How Technology Is Enhancing Traffic Law Fairness – Guest Post
Traffic law is intended to treat all people on the road fairly, but human bias and subjectivity sometimes stand in the way of this goal. One Stanford-led study involving 95 million traffic stop records found, for instance, that Black drivers were stopped more than White drivers during daylight, but less often after sunset, when race was less visible. Another study by Cornell researchers, meanwhile, came to similar findings, in that speed camera tickets were proportional to local driver percentages, yet police stops revealed a strong overrepresentation of Black drivers. While legal reform can help curb bias, so, too, can technology. In current times, technologies ranging from AI to automated enforcement are helping to reduce subjectivity and work on what matters most: road safety, accurate evidence, and efficiency.
AI and Data Analytics
Artificial intelligence (AI) has revolutionized the legal industry, since AI for legal research can help save time and improve the management of issues such as document volume, multijurisdictional complexity, live negotiations, and budget pressures. While human legal professionals remain important for citation-level review, AI is unique in its ability to analyze data and identify patterns and inconsistencies—functions that are key to spotting bias. For instance, by analyzing large datasets, AI can identify disproportionate legal enforcement, procedural irregularities, recurring dismissal trends, and flawed ticketing practices. It is easy to see how this can help lawmakers proactively combat bias. For instance, AI can reveal that some neighborhoods receive disproportionately more fines than others, or that some officers issue more tickets than their peers. Action can then be taken both legislatively and procedurally to ensure fair treatment of all persons at the wheel.
Relying on Automated Enforcement
AI is only one of many technologies being used to reduce subjectivity. One of the most important strategies to achieve this goal is the use of speed and red-light cameras, which apply rules fairly and reduce reliance on officer discretion. Data can be obtained from numerous sources, including dashcam footage, GPS and telematics data, and timestamped images. This evidence can be used in a wide range of cases, including those in which fines are contested or disputes arise between two drivers. One recent academic case study on automated traffic law enforcement in Israel compared camera-issued speeding tickets with officer-issued tickets specifically to examine bias and benchmark problems. The findings showed that objective camera records can be used to separate actual speeding from discretionary enforcement. New AI-assisted camera systems are even more effective, as they can identify issues such as seatbelt use, phone use, and violations, which can help establish guilt or fault.
Limitations on Subjectivity
Although technology, such as cameras, is generally more objective than human beings, it does have its own limits. For instance, cameras may be placed in specific neighborhoods, which impacts fairness. As noted by the Governors Highway Safety Association, all decisions about safety camera programs (including public engagement during the planning stage, where cameras are placed, and how fines are structured) should be viewed through an equity lens. Jurisdictions should also share data to inform decision-making when considering whether to create an automated enforcement program. Members of the public should know when and where cameras are deployed, so drivers aren’t caught by surprise. Cameras should be installed in locations with crash, injury, or fatality data that justifies their use.
The Utility of Speed Calming
Engineering and design strategies can help slow traffic speeds to improve safety, especially in built-up areas, where schools are located or where pedestrians and cyclists abound. Simple devices that can be employed include physical blocks (such as speed bumps, cushions, or tables) that raise the road surface, forcing drivers to brake. Narrower lanes, curves, raised crosswalks, and speed-activated rumble strips can also help, as can visual cues, including painted lane markings, signage, and textured pavement that signals slower speeds.
Endnote
Technology is helping ensure fairness on the roads in many ways. For one, technologies such as AI are helping identify patterns that indicate bias. Secondly, devices such as speed cameras can help automate enforcement, so that all drivers are viewed through the same lens. Finally, simple changes to road design, combined with physical devices such as speed bumps, can force drivers to slow down, thereby reducing accidents.