Law Commons^™

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 …

Fulfilling Government 2.0'S Promise With Robust Privacy Protections, Danielle Citron

Danielle Keats Citron

The public can now “friend” the White House and scores of agencies on social networks, virtual worlds, and video-sharing sites. The Obama Administration sees this trend as crucial to enhancing governmental transparency, public participation, and collaboration. As the President has underscored, government needs to tap into the public’s expertise because it doesn’t have all of the answers. To be sure, Government 2.0 might improve civic engagement. But it also might produce privacy vulnerabilities because agencies often gain access to individuals’ social network profiles, photographs, videos, and contact lists when interacting with individuals online. Little would prevent agencies from using and …