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Increasing resilience via the use of personal data: Lessons from COVID-19 dashboards on data governance for the public good

Li, VQT; Yarime, M; (2021) Increasing resilience via the use of personal data: Lessons from COVID-19 dashboards on data governance for the public good. Data & Policy , 3 (e29) 10.1017/dap.2021.27. Green open access

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Abstract

Contemporary data tools such as online dashboards have been instrumental in monitoring the spread of the COVID-19 pandemic. These real-time interactive platforms allow citizens to understand the local, regional, and global spread of COVID-19 in a consolidated and intuitive manner. Despite this, little research has been conducted on how citizens respond to the data on the dashboards in terms of the pandemic and data governance issues such as privacy. In this paper, we seek to answer the research question: how can governments use data tools, such as dashboards, to balance the trade-offs between safeguarding public health and protecting data privacy during a public health crisis? This study used surveys and semi-structured interviews to understand the perspectives of the developers and users of COVID-19 dashboards in Hong Kong. A typology was also developed to assess how Hong Kong’s dashboards navigated trade-offs between data disclosure and privacy at a time of crisis compared to dashboards in other jurisdictions. Results reveal that two key factors were present in the design and improvement of COVID-19 dashboards in Hong Kong: informed actions based on open COVID-19 case data, and significant public trust built on data transparency. Finally, this study argues that norms surrounding reporting on COVID-19 cases, as well as cases for future pandemics, should be co-constructed among citizens and governments so that policies founded on such norms can be acknowledged as salient, credible, and legitimate.

Type: Article
Title: Increasing resilience via the use of personal data: Lessons from COVID-19 dashboards on data governance for the public good
Open access status: An open access version is available from UCL Discovery
DOI: 10.1017/dap.2021.27
Publisher version: http://dx.doi.org/10.1017/dap.2021.27
Language: English
Additional information: This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited Copyright © The Author(s), 2021. Published by Cambridge University Press
Keywords: COVID-19, data governance, data transparency, data privacy, public trust
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > STEaPP
URI: https://discovery.ucl.ac.uk/id/eprint/10139104
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