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Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist

Torsten Reimer¹, Nathanael Johnson¹

¹Communication and Cognition Lab, Brian Lamb School of Communication, Purdue University, 100 North University Street, West Lafayette, IN 47907 USA.

Current Psychology (New Brunswick, N.J.)

|March 7, 2022

Summary

Public support for using probabilistic computing in counterterrorism is strong, especially for public data. However, privacy concerns influence willingness to share private data, with a bias against using information from in-group members.

Keywords:

Counterterrorism Ingroup favoritism Privacy concerns Social identity theory

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Area of Science:

Cybersecurity
Data Science
Social Psychology

Background:

Big data analytics and probabilistic computing offer advanced methods for identifying online threats, including terrorism.
These technologies present potential privacy concerns due to their reliance on large datasets.
Public perception and support are crucial for the ethical implementation of such technologies in counterterrorism.

Purpose of the Study:

To survey public support for employing probabilistic computing technologies in counterterrorism efforts.
To investigate public attitudes towards the use of different types of personal information for national security.

Main Methods:

A survey study was conducted with 1,023 participants.
Respondents' support for using probabilistic computing for counterterrorism was assessed.
Attitudes towards using publicly available versus private personal information were examined, differentiating between in-group and out-group members.

Main Results:

Strong public support exists for using publicly available personal information for counterterrorism.
Respondents showed greater willingness to allow the use of private personal information from non-citizens (out-group) compared to citizens (in-group).
In-group favoritism, particularly concerning private data, was most pronounced in individuals with strong national identities and high privacy concerns.

Conclusions:

Public acceptance of probabilistic computing in counterterrorism is contingent on data type and perceived privacy implications.
Social identity and in-group favoritism significantly shape public opinion on using personal data for national security.
Findings highlight the need for transparent policies addressing privacy concerns and potential biases in data usage for counterterrorism.