Machine Learning Against Terrorism: How Big Data Collection and Analysis Influences the Privacy-Security Dilemma.

Science and Engineering Ethics
H M VerhelstG Mecacci

Abstract

Rapid advancements in machine learning techniques allow mass surveillance to be applied on larger scales and utilize more and more personal data. These developments demand reconsideration of the privacy-security dilemma, which describes the tradeoffs between national security interests and individual privacy concerns. By investigating mass surveillance techniques that use bulk data collection and machine learning algorithms, we show why these methods are unlikely to pinpoint terrorists in order to prevent attacks. The diverse characteristics of terrorist attacks-especially when considering lone-wolf terrorism-lead to irregular and isolated (digital) footprints. The irregularity of data affects the accuracy of machine learning algorithms and the mass surveillance that depends on them which can be explained by three kinds of known problems encountered in machine learning theory: class imbalance, the curse of dimensionality, and spurious correlations. Proponents of mass surveillance often invoke the distinction between collecting data and metadata, in which the latter is understood as a lesser breach of privacy. Their arguments commonly overlook the ambiguity in the definitions of data and metadata and ignore the ability of machin...Continue Reading

References

May 3, 2011·Science and Engineering Ethics·Jeroen Van den HovenIbo Van de Poel
Mar 26, 2013·Scientific Reports·Yves-Alexandre de MontjoyeVincent D Blondel
Jul 31, 2013·Science and Engineering Ethics·Stephanie J Bird
Jan 31, 2015·Science·Yves-Alexandre de MontjoyeAlex Sandy Pentland
May 18, 2016·Proceedings of the National Academy of Sciences of the United States of America·Jonathan MayerJohn C Mitchell

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AlphaGo
PRISM

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