Machine Learning Against Terrorism: How Big Data Collection and Analysis Influences the Privacy-Security Dilemma.
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 individua...
| Published in: | Science & Engineering Ethics Vol. 26; no. 6; pp. 2975 - 2985 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
2020
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=147734319&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 147734319 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: 2020 vid: 26 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 147734319 10.1007/s11948-020-00254-w ppf: 2975 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P size: 563KB tig: atl: Machine Learning Against Terrorism: How Big Data Collection and Analysis Influences the Privacy-Security Dilemma. aug: au: Verhelst, H. M. Stannat, A. W. Mecacci, G. affil: Delft Institute of Applied Mathematics, Delft University of Technology, Van Mourik Broekmanweg 6, 2628XE, Delft, The Netherlands Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, The Netherlands su: Machine learning Acquisition of data Mass surveillance Data analysis Dilemma Big data sug: subj: Machine learning Acquisition of data Mass surveillance Data analysis Dilemma Big data keyword: Metadata collection National security Privacy-security dilemma ab: 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 machine learning techniques to infer the former from the latter. Given the sparsity of datasets used for machine learning in counterterrorism and the privacy risks attendant with bulk data collection, policymakers and other relevant stakeholders should critically re-evaluate the likelihood of success of the algorithms and the collection of data on which they depend. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2020. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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