Data jurisdictions and rival regimes of algorithmic regulation

Johns, Fleur; Compton, Caroline

doi:10.1111/rego.12296

Cited by 15 publications

(10 citation statements)

References 41 publications

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“…Public amenities must be fluid and responsive ' (2020, p. 85). This recalls the discussion, in P2P, of efforts to make available to government officials 'live dashboard [s]' fed by public and private data analysed using machine learning algorithms to indicate when, where and in what order their attention should be directed and governance powers potentially exercised (Johns 2019a, p. 848; see also Johns and Compton 2019).…”

Section: Prototypical Governancementioning

confidence: 99%

State Changes: Prototypical Governance Figured and Prefigured

Johns

2022

Law Critique

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My 2019 article ‘From Planning to Prototypes: New Ways of Seeing Like a State’ (P2P) drew attention to some shortcomings of the kinds of critical, reformist impulses fostered in law and development work. I sought to show that persistent preoccupations with the destructive hubris of ‘top-down’ planning—especially state planning—bypassed the tendency for great power to be deployed in other stylistic modes: through the release and responsive tweaking of prototypes, for instance. This article engages with later developments—at one of P2P’s main field sites and scholarly literature in dialogue with P2P—to build upon the analysis therein. It elaborates on the kinds of power animated through prototyping, showing how prototyping is often combined with other governance styles and what it makes of the inputs on which law has typically been said to draw and of law’s claims to autonomy. It complicates the provenance of this style by reference to mid-to-late-twentieth century UN sanctions regimes, highlighting that governance prototyping is as much state-made as business-led and not restricted to the development field. With particular attention to the strangeness of the human user presupposed by prototyping, this article also aims to identify some modes of critical engagement to which this governance style might be answerable.

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Section: Prototypical Governancementioning

confidence: 99%

State Changes: Prototypical Governance Figured and Prefigured

Johns

2022

Law Critique

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“…In their input-throughput-output model of algorithmic selection, for instance, Just and Latzer noted that "there is a wide spectrum of input sources, depending on the field of application" (Just & Latzer 2017, p. 241). Yet, few studies systematically analyze what kinds of data are used in algorithmic regulation, how they are produced, and how they are linked to the objects they are to represent (for exceptions, see Johns and Compton (2020) and Bellanova and de Goede (2020)). A pronounced focus on data and data-related practices, however, is important if we want to establish what is special about algorithmic regulation.…”

Section: Toward An Analytical Framework: Previous Work and Challengesmentioning

confidence: 99%

Extending the framework of algorithmic regulation. The Uber case

Eyert

Irgmaier

Ulbricht

2020

Regulation & Governance

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In this article, we take forward recent initiatives to assess regulation based on contemporary computer technologies such as big data and artificial intelligence. In order to characterize current phenomena of regulation in the digital age, we build on Karen Yeung's concept of "algorithmic regulation," extending it by building bridges to the fields of quantification, classification, and evaluation research, as well as to science and technology studies. This allows us to develop a more fine-grained conceptual framework that analyzes the three components of algorithmic regulation as representation, direction, and intervention and proposes subdimensions for each. Based on a case study of the algorithmic regulation of Uber drivers, we show the usefulness of the framework for assessing regulation in the digital age and as a starting point for critique and alternative models of algorithmic regulation.

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“…Although detection of violations may be performed on a reactive basis, after their occurrence, there is also a move towards pre-emptive ambition on the basis of correlations within massive data sets (Yeung, 2017). Here too, regulation by and through algorithms is interrogated in terms of its justification, formalization, transformative potential, ultimate effectiveness, ordering effects, unexpected consequences and related degree of transparency and legal accountability (Burk, 2019; Eyert et al, 2022; Hildebrandt, 2018; Johns and Compton, 2022).…”

Section: Introductionmentioning

confidence: 99%

Big data surveillance across fields: Algorithmic governance for policing & regulation

Amicelle

2022

Big Data & Society

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While the academic separation of policing and regulation is still largely operative, points of convergence are more significant than ever in the digital age, starting with concomitant debates about algorithms as a new figure of power. From the policing of illegal activities to the regulation of legal ones, the algorithmization of such critical social ordering practices has been the subject of growing attention. These burgeoning discussions are focused on one common element: big data surveillance. In accordance with such similarities and paralleled developments in policing and regulation, the article aims to further bridge the gap between literatures to respond to the calls for studying big data surveillance across institutional domains and social fields. To do so, it is focused on one case study that articulates algorithmic policing and regulation domains, in-between security and economic fields. This is the fight against illicit finance, i.e. ‘the global action against the financial flows that fuel crime and terrorism’. To what extent does big data surveillance make a difference in the main global policy of crime-fighting and financial regulation? Drawing on a fieldwork in a large North American bank, the present article takes stock of the algorithmic overlap between policing and regulation. It argues that the final result is policing and regulation of neither too much nor too little, which gives rise to automated and everyday mass surveillance while remaining as far removed from regulatory and crime-fighting ambitions as it is from dystopian visions of big data and algorithmic drama.

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Data jurisdictions and rival regimes of algorithmic regulation

Cited by 15 publications

References 41 publications

State Changes: Prototypical Governance Figured and Prefigured

State Changes: Prototypical Governance Figured and Prefigured

Extending the framework of algorithmic regulation. The Uber case

Big data surveillance across fields: Algorithmic governance for policing & regulation

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