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A guideline for understanding and measuring algorithmic governance in everyday life

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  • Latzer, Michael
  • Festic, Noemi

Abstract

Algorithmic governance affects individuals' reality construction and consequently social order in societies. Vague concepts of algorithmic governance and the lack of comprehensive empirical insights into this kind of institutional steering by software from a user perspective may, however, lead to unrealistic risk assessments and premature policy conclusions. Therefore, this paper offers a theoretical model to measure the significance of algorithmic governance and an empirical mixed-methods approach to test it in different life domains. Applying this guideline should lead to a more nuanced understanding of the actual significance of algorithmic governance, thus contributing to an empirically better-informed risk assessment and governance of algorithms.

Suggested Citation

  • Latzer, Michael & Festic, Noemi, 2019. "A guideline for understanding and measuring algorithmic governance in everyday life," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 8(2), pages 1-19.
    • Handle: RePEc:zbw:iprjir:214082
    DOI: 10.14763/2019.2.1415
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    References listed on IDEAS

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    Cited by:

    1. Noemi Festic, 2022. "Same, same, but different! Qualitative evidence on how algorithmic selection applications govern different life domains," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 85-101, January.
    2. Daria Gritsenko & Matthew Wood, 2022. "Algorithmic governance: A modes of governance approach," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 45-62, January.

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