Open Access. Powered by Scholars. Published by Universities.®
- Publication
Articles 1 - 3 of 3
Full-Text Articles in Law
Regulating New Tech: Problems, Pathways, And People, Cary Coglianese
Regulating New Tech: Problems, Pathways, And People, Cary Coglianese
All Faculty Scholarship
New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. …
Deterring Algorithmic Manipulation, Gina-Gail S. Fletcher
Deterring Algorithmic Manipulation, Gina-Gail S. Fletcher
Faculty Scholarship
Does the existing anti-manipulation framework effectively deter algorithmic manipulation? With the dual increase of algorithmic trading and the occurrence of “mini-flash crashes” in the market linked to manipulation, this question has become more pressing in recent years. In the past thirty years, the financial markets have undergone a sea change as technological advancements and innovations have fundamentally altered the structure and operation of the markets. Key to this change is the introduction and dominance of trading algorithms. Whereas initial algorithmic trading relied on preset electronic instructions to execute trading strategies, new technology is introducing artificially intelligent (“AI”) trading algorithms that …
Deploying Machine Learning For A Sustainable Future, Cary Coglianese
Deploying Machine Learning For A Sustainable Future, Cary Coglianese
All Faculty Scholarship
To meet the environmental challenges of a warming planet and an increasingly complex, high tech economy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that …