Individuality and Fairness in Public Health Surveillance Technology: A Survey of User Perceptions in Contact Tracing Apps

Abstract

Machine learning algorithms are playing an increasingly important role in public health measures, accelerated by the Covid-19 pandemic. It is therefore vital that machine learning algorithms are applied in ways that are generally considered fair. However, the question of how to define fairness in a public health context is still an open one. In this study, we investigated people's attitudes towards two ways of defining fairness in the context of Covid-19 contact tracing apps. In the first, 'high-individuality' approach, the likelihood of an algorithm asking a person to self-isolate would depend on the person's individual characteristics, such as their risk of spreading the virus through regular contacts. In the second 'low individuality' approach, these individual characteristics would not be used to come to a decision. For each approach, participants rated its fairness, overall quality, and their privacy concerns, and answered questions about basic psychological need satisfaction. Participants rated the high-individuality approach as fairer and better overall compared to the low-individuality approach, despite having greater privacy concerns. Further, we found a strong correlation between the participants' fairness perceptions and their overall impression of the tracking tool. Together, these findings suggest that people prefer individualised approaches in some contexts and perceive them as fairer. However, policy makers should consider the privacy trade-off of employing such measures.

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