How AI and Robot Adoption Affects Worker Safety
The influence of artificial intelligence (“AI”) on the American workplace is, in many industries, already being felt. This trend is destined to continue, and strengthen exponentially, as AI technology develops and its uses increase in ubiquity as it becomes more and more sophisticated, usable and understood by the business community. One fairly obvious way in which AI is very likely to disrupt workplaces around the country is through the adoption of sophisticated, intelligent robots and robotic systems that are capable of performing tasks once reserved for human workers exclusively. The extent to which AI will permeate all types of workplaces is yet unknown, but it appears likely to affect nearly every worker in every industry nationwide as the technology continues to develop in astounding ways and at a fast pace.
One interesting question is whether, and to what extent, the adoption of AI-powered tools in the workplace will affect worker safety and incidences of worker injuries suffered on the job. It is almost certain that AI will have an affect on worker safety, and that affect is very likely to be positive, though there are risks that, in some cases, it may raise new worker safety risks that have yet to be anticipated. In this article, I will consider a few examples of how AI-powered tools might be used to improve worker safety, and reduce incidences of on-the-job worker injuries by using the construction trades (and specifically, the job of an ironworker) as an example.
On today’s construction job sites, ironworkers are tasked with unloading and installing the large structural steel beams that form the internal skeleton of a building. Their job is particularly dangerous, as they are working with very large pieces of heavy equipment (cranes, forklifts, etc.) and very heavy steel beams and other similar large structural components of a building’s frame. Serious personal injury accidents involving falling objects and falls from great heights are a serious risk faced by ironworkers every single day on the job. Unfortunately, many ironworkers suffer serious debilitating injuries on the job, often due to failures to provide adequate safety equipment that is capable of protecting them from the serious hazards inherent in their work.
One interesting way in which AI could be implemented to protect ironworkers from serious construction accident injuries is by creating a database of all of the safety equipment in the stockpiles of construction companies and monitoring the service life of each piece of equipment to make sure that, at the end of what should be each piece of equipment’s useful lifespan, the piece of equipment is taken out of use and replaced by a new piece of equipment that is not worn out and is thus capable of protecting the ironworker from personal injury.
Each piece of safety equipment provided to ironworkers by a particular construction company could be tagged with a bar code, and scanned into a construction company’s computer system. The computer system could be programmed to record information such as the date of purchase of the particular piece of safety equipment, the expected useful life span of the equipment based upon available statistics, how many hours a particular piece of safety equipment has spent in service on a job site, and any repairs that have been performed on the equipment. Every time the piece of equipment was taken out into the field by an ironworker, was damaged and required a repair, had a part replaced, or any other similar type of event which presumably would have an effect on the future lifespan of the equipment, the bar code on the equipment would be scanned to record these events.
Over time, the computer program would also learn how often a particular type of safety equipment used by a particular construction company is expected to malfunction or require a repair, how particular types of repairs or damage may affect the lifespan of a particular piece of safety equipment, and other data with respect to the useful lifespan of the equipment. By cross-referencing all of these recorded events against information available from the manufacturer of the equipment regarding its useful lifespan, the AI program could provide warnings to the ironworkers regarding their safety equipment such as predictions of whether particular repairs are needed, or whether the equipment should be taken out of service or swapped for a different piece of equipment.
Take the example of a safety harness and lanyard, safety equipment designed to prevent a construction worker from falling when he or she is working at a great height. Ironworkers often work at great heights, standing atop scaffolding or large steel beams, during the erection of the frame of a building that is under construction. Under current construction safety regulations, and pursuant to simple common sense, ironworkers always wear a safety harness – a device which is generally made of super-strong composite materials and which is designed to support the full weight of the ironworker in case he or she falls off of a work platform and which is strapped across their body – whenever they have to work a significant distance off of the ground (which, given the nature of the work performed by ironworkers, is just about every day). Ironworkers then use a “lanyard” – a fancy word for a special kind of rope – to tie their safety harness to a secure point on the structure to ensure that they are supported and prevented from falling more than a few feet should an accident occur. These important safety devices are generally provided to ironworkers by their employers on job sites to ensure compliance with applicable rules and regulations pertaining to worker safety, as well as to protect workers from suffering serious injuries on the job.
An AI-driven system could track the day-to-day use of the harness and lanyard by tracking the number of hours per day that the harness is in use in the field, the identities of the specific ironworkers using the equipment, whether during the course of the day a worker using the equipment has any accidents (such as, say, by falling off of a beam and being caught by the harness and lanyard and thus prevented from falling to the ground), whether any components of the equipment are noted to have broken, and whether any repairs are performed regarding the equipment. The system would then, over time, be able to learn more and more about a particular harness and lanyard, and make predictions to the construction company that owns this equipment about things such as when the equipment is likely in need of repair, what types of repairs are likely to be required at particular times, and when a particular piece of equipment should be retired from service and replaced with entirely new equipment.
By tracking and learning about the safety equipment, the AI program could help predict potential future safety equipment failures, and protect ironworkers from serious personal injury due to a construction accident caused by a failure of a safety device. The ability of AI-powered software to aggregate and analyze data, and learn from trends that appear in the data over time, would allow construction companies to better protect ironworkers by being able to predict safety equipment failures – perhaps the leading cause of serious construction accidents and personal injury on construction job sites – before they even occur.
