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Towards platform observability

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  • Rieder, Bernhard
  • Hofmann, Jeanette

Abstract

The growing power of digital platforms raises the question of democratic control or at least containment. In light of the transforming impact of platforms on markets, the public sphere, elections, and employment conditions, governments, and civil society alike are demanding more transparency and accountability. Shedding light on the principles and practices of algorithmic ordering promises to limit the power of platforms by subjecting their hidden operations to regulatory inspection. This article questions the popular image of an openable 'black box'. Based on a critical reflection on transparency as a panacea for curtailing platform power, we propose the concept of observability to deal more systematically with the problem of studying complex algorithmic systems. We set out three broad principles as regulatory guidelines for making platforms more accountable. These principles concern the normative and analytical scope, the empirical and temporal dimension, and the necessary capacities for learning and knowledge generation.

Suggested Citation

  • Rieder, Bernhard & Hofmann, Jeanette, 2020. "Towards platform observability," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 9(4), pages 1-28.
    • Handle: RePEc:zbw:iprjir:233111
    DOI: 10.14763/2020.4.1535
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    References listed on IDEAS

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    1. Christopher Parsons, 2015. "Beyond Privacy: Articulating the Broader Harms of Pervasive Mass Surveillance," Media and Communication, Cogitatio Press, vol. 3(3), pages 1-11.
    2. Napoli, Philip M., 2015. "Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers," Telecommunications Policy, Elsevier, vol. 39(9), pages 751-760.
    3. Cornelius, Kristin B., 2019. "Zombie contracts, dark patterns of design, and 'documentisation'," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 8(2), pages 1-25.
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    Cited by:

    1. Ariadna Matamoros-Fernández & Joanne E. Gray & Louisa Bartolo & Jean Burgess & Nicolas Suzor, 2021. "What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time," Media and Communication, Cogitatio Press, vol. 9(4), pages 234-249.
    2. Balázs Bodó & Heleen Janssen, 2022. "Maintaining trust in a technologized public sector [Machine Bias]," Policy and Society, Darryl S. Jarvis and M. Ramesh, vol. 41(3), pages 414-429.
    3. Iglesias Keller, Clara, 2021. "Don’t Shoot the Message: Regulating Disinformation Beyond Content," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(99), pages 486-515.
    4. Mantello, Peter & Ho, Tung Manh & Nguyen, Minh-Hoang & Vuong, Quan-Hoang, 2021. "My Boss the Computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the Non-Human Resource Management," OSF Preprints 4exjs, Center for Open Science.

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