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AI-powered public surveillance systems: why we (might) need them and how we want them

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  • Fontes, Catarina
  • Hohma, Ellen
  • Corrigan, Caitlin C.
  • Lütge, Christoph

Abstract

In this article, we address the introduction of AI-powered surveillance systems in our society by looking at the deployment of real-time facial recognition technologies (FRT) in public spaces and public health surveillance technologies, in particular contact tracing applications. Both cases of surveillance technologies assist public authorities in the enforcement of the law by allowing the tracking of individual movements and extrapolating results towards monitoring and predicting social behavior. Therefore, they are considered as potentially useful tools in response to societal crises, such as those generated by crime and health related pandemics. To approach the assessment of the potentials and threats of such tools, we offer a framework with three dimensions. A function dimension, examines the type, quality and quantity of data the system needs to employ to work effectively.The consent dimension considers the user's right to be informed about and reject the use of surveillance, questioning whether consent is achievable and whether the user can decide fully autonomously/independently. Finally, a societal dimension that frames vulnerabilities and the impacts of the increased empowerment of established political regimes through new means to control populations based on data surveillance. Our analysis framework can assist public authorities in their decisions on how to design and deploy public surveillance tools in a way that enables compliance with the law while highlighting individual and societal tradeoffs.

Suggested Citation

  • Fontes, Catarina & Hohma, Ellen & Corrigan, Caitlin C. & Lütge, Christoph, 2022. "AI-powered public surveillance systems: why we (might) need them and how we want them," Technology in Society, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x22002780
    DOI: 10.1016/j.techsoc.2022.102137
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    References listed on IDEAS

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    1. Kao, Yu-Hui & Sapp, Stephen G., 2022. "The effect of cultural values and institutional trust on public perceptions of government use of network surveillance," Technology in Society, Elsevier, vol. 70(C).
    2. Ioannou, Athina & Tussyadiah, Iis, 2021. "Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours," Technology in Society, Elsevier, vol. 67(C).
    3. Tran, Cong Duc & Nguyen, Tin Trung, 2021. "Health vs. privacy? The risk-risk tradeoff in using COVID-19 contact-tracing apps," Technology in Society, Elsevier, vol. 67(C).
    4. Davide Castelvecchi, 2020. "Is facial recognition too biased to be let loose?," Nature, Nature, vol. 587(7834), pages 347-349, November.
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    Cited by:

    1. Fontes, Catarina & Corrigan, Caitlin & Lütge, Christoph, 2023. "Governing AI during a pandemic crisis: Initiatives at the EU level," Technology in Society, Elsevier, vol. 72(C).
    2. Burton, Joe, 2023. "Algorithmic extremism? The securitization of artificial intelligence (AI) and its impact on radicalism, polarization and political violence," Technology in Society, Elsevier, vol. 75(C).
    3. Wang, Victoria & Tucker, John V., 2023. "People watching: Abstractions and orthodoxies of monitoring," Technology in Society, Elsevier, vol. 72(C).
    4. Jun Liu & Shuang Lai & Ayesha Akram Rai & Abual Hassan & Ray Tahir Mushtaq, 2023. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace," IJERPH, MDPI, vol. 20(5), pages 1-24, February.

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