Ethics, AI, mass data and pandemic challenges: Responsible data use and infrastructure application for surveillance and pre-emptive tracing post-crisis - FINDLAY, Mark (Institutional Knowledge at Singapore Management University, 2020)
As the COVID-19 health pandemic rages governments and private companies across the globe are utilising AI-assisted surveillance, reporting, mapping and tracing technologies with the intention of slowing the spread of the virus. These technologies have the capacity to amass personal data and share for community control and citizen safety motivations that empower state agencies and inveigle citizen co-operation which could only be imagined outside such times of real and present danger. While not cavilling with the short-term necessity for these technologies and the data they control, process and share in the health regulation mission, this paper argues that this infrastructure application for surveillance has serious ethical and regulatory implications in the medium and long term in relation to individual dignity, civil liberties, transparency, data aggregation, explainability and other governance challenges. To conduct this analysis, the paper presents the Singapore and China case studies, and offers a comparative description based on the many more initiatives implemented worldwide in order to understand the purpose, goal and risk of these infrastructures. The analysis looks at data protection and citizen integrity and reflects on other surveillance methods outside the health context, such as initiatives implemented in the financial sector, where similar challenges have arisen.