Aiming to minimize alcohol-impaired road fatalities: Utilizing fairness-aware and domain knowledge-infused artificial intelligence

Document Type

Conference Proceeding

Publication Title

2023 IEEE International Conference on Big Data (BigData)

First Page

4205

Last Page

4212

Publisher

IEEE

School

School of Science

Comments

Venkateswaran, T., Islam, S. R., Hasan, M. G. M. M., & Ahmed, M. (2023). Aiming to minimize alcohol-impaired road fatalities: Utilizing fairness-aware and domain knowledge-infused artificial intelligence. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4205-4212). IEEE. https://doi.org/10.1109/BigData59044.2023.10386464

Abstract

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.

DOI

10.1109/BigData59044.2023.10386464

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