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

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