Aiming to Minimize Alcohol-Impaired Road Fatalities: Utilizing
  Fairness-Aware and Domain Knowledge-Infused Artificial Intelligence
Tejas Venkateswaran Sheikh Rabiul Islam Md Golam Moula Mehedi Hasan Mohiuddin Ahmed
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.