Truckers and Electronic Surveillance
Are Truck Drivers’ Complaints Valid?
In December, 2017 a new Electronic Logging Device (ELD) rule was enforced on truck drivers. This new rule requires drivers to digitally track the hours they work. The so-called electronic logging device (ELD) is a “flash drive that plugs into a truck engine control module to track things like whether the engine is running, the odometer, GPS location, and so on” [5]. With the ELD, the regulators hope to gain more control over illegal, overtime driving and prevent drivers from falsifying the records, a common situation for the old pen and paper system. However, the enforcement irritated the drivers. The main concern that the truckers and the Owner-Operator Independent Drivers Association have raised is the inability of the device to account for frequent unexpected situations.
The truckers complain that they have little control over the schedule, the weather, the traffic, and the parking space, etc. None of the situations above can be reflected in the tracked data, yet they influence the amount of hours logged. The tracked data can only reflect the hours that the engine is running, the distance traveled, the location or maybe the trajectory of the trip. Thus the numeric system and sometimes ill-represented tracked data can be unfair and ineffective. With the old pen and paper system, the uncontrollable circumstances can be factored into the records by “cheating the logbook” as a work-around. With the ELD, there seems to be no effective way to reflect their actual working conditions. Uber and Lyft’s drivers have faced the same challenges posted by data-driven evaluations, the passenger-driver rating system. The numeric metrics can sometimes be an inaccurate measure of the services quality as passengers misattribute situations such as system faults over which the drivers have no control to the ratings [4].
Can Some of the Problems be Solved?
The change from a logbook to a tracking device is not sufficient enough for the regulators to gain their desired benefit from the new surveillance system. As pointed out in Beyond the Productivity Paradox, “technology is only one components of an IT investment; there are usually large expenditures on training, process redesign and other organizational changes accompanying a systems investment” [2]. In order to improve the situation, we focus our attention on the ELD data collection process with the help of implicit interaction framework. The thesis of the framework is to study the human-human interaction of a situation or a series of actions involved, and then translate the interactions to the appropriate human-system interactions [3].
The framework characterizes the space of interactions along the dimensions of attentions demand and initiative. On the attention demand axis, the tracked data that are currently being collected are mostly background information about the vehicle such as the distance traveled, the engine running-hours. The lack of foreground information about the truckers, the traffic condition, and the other uncontrollable factors has been a major issue of the regulation. To alleviate this major problem, one can instruct the device to explicitly collect these foreground data.
Next, we turn to the initiative axis. Should the system be proactive or reactive? Clearly, having a device asking for various foreground information constantly is impractical. But neither is having the truckers manually labeling what happened for a several-hours trip data everyday. A simple plug-in flash drive is evidently not enough to account for the task and a more sophisticated logging device is required. In addition to the presumed information, the system also needs a mechanism for the drivers to report any additional uncontrollable factors.
Will Self-driving Truck Replace The Drivers Soon?
Given the current development of self-driving cars, one may want to argue that truck drivers may soon be automated out the job. To fully automate the job, the system must support decision making for various situations that a driver can handle independently. The system will be required to handle smoothly some nuanced and contextualized tasks such handing accidents, interacting with other drivers, and reporting damage or lost, etc. Trips involving complicated conditions such as traffic jams and navigating in a city require social interactions between the drivers and other drivers or the clients. To argue that drivers can be replaced by the system, one must demonstrate the ability of the system to handle all situations that a driver may encounter. However, the current state of technology can only demonstrate self-driving cars given a simple environment without interacting with other drivers or clients. From Ackerman’s [1] point of view, this divide between what we know the fully automated self-driving truck system must support socially and what the current self-driving system can support technically is the social-technical gap. It is unlikely that the drivers will be automated soon given a high complexity of their job involved with interacting with various stakeholders and handling unexpected situations on the road.
References
[1] Mark S. Ackerman. The intellectual challenge of cscw: The gap between social requirements and technical feasibility. Human–Computer Interaction, 15(2-3):179–203, 2000.
[2] Erik Brynjolfsson and Lorin M. Hitt. Beyond the productivity paradox: Computers are the catalyst for bigger changes. Communications of the ACM, 1998.
[3] Wendy Ju and Larry Leifer. The design of implicit interactions: Making interactive systems less obnoxious. Design Issues, 24:72–84, 2008.
[4] Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish. Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15, pages 1603–1612, New York, NY, USA, 2015. ACM.
[5] Nick Stockton. Truckers take on trump over electronic surveillance rules. 2018.