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Dissecting the Algorithmic Leviathan: On the Socio-Political Anatomy of Algorithmic Governance

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Abstract

A growing literature is taking an institutionalist and governance perspective on how algorithms shape society based on unprecedented capacities for managing social complexity. Algorithmic governance altogether emerges as a novel and distinctive kind of societal steering. It appears to transcend established categories and modes of governance—and thus seems to call for new ways of thinking about how social relations can be regulated and ordered. However, as this paper argues, despite its novel way of realizing outcomes of collective steering and coordination, it can nevertheless be grasped with an old and fundamental figure in political philosophy: that of Thomas Hobbes’ Leviathan. Comparing algorithmic governance with this figure serves to highlight their similarities as socio-political arrangements, and specifically to clarify how algorithmic governance parallels the apolitical traits of the Leviathan—it eliminates the political as it requires compliance and forgoing contestation to best fulfill its role and to produce satisfying outcomes.

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Notes

  1. Algorithmic governance can take various forms. Yeung (2017b) has provided a taxonomy of designs that vary along the three dimensions of standard-setting, monitoring, and sanctioning. The most potent designs combine a flexible, dynamic standard-setting with a pre-emptive monitoring and operating.

  2. It should be noted that this does not mean that all outputs—e.g., information, suggestions, decisions—are adapted to the particularities of every individual, but rather to features or group traits that individuals share with others.

  3. Rahwan (2017) has touched upon this figure thinking about what a social contract about algorithmic systems could look like. In the following, a different perspective is chosen, one that looks at how algorithmic governance itself amounts to a sort of social contract.

  4. The Chinese example of the Social Credit System, however, also shows that this can be turned around into effecting behavior and decisions through the fear of losing social status and access to various public or commercial offers and services.

  5. Moreover, algorithmic systems process inputs in the form of data that cannot speak for itself—it must be processed based on selection rules and decisions about what counts as relevant; and the specific ways of selecting, categorizing, and making distinctions based on available information always impose a certain way of seeing (Ananny and Crawford 2016; Mittelstadt and Floridi 2016; Floridi 2012).

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Acknowledgments

I would like to thank the reviewers for their valuable comments and suggestions. Thanks also go to Joschka Frech for assisting with the preparation of an earlier version of themanuscript.

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  1. Department of Social Sciences, University of Kaiserslautern, Erwin-Schrödinger-Straße, Building 57, PO-Box 3049, 67653, Kaiserslautern, Germany

    Pascal D. König

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König, P.D. Dissecting the Algorithmic Leviathan: On the Socio-Political Anatomy of Algorithmic Governance. Philos. Technol. 33, 467–485 (2020). https://doi.org/10.1007/s13347-019-00363-w

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