Ethical AI for Automated Bus Lane Enforcement

Abstract

There is an explosion of camera surveillance in our cities today. As a result, the risks of privacy infringement and erosion are growing, as is the need for ethical solutions to minimise the risks. This research aims to frame the challenges and ethics of using data surveillance technologies in a qualitative social context. A use case is presented which examines the ethical data required to automatically enforce bus lanes using camera surveillance and proposes ways of minimising the risks of privacy infringement and erosion in that scenario. What we seek to illustrate is that there is a challenge in using technologies in positive, socially responsible ways. To do that, we have to better understand the use case and not just the present, but also the downstream risks, and the downstream ethical questions. There is a gap in the literature in this aspect as well as a gap in the actual thinking of researchers in terms of understanding and responding to it. A literature review and detailed risk analysis of automated bus lane enforcement is conducted. Based on this, an ethical design framework is proposed and applied to the use case. Several potential solutions are created and described. The final chosen solution may also be broadly applicable to other use cases. We show how it is possible to provide an ethical AI solution for detecting infringements that incorporates privacy-by-design principles, while being fair to potential transgressors. By introducing positive, pragmatic and adaptable methods to support and uphold privacy, we support access to innovation that can help us mitigate current emerging risks.