Law Commons^™

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 …

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

Danielle Keats Citron

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 …

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

Frank A. Pasquale

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 …

Big Data's Other Privacy Problem, James Grimmelmann

James Grimmelmann

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 …

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 …

Online Privacy And The First Amendment: An Opt-In Approach To Data Processing, Joseph A. Tomain

Articles by Maurer Faculty

An individual has little to no ability to prevent online commercial actors from collecting, using, or disclosing data about her. This lack of individual choice is problematic in the Big Data era because individual privacy interests are threatened by the ever increasing number of actors processing data, as well as the ever increasing amount and types of data being processed. This Article argues that online commercial actors should be required to receive an individual’s opt-in consent prior to data processing as a way of protecting individual privacy. I analyze whether an opt-in requirement is constitutionally permissible under the First Amendment …

Secret Consumer Scores And Segmentations: Separating Consumer 'Haves' From 'Have-Nots', Amy J. Schmitz

Faculty Publications

“Big Data” is big business. Data brokers profit by tracking consumers’ information and behavior both on- and offline and using this collected data to assign consumers evaluative scores and classify consumers into segments. Companies then use these consumer scores and segmentations for marketing and to determine what deals, offers, and remedies they provide to different individuals. These valuations and classifications are based on not only consumers’ financial histories and relevant interests, but also their race, gender, ZIP Code, social status, education, familial ties, and a wide range of additional data. Nonetheless, consumers are largely unaware of these scores and segmentations, …

Governing, Exchanging, Securing: Big Data And The Production Of Digital Knowledge, Bernard E. Harcourt

Faculty Scholarship

The emergence of Big Data challenges the conventional boundaries between governing, exchange, and security. It ambiguates the lines between commerce and surveillance, between governing and exchanging, between democracy and the police state. The new digital knowledge reproduces consuming subjects who wittingly or unwittingly allow themselves to be watched, tracked, linked and predicted in a blurred amalgam of commercial and governmental projects. Linking back and forth from consumer data to government information to social media, these new webs of information become available to anyone who can purchase the information. How is it that governmental, commercial and security interests have converged, coincided, …

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…. …

Commons At The Intersection Of Peer Production, Citizen Science, And Big Data: Galaxy Zoo, Michael J. Madison

Book Chapters

The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, sometimes known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an …