Science and Technology Law Commons^™

Privacy In Pandemic: Law, Technology, And Public Health In The Covid-19 Crisis, Tiffany Li

Faculty Scholarship

The COVID-19 pandemic has caused millions of deaths and disastrous consequences around the world, with lasting repercussions for every field of law, including privacy and technology. The unique characteristics of this pandemic have precipitated an increase in use of new technologies, including remote communications platforms, healthcare robots, and medical AI. Public and private actors are using new technologies, like heat sensing, and technologically-influenced programs, like contact tracing, alike in response, leading to a rise in government and corporate surveillance in sectors like healthcare, employment, education, and commerce. Advocates have raised the alarm for privacy and civil liberties violations, but the …

Is The Future Of Law A Driverless Car? Assessing How The Data Analytics Revolution Will Transform Legal Practice, Eric L. Talley

Faculty Scholarship

Machine learning and artificial intelligence technologies (“data analytics”) are quickly transforming research and practice in law, raising questions of whether the law can survive as a vibrant profession for natural persons to enter. In this article, I argue that data analytics approaches are overwhelmingly likely to continue to penetrate law, even in domains that have heretofore been dominated by human decision makers. As a vehicle for demonstrating this claim, I describe an extended example of using machine learning to identify and categorize fiduciary duty waiver provisions in publicly disclosed corporate documents. Notwithstanding the power of machine learning techniques, however, I …

A Machine Learning Classifier For Corporate Opportunity Waivers, Gabriel V. Rauterberg, Eric L. Talley

Faculty Scholarship

Rauterberg & Talley (2017) develop a data set of “corporate opportunity waivers” (COWs) – significant contractual modifications of fiduciary duties – sampled from SEC filings. Part of their analysis utilizes a machine learning (ML) classifier to extend their data set beyond the hand-coded sample. Because the ML approach is likely unfamiliar to some readers, and in the light of its great potential across other areas of law and finance research, this note explains the basic components using a simple example, and it demonstrates strategies for calibrating and evaluating the classifier.

The Scored Society: Due Process For Automated Predictions, Danielle K. Citron

Faculty Scholarship

Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers — or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail — credit — the law focuses on credit history, not the derivation of scores from data.

Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The …

Digital Security In The Expository Society: Spectacle, Surveillance, And Exhibition In The Neoliberal Age Of Big Data, Bernard E. Harcourt

Faculty Scholarship

In 1827, Nicolaus Heinrich Julius, a professor at the University of Berlin, identified an important architectural mutation in nineteenth-century society that reflected a deep disruption in our technologies of knowledge and a profound transformation in relations of power across society: Antiquity, Julius observed, had discovered the architectural form of the spectacle; but modern times had operated a fundamental shift from spectacle to surveillance. Michel Foucault would elaborate this insight in his 1973 Collège de France lectures on The Punitive Society, where he would declare: “[T]his is precisely what happens in the modern era: the reversal of the spectacle into surveillance…. …