Approximately 30% of all traffic fatalities in the United States are attributed to alcohol-impaired driving. This means that, despite stringent laws against this offense in every state, the frequency of drunk driving accidents is alarming, resulting in approximately one person being killed every 45 minutes. The process of charging individuals with Driving Under the Influence (DUI) is intricate and can sometimes be subjective, involving multiple stages such as observing the vehicle in motion, interacting with the driver, and conducting Standardized Field Sobriety Tests (SFSTs). Biases have been observed through racial profiling, leading to some groups and geographical areas facing fewer DUI tests, resulting in many actual DUI incidents going undetected, ultimately leading to a higher number of fatalities. To tackle this issue, our research introduces an Artificial Intelligence-based predictor that is both fairness-aware and incorporates domain knowledge to analyze DUI-related fatalities in different geographic locations. Through this model, we gain intriguing insights into the interplay between various demographic groups, including age, race, and income. By utilizing the provided information to allocate policing resources in a more equitable and efficient manner, there is potential to reduce DUI-related fatalities and have a significant impact on road safety.

翻译：美国约30%的交通死亡事故归因于酒后驾驶。这意味着，尽管各州对此类违法行为均有严格法律，但酒驾事故的发生频率仍令人担忧，平均每45分钟就有一人因此丧生。对"酒驾"（DUI）的指控过程复杂且有时带有主观性，涉及多个阶段，如观察车辆行驶状态、与驾驶员互动以及进行标准化现场清醒测试（SFSTs）。种族定性的偏见已有所显现，导致某些群体或地区接受的DUI测试较少，使得大量实际DUI事件未被发现，最终引发更多死亡。为解决此问题，本研究提出了一种基于人工智能的预测模型，该模型兼具公平感知特性并融合领域知识，用于分析不同地理位置的DUI相关死亡事件。通过该模型，我们获得了关于年龄、种族和收入等不同人口群体之间相互作用的深刻见解。利用所提供的信息以更公平且高效的方式分配警务资源，有望减少DUI相关死亡人数，并对道路安全产生显著影响。