100,000 false positives for every real terrorist: Why anti-terror algorithms don’t work

by Timme Bisgaard Munk

First Monday, Volume 22, Number 9 - 4 September 2017

http://journals.uic.edu/ojs/index.php/fm/article/view/7126/6522
doi: http://dx.doi.org/10.5210/fm.v22i19.7126

Abstract

Can terrorist attacks be predicted and prevented using classification algorithms? Can predictive analytics see the hidden patterns and data tracks in the planning of terrorist acts? According to a number of IT firms that now offer programs to predict terrorism using predictive analytics, the answer is yes. According to scientific and application-oriented literature, however, these programs raise a number of practical, statistical and recursive problems. In a literature review and discussion, this paper examines specific problems involved in predicting terrorism. The problems include the opportunity cost of false positives/false negatives, the statistical quality of the prediction and the self-reinforcing, corrupting recursive effects of predictive analytics, since the method lacks an inner meta-model for its own learning- and pattern-dependent adaptation. The conclusion is algorithms don’t work for detecting terrorism and is ineffective, risky and inappropriate, with potentially 100,000 false positives for every real terrorist that the algorithm finds.