Robotruckers: The Double Threat of AI for Low-Wage Workers

May 5, 10:30 am, Donald Bren Hall

Much attention has been paid to the risk AI poses to employment, particularly in low-wage industries. Long-haul truck driving is perceived as a prime target for such displacement, due to the fast-developing technical capabilities of autonomous vehicles (many of which lend themselves to the specific needs of truck driving), characteristics of trucking labor, and the political economy of the industry. In most of the public rhetoric about the threat of the self-driving truck, the trucker is seen as a displaced party. He is displaced both physically and economically: removed from the cab of the truck, and from his means of economic provision. The robot has replaced his imperfect, disobedient, tired, and inefficient body, rendering him redundant, irrelevant, and jobless. But the reality is more complicated. The intrusion of automation into the truck cab certainly presents a threat to the trucker, but the threat is not solely or even primarily experienced, as it is so often described, as displacement. The trucker is still in the cab, doing the work of truck driving-but he is joined there by intelligent systems that monitor his body directly. As more trucking firms integrate such technologies into their safety programs, truckers are not being displaced by intelligent systems so much as they are experiencing the emergence of intelligent systems as a compelled hybridization, a very intimate incursion into their work and bodies. This talk considers the dual, conflicting narratives of job replacement by robots and of bodily integration with robots, to assess the true range of AI’s potential effects on low-wage work.

Karen Levy is an associate professor of Information Science at Cornell University and associated faculty at Cornell Law School. Her new book, Data Driven: Truckers, Technology, and the New Workplace Surveillance, offers a behind-the-scenes look at how surveillance and automation are affecting the trucking way of life.

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