Roboticists and economists have developed a method for estimating the likelihood of future intelligent robots automating jobs and recommending career transitions with lower risks and minimal retraining effort.
When it comes to the future of intelligent robots, the most frequently asked question is: how many jobs will they eliminate? Whatever the answer, the second question is almost certainly: how can I ensure that my job is not one of them?
A team of roboticists from EPFL and economists from the University of Lausanne recently published a study in Science Robotics that provides answers to both questions. They developed a method to calculate which of the currently existing jobs are more likely to be performed by machines in the near future by combining scientific and technical literature on robotic abilities with employment and wage statistics. They have also developed a method for suggesting career transitions to jobs that are less vulnerable and require the least amount of retraining.
“There are several studies predicting how many jobs will be automated by robots, but they all focus on software robots, such as speech and image recognition, financial robo-advisers, chatbots, and so forth. Furthermore, those predictions wildly oscillate depending on how job requirements and software abilities are assessed. Here, we consider not only artificial intelligence software, but also real intelligent robots that perform physical work and we developed a method for a systematic comparison of human and robotic abilities used in hundreds of jobs,” says Prof. Dario Floreano, Director of EPFL’s Laboratory of Intelligent System, who led the study at EPFL.
Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job. Through this advice, governments can support society in becoming more resilient against automation.
Prof. Rafael Lalive
The study’s key innovation is a new mapping of robot capabilities to job requirements. The team investigated the European H2020 Robotic Multi-Annual Roadmap (MAR), a European Commission strategy document that is revised on a regular basis by robotics experts. The MAR describes dozens of abilities that are required of current robots or may be required of future ones, organized in categories such as manipulation, perception, sensing, and human interaction. The researchers examined research papers, patents, and product descriptions to assess the maturity level of robotic abilities, employing a well-known scale for measuring the level of technological development, “technology readiness level” (TRL).
They relied on the O*net database for human abilities, a widely used resource database on the US job market that classifies approximately 1,000 occupations and breaks down the skills and knowledge that are most important for each of them.
The team could calculate the likelihood of each existing job occupation being performed by a robot by selectively matching human abilities from the O*net list to robotic abilities from the MAR document. Assume that a job requires a human to work with millimetre-level precision in movement. Robots excel at this, so the TRL for the corresponding ability is the highest. If a job requires enough such skills, it will be more likely to be automated than one that requires abilities such as critical thinking or creativity.
The result is a ranking of the 1,000 jobs, with “Physicists” being the ones who have the lowest risk of being replaced by a machine, and “Slaughterers and Meat Packers,” who face the highest risk. In general, jobs in food processing, building, and maintenance, construction, and extraction appear to have the highest risk.
“The key challenge for society today is how to become resilient against automation” says Prof. Rafael Lalive. who co-led the study at the University of Lausanne. “Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job. Through this advice, governments can support society in becoming more resilient against automation.”
The authors then developed a method for identifying alternative jobs for any given job that have a significantly lower automation risk and are reasonably close to the original one in terms of the abilities and knowledge required, reducing the retraining effort and making the career transition possible. To see how that method would work in practice, they used data from the US labor force and simulated thousands of career moves based on the algorithm’s recommendations, discovering that it would indeed allow workers in high-risk occupations to shift to medium-risk occupations with a relatively low retraining effort.
The method could be used by governments to determine how many workers are at risk of automation and adjust retraining policies, by businesses to assess the costs of increasing automation, by robotics manufacturers to better tailor their products to market needs, and by the general public to determine the simplest way to reposition themselves on the job market.
