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Algorithmic state surveillance: Challenging the notion of agency in human rights

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  • Eleni Kosta

Abstract

This paper explores the extent to which current interpretations of the notion of agency, as traditionally perceived under human rights law, pose challenges to human rights protection in light of algorithmic surveillance. After examining the notion of agency under the European Convention on Human Rights as a criterion for applications' admissibility, the paper looks into the safeguards of notification and of redress – crucial safeguards developed by the Court in secret surveillance cases – which are used as examples to illustrate their insufficiency in light of algorithmic surveillance. The use of algorithms creates new surveillance methods and challenges fundamental presuppositions on the notion of agency in human rights protection. Focusing on the victim status does not provide a viable solution to problems arising from the use of Artificial Intelligence in state surveillance. The paper thus raises questions for further research concluding that a new way of thinking about agency for the protection of human rights in the context of algorithmic surveillance is needed in order to offer effective protection to individuals.

Suggested Citation

  • Eleni Kosta, 2022. "Algorithmic state surveillance: Challenging the notion of agency in human rights," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 212-224, January.
    • Handle: RePEc:wly:reggov:v:16:y:2022:i:1:p:212-224
    DOI: 10.1111/rego.12331
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    References listed on IDEAS

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    1. Karen Yeung, 2018. "Algorithmic regulation: A critical interrogation," Regulation & Governance, John Wiley & Sons, vol. 12(4), pages 505-523, December.
    2. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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    Cited by:

    1. Wernick, Alina & Artyushina, Anna, 2023. "Future-proofing the city: A human rightsbased approach to governing algorithmic, biometric and smart city technologies," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 12(1), pages 1-26.
    2. Kira J.M. Matus & Michael Veale, 2022. "Certification systems for machine learning: Lessons from sustainability," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 177-196, January.

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