University of Leicester
Browse

Public perceptions of the use of artificial intelligence in Defence: a qualitative exploration

Download (679.64 kB)
journal contribution
posted on 2024-03-04, 12:09 authored by Lee Hadlington, Maria Karanika-MurrayMaria Karanika-Murray, Jane Slater, Jens Binder, Sarah Gardner, Sarah Knight

There are a wide variety of potential applications of artificial intelligence (AI) in Defence settings, ranging from the use of autonomous drones to logistical support. However, limited research exists exploring how the public view these, especially in view of the value of public attitudes for influencing policy-making. An accurate understanding of the public’s perceptions is essential for crafting informed policy, developing responsible governance, and building responsive assurance relating to the development and use of AI in military settings. This study is the first to explore public perceptions of and attitudes towards AI in Defence. A series of four focus groups were conducted with 20 members of the UK public, aged between 18 and 70, to explore their perceptions and attitudes towards AI use in general contexts and, more specifically, applications of AI in Defence settings. Thematic analysis revealed four themes and eleven sub-themes, spanning the role of humans in the system, the ethics of AI use in Defence, trust in AI versus trust in the organisation, and gathering information about AI in Defence. Participants demonstrated a variety of misconceptions about the applications of AI in Defence, with many assuming that a variety of different technologies involving AI are already being used. This highlighted a confluence between information from reputable sources combined with narratives from the mass media and conspiracy theories. The study demonstrates gaps in knowledge and misunderstandings that need to be addressed, and offers practical insights for keeping the public reliably, accurately, and adequately informed about the capabilities, limitations, benefits, and risks of AI in Defence.

Funding

This work was conducted as part of a funded project under the HSSRC framework, bid number HS1.041

History

Author affiliation

College of Social Sci Arts and Humanities/School of Business

Version

  • VoR (Version of Record)

Published in

AI & SOCIETY

Publisher

Springer Science and Business Media LLC

issn

0951-5666

eissn

1435-5655

Copyright date

2024

Available date

2024-03-04

Language

en

Deposited by

Professor Maria Karanika-Murray

Deposit date

2024-03-02

Data Access Statement

The data that support the findings of this study are available from the corresponding author, but restrictions apply to the availability of these data and are released with permission from the Defence Science Technology Laboratory (DSTL). Requests should be made to the corresponding author in the first instance.

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC