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FinTech\u27s Double Edges
Get PDFThis symposium essay examines the double-edged nature of financial technologies in financial transactions, especially transactions involving consumers. There are both benefits and risks—often undiscovered or hidden at first—in each new round of financial technologies. A FinTech tool may benefit consumers and then, applied later or in a different context, threaten consumer interests; a tool that harms consumer interests may then lead to development of a tool that favors them. This double-edged nature is an important but unappreciated structural feature of financial technologies. From the perspective of consumer protection, then, FinTech can neither be fully embraced as friend nor restricted as foe. Rather, it must be regulated with sensitivity to various competing goals: fostering innovation, policing abuse, and protecting access to markets, to financial services, and to the legal system. This essay cautiously endorses several strategies: the use of purposive and compliance-driven regulatory frameworks; regulatory “sandboxes” and other experimentalist and stakeholder-participatory approaches to FinTech governance; and the development of consumer-protective and consumer-enabling FinTech. It also calls attention the issues of distributive justice and equity that arise when there are prohibitive financial or cognitive barriers to effective use of FinTech; in other words, it calls attention to the fact that access to fair participation in the markets and access to justice may increasingly rely on access to FinTech
European Parliament Resolution of 9 July 2015 and Its Progeny: Why the Digital Age Demands a Single European Copyright Title
Get PDFIP Neutrality and Benefit Sharing for Seasonal Flu: An Argument In Favor of WHO PIP Framework Expansion
Get PDFCurrently, countries that share samples of influenza viruses with a global WHO network called GISRS can participate in IP and benefitsharing agreements over their samples only if those samples are considered potential pandemic triggers. Some key players in public health want to change that by extending those protections to seasonal flu viruses. Others argue that doing so will be problematic, by, for example, creating too much red tape for vaccine research and development or by destroying the progress that has already been made in creating GISRS. In this battle between WHO stakeholders, expanding the scope of IP and benefits agreements to seasonal flu virus-donating countries will satisfy both parties in the long term and save lives
Parallel Novels and the Reimagining of Literary Notables by Follow-On Authors: Copyrights Issues When Characters Are First Created by Others
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Patent Exhaustion Connects Common Law to Equity: \u3cem\u3eImpression Products, Inc. v. Lexmark International, Inc.\u3c/em\u3e
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Crediting the Incredible: How the Seventh Circuit Uses Procedure to Mask Its Improper Perfunctory Grant of Deference to Chicago’s Law Enforcement Officers
Get PDFIn January 2017, the Department of Justice released a Report after investigating the Chicago Police Department and its in-house accountability agencies tasked with detecting and deterring police misconduct, concluding that there is reasonable cause to believe that the CPD routinely engages in unlawful patterns and practices in violation of the Fourth Amendment. Moreover, the DOJ found that attempts by the CPD’s agencies to hold officers accountable for misconduct have been frustrated by the “code of silence” and “pervasive cover-up culture” among CPD officers. As a result, the burden of deterring police misconduct has effectively fallen on the victims themselves. Yet the primary tools at those victims’ disposal, 42 U.S.C. §1983 and the Illinois common law intentional tort of malicious prosecution, have yet to translate into an effective system for detecting and deterring misconduct
A General Approach for Predicting the Behavior of the Supreme Court of the United States
No full textBuilding on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications