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

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?

Disclaimer: I extracted the most interesting facts and paragraphs from the paper and only rephrased some of them. References to the original sources can be found in the paper itself.

The usual algorithms which are used successfully to predict buying habits in e-commerce don’t seem to work to predict terrorist attack. There is no scientific evidence that these counter-terrorism measures can actually predict or prevent terrorism. There is a lack of efficiency in profiling and modelling of the future due to the fact that predictive algorithms project the past and thus assume that the past categorically equals the present. Predictive algorithms are therefore not suitable for open, dynamic systems with complex causal links in which the future differs from the past and which require new or dynamic categories (Silver, 2013). A current category can quickly become outdated and the relationship can quickly change in open, dynamic systems that are meta-reflexive.

The problem is far worse than “finding a needle in a haystack”

The problem is far worse than “finding a needle in a haystack”. In that analogy, the needle is easy to identify once it is observed. In contrast, many problems of counter-terrorism are, in the words of DARPA’s Ted Senator, about “assembling and identifying dangerous needles in stacks of needle pieces”. The problem is to infer the existence of clandestine organizations and activities, based on lower-level records that relate people, places, things, and events.

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