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- W2752265982 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." @default.
- W2752265982 created "2017-09-15" @default.
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- W2752265982 date "2017-09-01" @default.
- W2752265982 modified "2023-10-18" @default.
- W2752265982 title "100,000 false positives for every real terrorist: Why anti-terror algorithms don't work" @default.
- W2752265982 doi "https://doi.org/10.5210/fm.v22i9.7126" @default.
- W2752265982 hasPublicationYear "2017" @default.
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