To score is human. Ranking individuals by grades and other performance numbers is as old as human society. Consumer scores — numbers given to individuals to describe or predict their characteristics, habits, or predilections — are a modern day numeric shorthand that ranks, separates, sifts, and otherwise categorizes individuals and also predicts their potential future actions. This new report by Pam Dixon and Robert Gellman explores this issue of predictive scores and privacy.
This op ed was originally published Wednesday, March 19 2014 in IAPP for the FTC Alternate Scoring Conference.
In our modern sea of data, the resources to examine all relevant information regarding a decision is no longer feasible, so we use shortcuts. Consumer scores built using predictive analytics and fed by large datasets are the modern-day shortcuts to understanding individual consumer behavior. That’s why new and unregulated consumer scores abound. They are used widely in today’s world to predict consumers’ behavior, spending, health, fraud, profitability, and much more. These scores rely on petabytes of information coming from newly available data streams, and some old ones.
Chris Hoofnagle of Berkeley Law has just published arguably the single most important archive in privacy today: it is the transcripts of six of the HEW meetings in the early 197os that formed the origins of today’s Fair Information Practices. FIPs have now for 40 years formed the cornerstone of most of the privacy laws passed globally. Long lost to the dust of time, the original hearing transcripts have never been available online, and even access to the paper versions have not been widely available.
This interactive map shows in one glance the medical data breaches in the U.S. that have been reported to the U.S. government from 2009-2012. Each blue dot represents one breach. The bigger the dot, the bigger the breach. To see the detailed information about where, when, and how the breach happened, mouse over the dots, or zoom in by state.
An important and emerging area of facial recognition tech is “Passive Inspection Point” tech. This means the technology automatically captures consumers’ face prints as they move through public spaces like malls and on city streets. This video showcases current technology by Accenture that can automatically collect and enroll consumers’ facial biometrics from many angles.