方向盘背后的偏见：自动驾驶系统的公平性测试
Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
AI总结：
本文对自动驾驶系统中的自动行人检测进行公平性测试，发现儿童未被检测比例比成人高20.14%且夜间存在性别偏见，同时挑战了公平性与性能权衡理论。
摘要：
本文对自动行人检测进行了公平性测试，这是自动驾驶系统中一个关键但尚未充分探索的问题。我们在大规模真实世界数据集上评估了八种最先进的基于深度学习的行人检测器在不同人口群体上的表现。为了实现彻底的公平性测试，我们为数据集提供了广泛的标注，最终得到8,311张图像，包含16,070个性别标签、20,115个年龄标签和3,513个肤色标签。我们的发现揭示了显著的公平性问题，特别是与年龄相关的问题。与成年人相比，未被检测到的儿童比例高出20.14%。此外，我们探讨了各种驾驶场景如何影响行人检测器的公平性。我们发现行人检测器在夜间表现出显著的性别偏见，这可能加剧了女性夜间外出安全担忧这一普遍的社会问题。此外，我们观察到行人检测器在特定驾驶条件下既能表现出更好的公平性，又能展现出更优越的性能，这挑战了公平性文献中广泛认可的公平性与性能权衡理论。我们公开发布代码、数据和结果，以支持未来关于自动驾驶公平性的研究。
英文摘要：
This paper conducts fairness testing of automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems. We evaluate eight state-of-the-art deep learning-based pedestrian detectors across demographic groups on large-scale real-world datasets. To enable thorough fairness testing, we provide extensive annotations for the datasets, resulting in 8,311 images with 16,070 gender labels, 20,115 age labels, and 3,513 skin tone labels. Our findings reveal significant fairness issues, particularly related to age. The proportion of undetected children is 20.14% higher compared to adults. Furthermore, we explore how various driving scenarios affect the fairness of pedestrian detectors. We find that pedestrian detectors demonstrate significant gender biases during night time, potentially exacerbating the prevalent societal issue of female safety concerns during nighttime out. Moreover, we observe that pedestrian detectors can demonstrate both enhanced fairness and superior performance under specific driving conditions, which challenges the fairness-performance trade-off theory widely acknowledged in the fairness literature. We publicly release the code, data, and results to support future research on fairness in autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
