Construct Validity in Automated Counterterrorism Analysis.

Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of "terrorist"' in artificial intelligence are difficult to justify given three issue...

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Published in:Philosophy of Science Vol. 92; no. 3; pp. 566 - 584
Main Author: Yee, Adrian K.
Format: Article
Published: Cambridge University Press Jul2025
Subjects:
Online Access:View this record in EBSCOhost
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        atl: Construct Validity in Automated Counterterrorism Analysis.
      aug:
        au: Yee, Adrian K.
        affil: Lingnan University, Department of Philosophy, Hong Kong Catastrophic Risk Centre, Hong Kong
      su:
        Counterterrorism
        Machine learning
        Prediction models
        Test validity
        Predictive validity
        Artificial intelligence
        Risk assessment
      sug:
        subj:
          Counterterrorism
          Machine learning
          Prediction models
          Test validity
          Predictive validity
          Artificial intelligence
          Risk assessment
      ab: Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of "terrorist"' in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches.
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    language: English
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