Long-haul truckers are monitored, scanned, and tracked, forced to ride along with silent spies.
This article appears in the December 2022 issue of The American Prospect magazine. Subscribe here.
Data Driven: Truckers, Technology, and the New Workplace Surveillance
By Karen Levy
Princeton University Press
I have a particular fondness for long-haul truckers.
My broken-down car in Newfoundland in the early ’90s led to the accidental discovery of the world of ride-alongs. A network of truckers drove me 1,400 miles from St. John’s to Boston. The drivers and I swapped stories to take the edge off the loneliness; in return, each one took me as far as they could. When their route diverged from my destination, they would radio around to find someone trustworthy who was going my way, or drop me off at a truck stop where, over coffee, a waitress would steer me toward responsible drivers.
I listened as the drivers traded tips and frustrations with others on the highway. I joined the drivers for endless truck stop meals, as they kept their hours within the federally mandated daily limit. I slept in bunks in the back of the cab.
In subsequent years, I returned occasionally to riding along—including a memorable trip from North Carolina to Wisconsin one frozen February—and on these trips I learned about loneliness, dogs, truck decoration, and different approaches to stimulants. I gained an appreciation for the difficulty of the job and the thoughtfulness and quirkiness of so many drivers.
So when I heard about Cornell information science and law professor Karen Levy’s new book on long-haul trucking, I raced to get a copy.
Data Driven: Truckers, Technology, and the New Workplace Surveillance does not disappoint. It is an exceptional exploration of how new rules and AI are transforming modern long-haul trucking, and how almost everyone who talks about the future of robots and work is getting it wrong.
In Levy’s book, we find the once-autonomous cowboys of the highway strapped to monitors and monitoring vests, scanned with intelligent cameras, and tracked while they drive, forced to ride along with silent spies instead of hitchhiking kids like me. Truckers already have compressed joints and lower-back pain while carting food, medicine, and other goods around the country. Now, having taken on high-stress conditions, their heart rate and health are treated with microscopes and suspicion. Their pay, which collapsed after deregulation in the 1980s, is buried at the bottom of the American median. Turnover rates approach 100 percent annually.
Is it any wonder that truckers are angry and despairing?
Long-haul trucking is a brilliant topic, because it sits at the intersection of many different economic and social trends. Truckers’ workplace doubles as their home for several days a month, like a growing number of white-collar workers. Like Uber drivers and other gig workers, they are not paid for the time they prepare for and wait for work. And drivers are becoming among the most intimately monitored workers in America, with physical surveillance that echoes that of Amazon workers.
But there’s more: In trucking, a hidden driver of modern surveillance may in fact be government. As we learn in Data Driven, it isn’t always so clear-cut where government surveillance ends and where corporate surveillance begins.
Levy explodes the false image of automation that is embedded in so much of both tech utopianism and dystopianism.
IN 2017, THE U.S. DEPARTMENT OF TRANSPORTATION (DOT) decided to do something about terrible accidents in long-haul trucking. The problem, as DOT saw it, was that drivers were evading the “hours of service” rules that limited how long they could drive. According to the rules, a driver should take ten hours off between shifts, work no more than 60 hours in any seven-day period, and fit all their 14 hours of work time in a consecutive slot. For example, if you start driving at 7:00 a.m., you can drive up to 11 hours for the day, but none of those driving hours can come after 9:00 p.m., or that would violate the 14-hour rule.
Hours-of-service rules are designed to keep weary drivers off the road, by limiting consecutive hours, total hours worked in one day, and total hours worked over a week-long period. (The rules were a little different when I was riding along, but the procedure was the same; drivers would log their hours on paper.) However, DOT became concerned that drivers were fudging the numbers. So the federal solution was to mandate a digital ride-along to stop the evasion. Starting in 2017, DOT required that all trucks be equipped with electronic logging devices (ELDs). The paper logs were tossed out the window, along with an enormous amount of discretion.
Levy, who had already spent years interviewing drivers at truck stops, chronicled the regulatory change as a natural experiment that reveals what happens to an industry when the government starts requiring a particular surveillance device.
It was more transformational than you might think. What started as federal safety regulation quickly became an entry point for general corporate regulation.
The ELDs changed hours-of-service enforcement from manual to digital, which also changed the management of drivers from after-the-fact to real-time. Every moment spent idling is a blinking red data point at headquarters now. Drivers get pinged by their superiors when a truck stop break goes longer than expected. When a driver is tired, or encounters bad weather, they no longer feel free to make a decision to sleep or take a break, because the tracking technology signals to headquarters that the truck is idle. (Levy shares a chilling exchange where headquarters barrages a sleeping driver who insists there is a storm with its own weather analysis.) Almost a third of drivers surveyed reported being woken up with instructions to drive, and two-thirds said they were told to drive longer.
ELDs also created a new structure of ongoing data collection, which positions employers to be able to more precisely set, change, and withhold wages. They use the data to push for competition between drivers, shame drivers who are less productive, and discipline them. (It isn’t clear how much this has led to more personalized wages, but the technology certainly enables it.)
Finally, the ELDs acted as a kind of gateway drug for micromanagement, and have been followed by an array of devices attached to practically every part of the human body to “augment,” regulate, and control the bodies doing the work at the most minute level. It’s a culture of both surveillance and AI/human intertwining.
One company has a prototype for a vest that will stop the truck if it senses bodily fatigue. Trucking tools already in circulation include a “SmartCap” that monitors brain waves for weariness. Robotic devices, Levy argues, operate as a boss peeking over your shoulder at all times. As Levy writes, “AI in trucking today doesn’t kick you out of the cab; it texts your boss and your wife, flashes lights in your eyes, and gooses your backside.”
JAMES BORCHUCK/AP PHOTO
The threat of traffic accidents is being used to justify invasions of trucker privacy.
Safety was the ostensible purpose, but the “cyborg” world Levy discovers is not safer. After the ELD rollout, trucking safety stayed the same or declined: There were even more crashes in some parts of the industry. Levy argues that the micromanagement is not just neutral, but dangerous, because it removes the discretion of skilled drivers. Experienced drivers are quitting. Meanwhile, surveillance is a known driver of stress and cortisol levels, which decreases motor skill sensitivity. The loss of autonomy drives depression, burnout, and quitting, increasing the danger posed by the unskilled replacements.
It’s hard to turn off a camera once you know you can use it to reward and punish, whether it is to spy on babysitters, nurses, or in this case, truckers. Corporate bosses will use the threat of a small car getting crushed by tons of metal to argue that privacy precautions seem secondary. Surveillance justified by safety has an accelerating logic to it, where the gaps in knowledge that used to seem just human and normal suddenly appear as vast, dangerous lacunae. Inasmuch as the goals of safety keep failing, the logic of the arrangement will tend toward invasiveness, instead of a reconsideration of the value of spying.
What we’ve learned from the ELD experiment is very important for policy: Surveillance just doesn’t work that well as a safety tool. Yes, it gives the company more information. But as the saying goes, you can’t fatten a cow by weighing it; you can’t improve safety by forcing drivers to have their rest stops recorded.
As Levy shows, the problem of economic incentives can’t be solved by surveillance. The two million long-haul truckers in this country are paid per mile, which creates an embedded incentive to overwork. To add insult to injury, they are not paid when they are waiting to unload or looking for parking. Drivers lose as much as $1.3 billion in earnings each year just while waiting for an unloading dock to open up. Spending what can be hours without pay creates more incentive for drivers to make up that time by speeding.
If we really want fewer crashes, we have to pay drivers better. “The problem’s roots are economic,” Levy argues, but the “solution on which hopes have been pinned is not economic, but technological. Rather than change the underlying conditions that give rise to lawbreaking, regulators and companies have tried to make it more difficult for truckers to falsify their time records.”
IF YOU HAVEN’T FIGURED IT OUT ALREADY, the book is about far more than truckers. Levy, by taking it from public debates to reality, explodes the false image of automation that is embedded in so much of both tech utopianism and dystopianism.
For the tech utopians, robots will take over the hard human tasks and release workers from backbreaking work; for tech dystopians, robots will take away essential human jobs, destroying the middle class and worker power. What Levy points us to instead, holding up truckers as the live experiment, is “human/machine hybridization.” She reveals that there is no clear line between AI replacing humans and humans reaping the benefits of new technologies.
I was at a conference recently where a panelist gave a fascinating presentation on seed-sorting, where AI aided humans who were sorting different seeds in an agricultural operation. Left unsaid, but implicit in the presentation, was the notion that augmented tasks empower the human. That is likely true in many cases: The augmentation of having a tool expands, instead of diminishes, the human wielding that tool. But sometimes, the augmentation may look a lot more like the “augmented” truck driver, who is scolded and watched, and driven to sky-high cortisol levels.
The modern neo-Brandeisian revolution, led by Tim Wu, Barry Lynn, Lina Khan, and others, is often understood as being largely about antitrust and anti-monopoly. But it is also a reality revolution, an insistence on exploring the world as it is, as opposed to how it is modeled.
It is founded, in part, on the recognition that economics as currently constructed cannot make sense of power, and that law has become too infected by the abstractions of economics. In our world of AI and digital spying, surveillance reaches new workplaces, unearths new data, and brings new forms of analysis; it creates new forms of power.
Louis Brandeis did not just insist on the dangers of centralized power; he insisted on the facts on the ground. In the 1908 case Muller v. Oregon, where a laundry owner challenged a state law that restricted work hours for women, Brandeis presented his famous “Brandeis brief” to the Supreme Court. It included 110 pages of testimony about the experience of brutally overworked women, and just two pages of law.
Part of what makes Levy’s book so refreshing—and important—is that the methodology is decidedly not that of an economist. Levy has a dual appointment at Cornell, both in the law school and as an associate professor of information science, and she is also trained as a sociologist. The triple vision lights up her writing. She asks questions, and uncovers stories that only come from listening, watching, and learning, from testimonies and history, to tell a story about brutally overworked men, who are rewarded for their labor by being treated like suspects.
