Economists develop a method to estimate the automation of tasks by robots

A team of roboticists from the Ecole Polytechnique Fédérale de Lausanne and economists from the University of Lausanne have developed a new method to calculate which existing jobs are most at risk of being automated by machines in the near future.

The study was published in Scientific robotics.

The team also developed a method to suggest career transitions to jobs less likely to be automated and with the smallest retraining efforts.

Professor Dario Floreano is director of EPFL’s Intelligent Systems Laboratory and lead author of the study.

“There are several studies predicting how many jobs will be automated by bots, but they all focus on software bots, such as speech and image recognition, financial robo-advisors, chatbots, etc. “, says Professor Floreano. “Furthermore, these predictions fluctuate wildly depending on how job requirements and software capabilities are assessed. Here, we consider not only artificial intelligence software, but also highly intelligent robots that perform physical work, and we have developed a method for a systematic comparison of human and robotic capabilities used in hundreds of jobs.

Develop the method

The team was able to map the robot’s capabilities to the job requirements, which was the main breakthrough of the study. They reviewed the H2020 European Robotics Multi-Annual Roadmap (MAR), which is a European Commission strategy document periodically reviewed by robotics experts. The MAR details the capabilities required of current robots or likely to be required by future robots. These are organized into categories like manipulation, perception, and interaction with humans.

The team analyzed numerous research papers, patents and robotic product descriptions to assess the level of maturity of robotic capabilities. They relied on the “Technology Readiness Level” (TRL), which is a scale for measuring the level of technological development.

For human capabilities, the researchers used the O*net database, which is a resource database widely used in the US labor market. It categorizes around 1,000 occupations while detailing the skills and knowledge needed for each.

The team first selectively matched human abilities from the O*net list to robotic abilities from the MAR document, which allowed them to calculate the likelihood that every existing task would be performed by a robot in the future. . If a robot is good at a job, the TRL is higher.

Job Ranking

After performing this analysis, the result was a ranking of 1,000 jobs. One of the lowest on the list was “Physicists”, while “Meat Packers” was one of the highest. Jobs in food processing, construction and maintenance, and construction presented the highest risks.

Professor Rafael Lalive co-led the study at the University of Lausanne.

“The main challenge for society today is how to become resilient in the face of automation,” says Professor Lalive. “Our work provides in-depth career advice to workers facing high risks of automation, enabling them to take on more secure jobs while reusing many of the skills learned in the old job. With these tips, governments can help society become more resilient to automation.

The authors have created a method to find a given job an alternative employment with a significantly lower risk of automation. These jobs were also close to the old one in terms of skills and knowledge required, which keeps retraining efforts to a minimum.

This new method could be used in different ways. On the one hand, governments can use it to gauge how many workers might face automation in the future. This would help tailor recycling initiatives and policies accordingly. Companies could also use it to analyze the costs associated with automation.

All of this work has been translated into an algorithm that can predict automation risk for hundreds of jobs while suggesting career transitions.

You can find the algorithm publicly available here.

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