Recommended Citation
Anita Ramasastry, Lost in Translation? Data Mining, National Security and the Adverse Inference Problem, 22 Santa Clara Computer & High Tech. L.J. 757 (2006), https://digitalcommons.law.uw.edu/faculty-articles/739
Keywords
data mining, terrorism prevention, adverse inferences, privacy
Document Type
Article
Abstract
To the extent that we permit data mining programs to proceed, they must provide adequate due process and redress mechanisms that permit individuals to clear their names. A crucial criteria for such a mechanism is to allow access to information that was used to make adverse assessments so that errors may be corrected. While some information may have to be kept secret for national security purposes, a degree of transparency is needed when individuals are trying to protect their right to travel or access government services free from suspicion.
Part II of this essay briefly outlines the government's ability to gain access to private sector data held by commercial entities or "third parties." Part III of this essay examines data mining and some of the problems inherent in using data analysis as a predictive tool for terrorism prevention. Part IV of this paper focuses on the specific problem of adverse inferences. This section examines the recent efforts of the federal Transportation Security Administration (TSA) to use data mining in airline passenger profiling. The Computer Assisted Passenger Profiling and Prescreening System II (CAPPS II) as mapped out by the TSA, and the most recent initiative, Secure Flight, illustrate some of the perceived risks inherent in the use of data mining to try and predict whether individuals are a security risk. Part V of this paper explores what efforts Congress and policy makers can make to address the risk of false positives and adverse influences,and the rise of commercial data mining as a favored tool for combating terrorism.