According to Ars Technica, the machine learning algorithm used by the NSA to analyse cellular data and determine if someone is a terrorist is "ridiculously optimistic" and "completely bullshit." As a result, thousand of innocent people "may have been mislabelled as terrorists," resulting in their "untimely demise." The authors worry when this system will be used nationally, "assuming it hasn't been already." Above, a screenshot of an NSA presentation showing how a journalist was labelled as a "member of Al-Qa'ida" because of his travels.
The program, the slides tell us, is based on the assumption that the behaviour of terrorists differs significantly from that of ordinary citizens with respect to some of these properties. However, as The Intercept's exposé last year made clear, the highest rated target according to this machine learning program was Ahmad Zaidan, Al-Jazeera's long-time bureau chief in Islamabad.
As The Intercept reported, Zaidan frequently travels to regions with known terrorist activity in order to interview insurgents and report the news. But rather than questioning the machine learning that produced such a bizarre result, the NSA engineers behind the algorithm instead trumpeted Zaidan as an example of a SKYNET success in their in-house presentation, including a slide that labelled Zaidan as a "MEMBER OF AL-QA'IDA."
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