Big Data Justice: A Case for Regulating the Global Information Commons

Spiekermann, Kai; Slavny, Adam; Axelsen, David V.; Lawford‐Smith, Holly

doi:10.1086/709862

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Online platforms which have become paradigmatic of today's digital ecosystem testify to the powerful economies of scale and scope that can be built on data. Political scientists caution against the concentration of data in very large corporations who can scale-up their data-based operations for their private benefit (Spiekermann et al, 2019). Also, the European Commission states that "in the US, the organisation of the data space is left to the private sector, with considerable concentration effects" (European Commission 2020a).…”

Section: Us Macro-level Data Governance Approachmentioning

confidence: 99%

“…Next to considerations that the public has a right to access information and that public sector data is an important resource that can benefit society, another argument for opening up public sector data is that such data are generated with public funds, meaning that they should not be kept exclusive or that no new charges should be levied for its re-use. this-similar to a critique of public data-promotes inequality (Spiekermann et al, 2019). Kitchin (2013) Moreover, while publicly funded data has to be open and released, private sector data is conventionally treated as an exclusive resource that is constitutionally protected under the freedom to conduct a business (Article 16, EU Charter of Fundamental Rights).…”

Section: Public Sector Information Logicsmentioning

confidence: 99%

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Personal data ordering in context: the interaction of meso-level data governance regimes with macro frameworks

Bodó¹,

Irion²,

Janssen³

et al. 2021

Internet Policy Review

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The technological infrastructures enabling the collection, processing, and trading of data have fuelled a rapid innovation of data governance models. We differentiate between macro, meso, and micro level models, which correspond to major political blocks; societal-, industry-, or community level systems, and individual approaches, respectively. We focus on meso-level models, which coalesce around: (1) organisations prioritising their own interests over interests of other stakeholders; (2) organisations offering technological and legal tools aiming to empower individuals; (3) community-based data intermediaries fostering collective rights and interests. In this article we assess these meso-level models, and discuss their interaction with the macro-level legal frameworks that have evolved in the US, the EU, and China. The legal landscape has largely remained inconsistent and fragmented, with enforcement struggling to keep up with the latest developments. We argue, first, that the success of meso-logics is largely defined by global economic competition, and, second, that these meso-logics may potentially put the EU's macrolevel framework with its mixed internal market and fundamental rights-oriented model under pressure. We conclude that, given the relative absence of a strong macro level-framework and an intensive competition of governance models at meso-level, it may be challenging to avoid compromises to the European macro framework. This paper is part of Governing "European values" inside data flows, a special issue of Internet Policy Review guest-edited by Kristina Irion,

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Section: Us Macro-level Data Governance Approachmentioning

confidence: 99%

Section: Public Sector Information Logicsmentioning

confidence: 99%

Personal data ordering in context: the interaction of meso-level data governance regimes with macro frameworks

Bodó¹,

Irion²,

Janssen³

et al. 2021

Internet Policy Review

View full text Add to dashboard Cite

show abstract

Learning Hybrid by Doing Hybrid: Teaching Critical Digital Skills in a Safe Learning Space

Rennstich

2023

Handbook of Applied Teaching and Learning in Social Work Management Education

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The Global Governance of Artificial Intelligence: Some Normative Concerns

Erman

Furendal

2022

Moral Philosophy and Politics

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The creation of increasingly complex artificial intelligence (AI) systems raises urgent questions about their ethical and social impact on society. Since this impact ultimately depends on political decisions about normative issues, political philosophers can make valuable contributions by addressing such questions. Currently, AI development and application are to a large extent regulated through non-binding ethics guidelines penned by transnational entities. Assuming that the global governance of AI should be at least minimally democratic and fair, this paper sets out three desiderata that an account should satisfy when theorizing about what this means. We argue, first, that an analysis of democratic values, political entities and decision-making should be done in a holistic way; second, that fairness is not only about how AI systems treat individuals, but also about how the benefits and burdens of transformative AI are distributed; and finally, that justice requires that governance mechanisms are not limited to AI technology, but are incorporated into a range of basic institutions. Thus, rather than offering a substantive theory of democratic and fair AI governance, our contribution is metatheoretical: we propose a theoretical framework that sets up certain normative boundary conditions for a satisfactory account.

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Big Data Justice: A Case for Regulating the Global Information Commons

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 3 publications

References 17 publications

Personal data ordering in context: the interaction of meso-level data governance regimes with macro frameworks

Personal data ordering in context: the interaction of meso-level data governance regimes with macro frameworks

Learning Hybrid by Doing Hybrid: Teaching Critical Digital Skills in a Safe Learning Space

The Global Governance of Artificial Intelligence: Some Normative Concerns

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