1932

Abstract

The COVID-19 pandemic has highlighted many complexities involved in using data and advanced technologies to help resolve public health emergencies. These complexities highlight the need to embrace a broader framework of data governance with three foundational questions: () who decides about data flows, () on what basis, and () with what accountability and oversight. These questions can accommodate the issues that have arisen in the literature regarding new types of data harms. However, these questions also foreground important issues of power, authority, and legitimacy. Data governance can provide an organizing normative framework to address emerging data themes including access to data, collective decision making, data intermediaries, data sovereignty, design and digital infrastructure, regulatory technologies, the rule of law, and social trust and license. The pandemic experience with contact tracing apps, in particular, showed the many unresolved governance challenges.

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2023-10-05
2024-10-06
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Literature Cited

  1. Abraham R, Schneider J, vom Brocke J. 2019. Data governance: a conceptual framework, structured review, and research agenda. Int. J. Inf. Manag. 49:424–38
    [Google Scholar]
  2. Ada Lovelace Inst 2020. Exit through the app store: a rapid evidence review on the technical considerations and societal implications of using technology to transition from the COVID-19 crisis Rev. Ada Lovelace Inst. London: https://www.adalovelaceinstitute.org/wp-content/uploads/2020/04/Ada-Lovelace-Institute-Rapid-Evidence-Review-Exit-through-the-App-Store-April-2020-2.pdf
    [Google Scholar]
  3. Alhassan I, Sammon D, Daly M. 2016. Data governance activities: an analysis of the literature. J. Decis. Syst. 25:64–75
    [Google Scholar]
  4. Alhassan I, Sammon D, Daly M. 2018. Data governance activities: a comparison between scientific and practice-oriented literature. J. Enterp. Inf. Manag. 31:2300–16
    [Google Scholar]
  5. Artyushina A. 2020. Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto. Telematics Inform. 55:101456
    [Google Scholar]
  6. Austin L. 2022. From privacy to social legibility. Surveill. Soc. 20:3 https://doi.org/10.24908/ss.v20i3.15762
    [Google Scholar]
  7. Austin L, Lie D. 2019. Safe sharing sites. N.Y. Univ. Law Rev. 94:4581–623
    [Google Scholar]
  8. Austin L, Lie D. 2021. Data trusts and the governance of smart environments: lessons from the failure of Sidewalk Labs’ Urban Data Trust. Surveill. Soc. 19:2 https://doi.org/10.24908/ss.v19i2.14409
    [Google Scholar]
  9. Austin L, Slane A. 2023. Digitally rethinking Hunter v Southam. . Osgoode Hall Law J. 60: In press
    [Google Scholar]
  10. Bacharach M, Gambetta D. 2001. Trust in signs. Trust in Society K Cook 148–84. New York: Russell Sage Found.
    [Google Scholar]
  11. Bagchi K, Bannan C, Franklin SB, Hurlburt H, Sarkesian L et al. 2020. Digital tools for COVID-19 contact tracing: identifying and mitigating the equity, privacy, and civil liberties concerns White Pap. 22 Edmond Lily Safra Cent. Ethics, Harvard Univ. Cambridge, MA: https://ethics.harvard.edu/digital-tools-for-contact-tracing
    [Google Scholar]
  12. Balkin J. 2016. Information fiduciaries and the first amendment. U.C. Davis Law Rev. 49:1183–234
    [Google Scholar]
  13. Bay J. 2020. Automated contact tracing is not a coronavirus panacea. Government Digital Services Singapore April 10. https://medium.com/singapore-gds/automated-contact-tracing-is-not-a-coronavirus-panacea-57fb3ce61d98
    [Google Scholar]
  14. Benfeldt O, Persson JS, Madsen S. 2020. Data governance as a collective action problem. Inf. Syst. Front. 22:2299–313
    [Google Scholar]
  15. Benn C, Lazar S. 2022. What's wrong with automated influence. Can. J. Philos. 52:1125–48
    [Google Scholar]
  16. Bhatt A. 2021. Data sovereignty: the quintessential model for the new world order. Indian Law Inst. Law Rev. 2021:285–302
    [Google Scholar]
  17. Bijker WE, Hughes TP, Pinch T, eds. 2012. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology Cambridge, MA: MIT Press Anniv. ed .
    [Google Scholar]
  18. Birnhack M, Zar M. 2020. Privacy in crisis: privacy guidelines for the design of contact tracing technologies Work. Pap. Tel Aviv Univ., Tel Aviv Israel: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3683166
    [Google Scholar]
  19. Bourdeaux M, Gray ML, Crosz B. 2020. How human-centered tech can beat COVID-19 through contact tracing. The Hill April 21. https://thehill.com/opinion/technology/493648-how-human-centered-technology-can-beat-covid-19-through-contact-tracing/
    [Google Scholar]
  20. Bradford L, Aboy M, Liddell K. 2020. COVID-19 contact tracing apps: a stress test for privacy, the GDPR, and data protection regimes. J. Law Biosci. 7:1lsaa034
    [Google Scholar]
  21. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. 2020. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int. J. Environ. Res. Public Health 17:93176
    [Google Scholar]
  22. Brous P. 2020. Trusted decision-making: data governance for creating trust in data science decision outcomes. Adm. Sci. 10:481
    [Google Scholar]
  23. Brownsword R. 2021. Law 3.0: Rules, Regulation, and Technology New York: Routledge
    [Google Scholar]
  24. Butler A, Zhou E. 2021. Disease and data in society: how the pandemic expanded data collection and surveillance systems. Am. Univ. Law Rev. 70:51577–628
    [Google Scholar]
  25. Cavoukian A. 2009. Privacy by Design: the 7 foundational principles Resour., Inf. Priv. Comm. Ont., Tor. Ont., Can: https://iapp.org/resources/article/privacy-by-design-the-7-foundational-principles/
    [Google Scholar]
  26. [Google Scholar]
  27. Chakravorti B. 2022. Why AI failed to live up to its potential during the pandemic. Harvard Business Review March 17. https://hbr.org/2022/03/why-ai-failed-to-live-up-to-its-potential-during-the-pandemic
    [Google Scholar]
  28. Churches G, Zalnieriute M. 2020. The instrumentality of metadata access regime for suppressing political protests in Australia. Blog of the International Journal of Constitutional Law Aug. 4. http://www.iconnectblog.com/2020/08/the-instrumentality-of-metadata-access-regime-for-suppressing-political-protests-in-australia
    [Google Scholar]
  29. Cofone I. 2019. Algorithmic discrimination is an information problem. Hastings Law J. 70:61389–443
    [Google Scholar]
  30. Cofone I. 2021a. Beyond data ownership. Cardozo Law Rev. 43:2501–72
    [Google Scholar]
  31. Cofone I. 2021b. Immunity passports and contact tracing surveillance. Stanford Technol. Law Rev. 24:2176–236
    [Google Scholar]
  32. Cohen JE. 2019. Between Truth and Power: The Legal Constructions of Informational Capitalism New York: Oxford Univ. Press
    [Google Scholar]
  33. Daly M. 2003. Governance and social policy. J. Soc. Policy 32:1113–28
    [Google Scholar]
  34. Daskal J. 2020. Good health and good privacy go hand-in-hand (originally published by JNSLP). Joint PIJIP/TLS Res. Pap. Ser. 60. https://digitalcommons.wcl.american.edu/research/60
    [Google Scholar]
  35. [Google Scholar]
  36. Delacroix S, Lawrence ND. 2019. Bottom-up data trusts: disturbing the “one size fits all” approach to data governance. Int. Data Priv. Law 9:4236–52
    [Google Scholar]
  37. Elouazizi N. 2014. Critical factors in data governance for learning analytics. J. Learn. Anal. 1:3211–22
    [Google Scholar]
  38. Enriquez-Sarano L. 2020. Data-rich and knowledge-poor: how privacy law privatized medical data and what to do about it. Columbia Law Rev. 120:82319–58
    [Google Scholar]
  39. ETHI (Stand. Comm. Access Inf. Priv. Ethics) 2022. Collection and use of mobility data by the government and related issues Rep. ETHI Ottawa, Can.: https://www.ourcommons.ca/DocumentViewer/en/44-1/ETHI/report-4/
    [Google Scholar]
  40. Eur. Data Prot. Board 2019. Guidelines 4/2019 on article 25 data protection by design and by default, Version 2.0 Guidel., Eur. Data Prot. Board, Brussels https://edpb.europa.eu/our-work-tools/our-documents/guidelines/guidelines-42019-article-25-data-protection-design-and_en
    [Google Scholar]
  41. Eur. Data Prot. Board 2020. Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak Guidel., Eur. Data Prot. Board, Brussels https://edpb.europa.eu/our-work-tools/our-documents/guidelines/guidelines-042020-use-location-data-and-contact-tracing_en
    [Google Scholar]
  42. Ferreti L, Wymant C, Kendall M, Zhao L, Nurtay A et al. 2020. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368:6491619
    [Google Scholar]
  43. Findlay M, Remolina N. 2020. Regulating personal data usage in COVID-19 control conditions Res. Pap. 2020/04 SMU Cent. AI Data Gov. Dallas, TX: https://ssrn.com/abstract=3607706 or http://dx.doi.org/10.2139/ssrn.3607706
    [Google Scholar]
  44. Fisher A, Streinz T. 2022. Confronting data inequality. Columbia J. Transnatl. Law 60:3829–956
    [Google Scholar]
  45. Flood CM, Thomas B, Wilson K 2020. Civil liberties versus public health. Vulnerable: The Law, Policy and Ethics of COVID-19 CM Flood, V MacDonnell, J Philpott, S Thériault, S Venkatapuram, chapter C-1 Ottawa: Univ. Ott. Press
    [Google Scholar]
  46. Frischmann B, Selinger E. 2018. Re-Engineering Humanity New York: Cambridge Univ. Press
    [Google Scholar]
  47. Ghose A, Li B, Macha M, Sun C, Foutz NZ. 2020. Trading privacy for the greater social good: How did America react during COVID-19? IDEAS Work. Pap. Ser. Res. Pap. Econ. http://dx.doi.org/10.2139/ssrn.3624069
    [Google Scholar]
  48. Gil ED. 2021. Digital contact tracing has failed: Can it be fixed with better legal design? Va. . J. Law Technol. 25:11–37
    [Google Scholar]
  49. Glob. Priv. Assem 2020. Adopted resolution on the privacy and data protection challenges arising in the context of the COVID-19 pandemic Resolut., 42nd Closed Sess Glob. Priv. Assem., Oct. Mexico City:
    [Google Scholar]
  50. Griffin J. 2005. Data governance: a strategy for success. DM Rev. 15:649
    [Google Scholar]
  51. Guo S, Lumineau F, Lewicki R. 2017. Revisiting the foundations of organizational distrust. Found. Trends Manag. 1:1–88
    [Google Scholar]
  52. Hadfield G. 2017. Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy New York: Oxford Univ. Press
    [Google Scholar]
  53. Hartzog W. 2018. Privacy's Blueprint: The Battle to Control the Design of New Technologies Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  54. Herzog L. 2021. Privatizing private data. The Cambridge Handbook of Privatization A Dorfman, A Harel 230–44. Cambridge, MA: Cambridge Univ. Press
    [Google Scholar]
  55. Hildebrandt M. 2015. Smart Technologies and the End(s) of Law Cheltenham, UK: Edward Elgar
    [Google Scholar]
  56. Hildebrandt M, Tielemans L. 2013. Data protection by design and technology neutral law. Comput. Law Secur. Rep. 29:5509–21
    [Google Scholar]
  57. Hinch R, Probert W, Nurtay A, Kendall M, Wymant C et al. 2020. Effective configurations of a digital contact tracing app: a report to NHSX. Rep. Conversation Waltham, MA: https://cdn.theconversation.com/static_files/files/1009/Report_-_Effective_App_Configurations.pdf
    [Google Scholar]
  58. Hoffman D. 2020. Increasing access to care: telehealth during COVID-19. J. Law Biosci. 7:1lsaa043
    [Google Scholar]
  59. Hoffman D, Podgurski A. 2020. Artificial intelligence and discrimination in health care. Yale J. Health Policy Law Ethics 19:33–49
    [Google Scholar]
  60. Houser KA, Bagby JM. 2022. The data trust solution to data sharing problems. Vanderbilt J. Entertain. Technol. Law. In press. http://dx.doi.org/10.2139/ssrn.4050593
    [Google Scholar]
  61. Howell BE, Potgieter PH. 2021. A tale of two contact-tracing apps—comparing Australia's COVIDSafe and New Zealand's NZ COVID Tracer. Info 23:5509–28
    [Google Scholar]
  62. Hubbard D. 2020. Data governance 101: IR's critical role in data governance. New Dir. Inst. Res. 2020:51–65
    [Google Scholar]
  63. Huq AZ. 2021. The public trust in data. Georgetown Law J. 110:2333–402
    [Google Scholar]
  64. Iliadis A, Russo F. 2016. Critical data studies: an introduction. Big Data Soc. 3:2 https://doi.org/10.1177/2053951716674238
    [Google Scholar]
  65. Jalali MS, Landman A, Gordon W. 2021. Telemedicine, privacy, and information security in the age of COVID-19. J. Am. Med. Inform. Assoc. 28:3671–72
    [Google Scholar]
  66. Janssen M. 2020. Data governance: organizing data for trustworthy artificial intelligence. Gov. Info. Q. 37:3101493
    [Google Scholar]
  67. Kampmark B. 2020. The pandemic surveillance state: an enduring legacy of COVID-19. J. Glob. Faultlines 7:159–70
    [Google Scholar]
  68. Kaplan B. 2020. Revisiting health information technology ethical, legal, and social issues and evaluation: telehealth/telemedicine and COVID-19. Int. J. Med. Inform. 143:104239
    [Google Scholar]
  69. Karmakar S, Das S. 2021. Understanding the rise of Twitter-based cyberbullying due to COVID-19 through comprehensive statistical evaluation. Proceedings of the 54th Hawaii International Conference on System Sciences, Maui, Hawaii (Virtual). http://dx.doi.org/10.2139/ssrn.3768839
    [Google Scholar]
  70. Kingsbury B, Maisley N. 2021. Infrastructures and laws: publics and publicness. Annu. Rev. Law Soc. Sci. 17:353–73
    [Google Scholar]
  71. Knoppers BM, Beauvais MJS, Joly Y, Zawati MH, Rousseau S et al. 2020. Modeling consent in the time of COVID-19. J. Law Biosci. 7:1lsaa020
    [Google Scholar]
  72. Koch R, Corban T. 2020. Data governance in digital transformation. Strateg. Finance 102:360–61
    [Google Scholar]
  73. Koops B, Leenes RE. 2014. Privacy regulation cannot be hardcoded: a critical comment on the ‘Privacy by Design’ provision in data-protection law. Int. Rev. Law Comput. Technol. 28:159–71
    [Google Scholar]
  74. Kostka G, Habich-Sobiegalla S. 2022. In times of crisis: public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States. New Media Soc. In press
    [Google Scholar]
  75. Landau S. 2021. People Count: Contact-Tracing Apps and Public Health Cambridge, MA: MIT Press
    [Google Scholar]
  76. Lazar S. 2022. Legitimacy, authority, and the political value of explanations. arXiv 08628. https://doi.org/10.48550/arxiv.2208.08628
  77. Leith D, Farrell S. 2020a. GAEN due diligence: verifying the Google/Apple COVID exposure notification API https://www.scss.tcd.ie/Doug.Leith/pubs/gaen_verification.pdf
    [Google Scholar]
  78. Leith D, Farrell S. 2020b. Contact tracing app privacy: What data is shared by Europe's GAEN contact tracing apps. 2021 IEEE Conference on Computer Communications1–10. Vancouver, BC: IEEE
    [Google Scholar]
  79. Leslie D. 2020. Tackling COVID-19 through responsible AI innovation: five steps in the right direction. Harvard Data Sci. Rev. https://doi.org/10.48550/arxiv.2008.06755
    [Google Scholar]
  80. Levin A. 2018. Privacy by design by regulation: the case study of Ontario. Can. J. Contemp. Comp. Law 1:116–59
    [Google Scholar]
  81. Lewicki J, McAllister DJ, Bies RJ. 1998. Trust and distrust: new relationships and realities. Acad. Manag. Rev. 23:438–58
    [Google Scholar]
  82. Li T. 2021. Privacy in pandemic: law, technology, and public health in the COVID-19 crisis. Loyola Univ. Chicago Law J. 52:3767–865
    [Google Scholar]
  83. Liakh O. 2021. Accountability through sustainability data governance: reconfiguring reporting to better account for the digital acceleration. Sustainability 13:2413814
    [Google Scholar]
  84. Mann M, Mitchell P, Foth M. 2022. Between surveillance and technological solutionism: a critique of privacy-preserving apps for COVID-19 contact-tracing. New Media Soc. In press
    [Google Scholar]
  85. Marková I, Grossen M, Linell P, Salazar OA. 2007. Dialogue in Focus Groups: Exploring Socially Shared Knowledge London: Equinox
    [Google Scholar]
  86. Marks M. 2021. Emergent medical data: health information inferred by artificial intelligence. UC Irvine Law Rev. 11:995–1066
    [Google Scholar]
  87. Marx G. 2015. Surveillance studies. Int. Encycl. Soc. Behav. Sci. 23:733–41
    [Google Scholar]
  88. McCall MK, Skutsch MM, Honey-Roses J. 2021. Surveillance in the COVID-19 normal: tracking, tracing, and snooping—trade-offs in safety and autonomy in the e-city. Int. J. E-Plann. Res. 10:227–44
    [Google Scholar]
  89. Micheli M, Ponti M, Craglia M, Suman AB. 2020. Emerging models of data governance in the age of datafication. Big Data Soc. 7:2 https://doi.org/10.1177/2053951720948087
    [Google Scholar]
  90. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. 2016. The ethics of algorithms: mapping the debate. Big Data Soc. 3:2 https://doi.org/10.1177/2053951716679679
    [Google Scholar]
  91. Morley J, Cowls J, Taddeo M, Floridi L. 2020. Ethical guidelines for COVID-19 tracing apps. Nature 582:781029–31
    [Google Scholar]
  92. Moses LB, Zalnieriute M. 2020. Law and technology in the dimension of time. Time, Law and Change: An Interdisciplinary Study S Ranchordas, Y Roznai 303–26. Oxford, UK: Hart Publ.
    [Google Scholar]
  93. Naudé W. 2020. Artificial intelligence against COVID-19: an early review. AI Soc. 35:761–65
    [Google Scholar]
  94. OMDDAC 2021. Mapping the data-driven landscape. OMDDAC News Jan. 25. https://www.omddac.org.uk/news/mapping-the-data-driven-landscape/
    [Google Scholar]
  95. O'Connell J, O'Keefe D. 2021. Contact tracing for COVID-19—a digital inoculation against future pandemics. N. Engl. J. Med. 385:484–87
    [Google Scholar]
  96. O'Neil C. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy New York: Broadway Books
    [Google Scholar]
  97. Oswald M, Grace J. 2021. The COVID-19 contact tracing app in England and “experimental proportionality. .” Public Law 2021:27–37
    [Google Scholar]
  98. Otto B. 2011. Organizing data governance: findings from the telecommunications industry and consequences for large service providers. Commun. Assoc. Inf. Syst. 29:3
    [Google Scholar]
  99. Pagallo U. 2012. On the principle of privacy by design and its limits: technology, ethics and the rule of law. European Data Protection: In Good Health? S Gutwirth, R Leenes, P De Hert, Y Poullet 331–46. Dordrecht: Springer Neth.
    [Google Scholar]
  100. Parker MJ, Fraser C, Abeler-Dörner L, Bonsall D. 2020. Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic. . J. Med. Ethics 46:7427–31
    [Google Scholar]
  101. Pila J. 2020. COVID-19 and contact tracing: a study in regulation by technology. Eur. J. Law Technol. 11:2 https://ejlt.org/index.php/ejlt/article/view/782/1010
    [Google Scholar]
  102. Postema GJ. 2022. Law's Rule: The Nature, Value, and Viability of the Rule of Law New York: Oxford Univ. Press
    [Google Scholar]
  103. Prainsack B. 2019. Logged out: ownership, exclusion and public value in the digital data and information commons. Big Data Soc. 6:1 https://doi.org/10.1177/205395171982977
    [Google Scholar]
  104. Ramjee D, Sanderson P, Malek I. 2021. COVID-19 and digital contact tracing: regulating the future of public health surveillance. Cardozo Law Rev. De Novo 2021:101–61
    [Google Scholar]
  105. Regan P. 1995. Legislating Privacy: Technology, Social Values, and Public Policy Chapel Hill: Univ. N.C. Press
    [Google Scholar]
  106. Rhue L, Washington A. 2020. AIs wide open: premature artificial intelligence and public policy. Boston Univ. J. Sci. Technol. Law 26:2353–78
    [Google Scholar]
  107. Richards N, Hartzog W. 2020. A duty of loyalty for privacy law. Washington Univ. Law Rev. 99:961–1021
    [Google Scholar]
  108. Roberts H, Cowls J, Casolari F, Morley J, Taddeo M, Floridi L. 2021. Safeguarding European values with digital sovereignty: an analysis of statements and policies. Internet Policy Rev. 10:3 https://doi.org/10.14763/2021.3.1575
    [Google Scholar]
  109. Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M et al. 2021. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Machine Intel. 3:199–217
    [Google Scholar]
  110. Robin R, Dandis A. 2021. Business as usual through contact tracing app: What influences intention to download?. J. Mark. Manag. 37:17–181903–32
    [Google Scholar]
  111. Rubinstein IS, Good N. 2020. The trouble with Article 25 (and how to fix it): the future of data protection design and default. Int. Data Priv. Law 10:137–56
    [Google Scholar]
  112. Salathé M, Althaus C, Anderegg N, Antonioli D, Ballouz T et al. 2020. Early evidence of effectiveness of digital contact tracing for SARS-COV-2 in Switzerland. Swiss Med. Wkly. 150:w20457
    [Google Scholar]
  113. Saunders MNK, Dietz G, Thornhill A. 2014. Trust and distrust: Polar opposites, or independent but co-existing?. Hum. Relat. 67:639–65
    [Google Scholar]
  114. Savona M. 2020. The saga of the COVID-19 contact tracing apps: lessons for data governance Work. Pap. Ser. RePEc IDEAS http://dx.doi.org/10.2139/ssrn.3645073
    [Google Scholar]
  115. Scassa T. 2020. Designing data governance for data sharing: lessons from Sidewalk Toronto. Technol. Regul. 2020:44–56
    [Google Scholar]
  116. Scott M, Braun E, Delcker J, Manancourt V. 2020. How Google and Apple outflanked governments in the race to build coronavirus apps. Politico May 15. https://www.politico.eu/article/google-apple-coronavirus-app-privacy-uk-france-germany/
    [Google Scholar]
  117. Sharon T. 2020. Blind-sided by privacy? Digital contact tracing, the Apple/Google API and big tech's newfound role as global health policy makers. Ethics Inf. Technol. 23:Suppl. 145–57
    [Google Scholar]
  118. Shaw J, James A, Nayha S, Cassel CK. 2020. Social license for the use of big data in the COVID-19 era. npj Digit. Med. 3:128
    [Google Scholar]
  119. Sitkin SB, Roth NL. 1993. Explaining the limited effectiveness of legalistic “remedies” for trust/distrust. Organ. Sci. 4:367–92
    [Google Scholar]
  120. Smichowski BC. 2019. Alternative data governance models: moving beyond one-size-fits-all solutions. Intereconomics 54:4222–27
    [Google Scholar]
  121. Solove D. 2021. The myth of the privacy paradox. George Washington Law Rev. 89:1–51
    [Google Scholar]
  122. Soltani A, Calo R, Bergstrom C. 2020. Contact-tracing apps are not a solution to the COVID-19 crisis. Brookings TechStream April 27. https://www.brookings.edu/techstream/inaccurate-and-insecure-why-contact-tracing-apps-could-be-a-disaster/
    [Google Scholar]
  123. Susser D, Roessler B, Nissenbaum H. 2019. Online manipulation: hidden influences in a digital world. Georgetown Law Technol. Rev. 4:1–45
    [Google Scholar]
  124. Syrowatka A, Kuznetsova M, Alsubai A, Beckman AL, Bain PA et al. 2021. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. npj Digit. Med. 4:96
    [Google Scholar]
  125. Taylor L. 2017. What is data justice? The case for connecting digital rights and freedoms globally. Big Data Soc. 4:2 https://doi.org/10.1177/2053951717736335
    [Google Scholar]
  126. Taylor L, Floridi L, van der Sloot B, eds. 2017. Group Privacy: New Challenges of Data Technologies Cham, Switz: Springer Int. Publ.
    [Google Scholar]
  127. Thompson N. 2015. Government data does not mean data governance: lessons learned from a public sector application audit. Gov. Inf. Q. 32:3316–22
    [Google Scholar]
  128. Troncoso C, Payer M, Hubaux J-P, Salathé M, Larus J et al. 2020. Decentralized privacy-preserving proximity tracing White Pap. EPFL Lausanne, Switz: https://arxiv.org/ftp/arxiv/papers/2005/2005.12273.pdf
    [Google Scholar]
  129. Tsosie R. 2019. Tribal data governance and informational privacy: constructing “Indigenous data sovereignty. .” Mont. Law Rev. 80:2229–67
    [Google Scholar]
  130. Vatanparast R. 2020. Data governance and the elasticity of sovereignty. Brooklyn J. Int. Law 46:11–38
    [Google Scholar]
  131. Veale M 2020. Sovereignty, privacy, and contact tracing protocols. Data Justice and COVID-19: Global Perspectives L Taylor, G Sharma, AAK Martin, SM Jameson 34–39. Manchester, UK: Meatspace
    [Google Scholar]
  132. Veale M, Binns R, Ausloos J. 2018. When data protection by design and data subject rights clash. Int. Data Priv. Law 8:2105–203
    [Google Scholar]
  133. Viljoen S. 2021. A relational theory of data governance. Yale Law J. 131:2573–654
    [Google Scholar]
  134. Wang C. 2019. An integrated data analytics process to optimize data governance of non-profit organization. Comput. Hum. Behav. 101:495–505
    [Google Scholar]
  135. Wang M, Li S, Zheng T, Li N, Shi Q et al. 2022. Big data health care platform with multisource heterogeneous data integration and massive high-dimensional data governance for large hospitals: design, development, and application. JMIR Med. Inform. 10:4e36481
    [Google Scholar]
  136. Wee A, Findlay MJ. 2021. Digital contact tracing—an examination of uptake in UK and Germany Res. Pap. 10 SMU Cent. AI Data Gov. Dallas, TX: https://ssrn.com/abstract=3915303
    [Google Scholar]
  137. Were V, Moturi C. 2017. Toward a data governance model for the Kenya health professional regulatory authorities. TQM J. 29:4579–89
    [Google Scholar]
  138. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018
    [Google Scholar]
  139. World Health Organ 2017. Contact tracing https://www.who.int/news-room/q-a-detail/contact-tracing
    [Google Scholar]
  140. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G et al. 2020. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ 369:m1328
    [Google Scholar]
  141. Yallop AC, Aliasghar O. 2020. No business as usual: a case for data ethics and data governance in the age of coronavirus. Online Inf. Rev. 44:61217–21
    [Google Scholar]
  142. Zhang Q. 2022. Data matters: a strategic action framework for data governance. Inf. Manag. 59:4103642
    [Google Scholar]
  143. Zhou Y, Jiang H, Wang Q, Yang M, Chen Y, Jiang Q. 2021. Use of contact tracing, isolation, and mass testing to control transmission of COVID-19 in China. BMJ 375:n2330
    [Google Scholar]
  144. Zuboff S. 2020. Caveat usor: surveillance capitalism as epistemic inequality. After the Digital Tornado: Networks, Algorithms, Humanity K Werbach 174–214. Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  145. Zwitter A, Gstrein OJ. 2020. Big data, privacy and COVID-19—learning from humanitarian expertise in data protection. J. Int. Humanit. Action 5:4
    [Google Scholar]
/content/journals/10.1146/annurev-lawsocsci-050520-101947
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