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Algorithmic Vulnerability in Deploying Vaccination Certificates in the European Union and China

Published online by Cambridge University Press:  18 August 2021

Janet Hui XUE*
Affiliation:
Senior Research Fellow, Käte Hamburger Kolleg/Centre for Global Cooperation Research, University of Duisburg-Essen, Duisburg, Germany; email: janet.xue@uni-due.de.
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Abstract

This article further develops the concept of algorithmic vulnerability. The analysis is built on empirical evidence of the Chinese Health Code System (HCS), compared to similar plans for the “COVID-19 Certificate” in the European Union (EU). Implementing the HCS has shown two-sided regulatory implications: improving social protection (a national strategy, a common mutual-recognition standard, scaled-up public–private cooperation) and increasing risks of social exclusion (non-digital and digital forms of vulnerability). This article argues that algorithmic vulnerability is caused by mismatches between biased databases, unfairly pre-designed algorithms and dynamically changed risk groups in reality in the context of COVID-19 vaccination. It contributes a framework for deploying plans for digital certificates in the EU concerning minimising the social risks associated with algorithmic vulnerability. The framework consists of (1) reinforcing existing vulnerability inherited from non-digital society (eg caused by intersectional factors of race/ethnicity, gender, age and health) and (2) introducing new forms of vulnerability generated by algorithm design and implementation (eg excluding the risk groups of individuals who are un/mis/overrepresented in the databases, such as those defined by nationality plus COVID-19 status).

Information

Type
Symposium on COVID-19 Certificates
Creative Commons
Creative Common License - CCCreative Common License - BY
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 in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press