How to Cite
Abstract
Artificial intelligence (AI) responsible for decision-making requires the capacity for explainability to help regulate both the act and the decision made on the basis of this act. This research identifies AI architectures that are able to generate a social and human benefit in decisions enabled by AI techniques and takes as its focus autonomous vehicle (AV) and connected vehicle (CVs) network council decisions to control specific autonomous vehicle functionalities within competing privacy and real-time security paradigms. The fundamental tensions of these paradigms can be characterized as privacy versus autonomy. Up to now, there has been a paucity of research on governance of the capacity of connected and autonomous vehicles to share and learn from real-time information communicated by other connected and autonomous vehicles and infrastructure as part of interactions in Dynamic Road Traffic Network problems.
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