Law Commons^™

Interview On The Black Box Society, Lawrence Joseph, Frank A. Pasquale

Faculty Scholarship

Hidden algorithms drive decisions at major Silicon Valley and Wall Street firms. Thanks to automation, those firms can approve credit, rank websites, and make myriad other decisions instantaneously. But what are the costs of their methods? And what exactly are they doing with their digital profiles of us?

Leaks, whistleblowers, and legal disputes have shed new light on corporate surveillance and the automated judgments it enables. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only …

When Enough Is Enough: Location Tracking, Mosaic Theory, And Machine Learning, Steven M. Bellovin, Renée M. Hutchins, Tony Jebara, Sebastian Zimmeck

Faculty Scholarship

Since 1967, when it decided Katz v. United States, the Supreme Court has tied the right to be free of unwanted government scrutiny to the concept of reasonable xpectations of privacy.^[1] An evaluation of reasonable expectations depends, among other factors, upon an assessment of the intrusiveness of government action. When making such assessment historically the Court has considered police conduct with clear temporal, geographic, or substantive limits. However, in an era where new technologies permit the storage and compilation of vast amounts of personal data, things are becoming more complicated. A school of thought known as “mosaic theory” …

Big Data's Other Privacy Problem, James Grimmelmann

Faculty Scholarship

Big Data has not one privacy problem, but two. We are accustomed to talking about surveillance of data subjects. But Big Data also enables disconcertingly close surveillance of its users. The questions we ask of Big Data can be intensely revealing, but, paradoxically, protecting subjects' privacy can require spying on users. Big Data is an ideology of technology, used to justify the centralization of information and power in data barons, pushing both subjects and users into a kind of feudal subordination. This short and polemical essay uses the Bloomberg Terminal scandal as a window to illuminate Big Data's other privacy …

The Scored Society: Due Process For Automated Predictions, Danielle Keats Citron, Frank A. Pasquale

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 American due process tradition should inform …

Promoting Innovation While Preventing Discrimination: Policy Goals For The Scored Society, Frank A. Pasquale, Danielle Keats Citron

Faculty Scholarship

There are several normative theories of jurisprudence supporting our critique of the scored society, which complement the social theory and political economy presented in our 2014 article on that topic in the Washington Law Review. This response to Professor Tal Zarsky clarifies our antidiscrimination argument while showing that is only one of many bases for the critique of scoring practices. The concerns raised by Big Data may exceed the capacity of extant legal doctrines. Addressing the potential injustice may require the hard work of legal reform.

Protecting Health Privacy In An Era Of Big Data Processing And Cloud Computing, Frank A. Pasquale, Tara Adams Ragone

Faculty Scholarship

This Article examines how new technologies generate privacy challenges for both healthcare providers and patients, and how American health privacy laws may be interpreted or amended to address these challenges. Given the current implementation of Meaningful Use rules for health information technology and the Omnibus HIPAA Rule in health care generally, the stage is now set for a distinctive law of “health information” to emerge. HIPAA has come of age of late, with more aggressive enforcement efforts targeting wayward healthcare providers and entities. Nevertheless, more needs to be done to assure that health privacy and all the values it is …