Narrow surveillance: Preserving privacy in AI-based safety monitoring in public spaces

This article examines AI-based video surveillance systems in public spaces. We investigate a case of AI-based camera technology designed to detect falls into the water and prevent drowning accidents at the harbor front of a large Scandinavian city. This case confronts a common state-citizen tension between safety enhancement and privacy intrusion, and illustrates how involved actors strived to balance this through technological design choices such as thermal imaging. Building on these empirical insights and approaches that seek to deal with the complex safety-privacy tension, we draw out the notion of narrow surveillance as a general approach to safety monitoring in public spaces, aiming to minimize privacy intrusion through three criteria: (i) non-identification, (ii) purpose limitation, and (iii) alignment of interests. By implication, our study contributes new insights to interdisciplinary debates in surveillance studies by providing a framework for evaluating current AI-based surveillance systems and guiding future implementations of such systems.