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SYSTEMATIC REVIEW article

Front. Sustain. Cities
Sec. Innovation and Governance
Volume 6 - 2024 | doi: 10.3389/frsc.2024.1518618

Characterising and reassessing people-centred data governance in cities

Provisionally accepted
  • 1 Monash University, Melbourne, Australia
  • 2 Open University of Catalonia, Barcelona, Catalonia, Spain
  • 3 Simon Fraser University, Burnaby, British Columbia, Canada

The final, formatted version of the article will be published soon.

    The increasing deployment of digital infrastructures in cities highlights challenges in how people shape the conditions of data production that shape their cities and lives. As such, the need to centre data governance (DG) models around people is amplified. This paper unpacks and reassesses how people-centredness materialises at the level of DG in cities by conducting a scoping review of the literature on people-centred data governance (PCDG) in cities.Utilising twelve extraction categories framed by the conceptualisation of DG as a sociotechnical system, this review synthesises identified themes and outlines six archetypes.PCDG is characterised by people-centred values; the inclusion of people as agents, beneficiaries, or enablers; the employment of mechanisms for engaging people; or the pursuit of people-centred goals. These coalesce into diverse PCDG archetypes including compensation, rights-based, civic deliberation, civic representation, data donations, and community-driven models. The paper proposes a nuanced reassessment of what constitutes PCDG, focusing on whether DG models include people in the emergent benefits of data or merely legitimise their exclusion, the extent to which embedded power dynamics reflect people's perspectives, the extent to which participation influences decision-making, and the model's capacity to balance power asymmetries underpinning the landscape in which it is situated.

    Keywords: Data governance, People-centric, Cities, Smart initiatives, socio-technical system This mediation, underpinned by various entities

    Received: 28 Oct 2024; Accepted: 09 Dec 2024.

    Copyright: © 2024 Bou Nassar, C, Anwar, Sharp, Bartram and Goodwin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jessica Bou Nassar, Monash University, Melbourne, Australia

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