if the arc of constitutional redemption is defined by the eventual repudiation of infamous race cases—Dred Scott v. Sanford and Korematsu v. United States—then a case like Johnson v. M’Intosh is revealing of the ways in which law continues to place colonial power beyond the scope of constitutional review, historical redress, and national progress. In Johnson, the Supreme Court recognized that the federal government had the extraordinary power to assert its authority over indigenous peoples, a power which “the Courts of the Conqueror cannot deny.” That extra-constitutional power, inherent to sovereignty, has since been formalized in the plenary power doctrine, according to which the federal government continues to assert unilateral authority over matters involving Indians, immigrants, and peoples in the United States’ overseas colonies. As such Johnson and its unfolding legacy represent the stillness of sovereignty, a colonial power that stands beyond the time of redemption.
"Observing the Effects of Automating the Judicial System with Behavioral Equivalence"
South Carolina Law Review, Vol. 72, No. 4, 2022
JOSEPH BLASS, Northwestern University Pritzker School of Law, Northwestern University - Dept. Electrical Engineering & Computer Science
Building on decades of work in Artificial Intelligence, legal scholars have begun to consider whether components of the judicial system could be replaced by computers. Much of the scholarship in AI and Law has focused on whether such automated systems could reproduce the reasoning and outcomes produced by the current system. This scholarly framing captures many aspects of judicial processes, but overlooks how automated judicial decision-making likely would change how participants in the legal system interact with it, and how societal interests outside that system who care about its processes would be affected by those changes.
This Article demonstrates how scholarship on legal automation comes to leave out perspectives external to the process of judicial decision-making. It analyses the problem using behavioral equivalence, a Computer Science concept that assesses systems’ behaviors according to the observations of specific monitors of those systems. It introduces a framework to examine the various observers of the judicial process and the tradeoffs they may perceive when legal systems are automated. This framework will help scholars and policymakers more effectively anticipate the consequences of automating components of the legal system.
"A Global Perspective of Soft Law Programs for the Governance of Artificial Intelligence"
CARLOS IGNACIO GUTIERREZ, Arizona State University (ASU) - Sandra Day O'Connor College of Law
GARY E. MARCHANT, Arizona State University - College of Law
Soft law is defined as a program that sets substantive expectations, but is not directly enforceable by government. Because soft law is not bound by a geographic jurisdiction and can be developed, amended, and adopted by any entity, it will be the dominant form of artificial intelligence (AI) governance for the foreseeable future. The objective of this document is to compile and analyze global trends on how this governance tool is used by government, non-profits, and the private sector to manage AI’s methods and applications.
Inspired by similar efforts, this document contains a scoping review of AI soft law programs. Our process was divided into three steps: identification, screening, and classification. Our identification of programs began by establishing eligibility criteria. All programs had to: 1) conform to the definition of soft law, 2) emphasize the governance or management of a method or application of AI, and 3) were published by December 31st, 2019. These criteria made it possible to detect relevant programs through one of three methods that were implemented in a parallel manner. We found and mined over 80 linkhubs, resources that aggregate programs. We performed 370 keyword searches that combined our soft law program typology with a diverse list of themes, applications, and methods related to AI. Lastly, every screened-in program was vetted to search for references to other relevant programs, efforts such as these are denominated citation chaining.
In the screening process, we verified each program’s compliance to the project’s eligibility criteria. Out of the 1,599 programs initially identified, 965 were excluded because they were deemed to be articles or documents without a soft law component, unrelated to AI, or published after our 2019 cut-off. The final step of the process involved classifying the programs. Through several pilot exercises and by adopting best practices from relevant research, we developed 107 variables and themes to describe the programs. Variables provide information on how it is organized, functions, and its general characteristics, while themes communicate the subject matter discussed within a program’s text.
Overall, we identified 634 soft law AI programs. Through our variables and themes, we were able to gather insights from this database. For one, the governance of this technology through soft law is a relatively new endeavor. Despite finding programs from the year 2001, over 90% of those in our sample were published between 2016-2019. Geographically, there appears to be limited diversity. The vast majority originate in countries classified as high income within Europe and North America. In the development of these tools, organizations appear to overwhelmingly prefer programs geared towards influencing the behavior of internal and external stakeholders, as opposed to those limited to internal stakeholders. At the same time, less than a third publicly mention enforcement or implementation tools meant to compel compliance with soft law program.
Despite having seven categories for classifying the type of soft law program, about 80% were labeled as principles or recommendations/strategy. This includes a list of 158 principles, one of the largest compilations dedicat