Autonomous driving paper index
Consumer reactions to AI enabled surveillance of drivers. the case of Pay-how-you-drive insurance products
One-line summary
AI-enabled scoring systems that translate behavioural data into financial consequences are spreading across consumer markets, raising the question of how democratic societies should engage with this emerging form of algorithmic governance ( Zuboff, 2019 ).
Engineering notes
Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
AI-enabled scoring systems that translate behavioural data into financial consequences are spreading across consumer markets, raising the question of how democratic societies should engage with this emerging form of algorithmic governance ( Zuboff, 2019 ). Pay-how-you-drive (PHYD) motor insurance offers a particularly tangible setting in which to study how citizens experience such systems. The data are continuous, the financial consequences are direct, and a clear transition is now underway from reward-only schemes to schemes that also sanction risky driving. This paper examines that transition empirically. We draw on 19 semi-structured interviews with experienced users of HUK-COBURG’s reward-based Telematik Plus product, all aged 30 to 50, and analyse their reactions to a stimulus describing a sanction-based extension. Following Holland and John (2023) , we use the term AI-enabled to refer to the ensemble of machine learning techniques that are at the heart of the HUK-COBURG scoring pipeline. The case is informative because HUK-COBURG’s reward-based Telematik Plus product represents a relatively well-governed implementation in the German market. Qualitative content analysis yields six dimensions of user concern: transparency and complexity, acceptability of risk factors, financial distress, prompting and sanctioning, privacy and data insecurity, and technical issues. We identify what we call a perception-implementation gap. Even in a system designed to exclude socio-demographic discrimination, users perceive themselves to be disadvantaged along precisely those excluded dimensions. We argue that this gap is a structural feature of algorithmic sanctioning more generally, rather than a defect of any single implementation, and we draw out implications for the governance of AI-enabled scoring systems in transportation and beyond.
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