Imagine that you
have just purchased the latest self-driving car and you are on your way to work.
The car is in control because the route is familiar and the car has all the
modern sensing and decision-making functions. You assure yourself that you can
take control of it at any time. But if there is a sudden dangerous situation—someone
darts into the road or another vehicle is moving erratically—will you do so? I
venture that the answer is yes if you are an experienced driver of
old-fashioned cars that need to be driven—your instinct will be to rely on your
own skills to avert problems. If you learned to drive after the self-driving
technology was the standard? I’m guessing the answer might be no. And the
answer could be very consequential.
Research by Matthew Beane published in Administrative Science Quarterly explores a situation with
similarities to this one. He examined what’s happening in operating rooms
around the world, where robots are increasingly used to perform surgeries. At
least for the moment, the most effective robots do not make their own
decisions, but they can be controlled quite effectively by a single surgeon at
the control panel. That single surgeon often no longer needs a team performing
different surgical tasks for the operation to go smoothly. And that surgeon is
not likely to give up the controls during a challenging operation to a medical
intern who needs to learn how to perform the surgery.
If you’re the
intern, that leaves you in a position of scrambling for any opportunity you can
get to practice with the new technology so you can gain the necessary skills to
guide the robots. At the same time, you aren’t (as often, at least) part of a
surgical team performing traditional surgeries, so you aren’t building the
skills that are essential backups if something goes awry during the robotic surgeries
you’re observing.
Surgeons don’t live forever. What happens when they are replaced by the interns who are currently receiving less hands-on training than they had? I guess we’re all going to find out.
Because
the robots were only supposed to be used by the most qualified surgeons, only
two approaches to learn were open to the residents. They could learn when the
surgeon was not there, or they could learn as a favor from the surgeon.
Learning when the surgeon was away could be done before they even became
residents at the hospital, if they were lucky enough to be in a place with the
right equipment and surgeons who allowed them to practice. It could also be
done by watching recordings of earlier surgeries, though this type of training
only showed what the robot did, not how to use the actual controls.
The
other option is to learn from the surgeon during an actual operation. Usually
that means getting control of the robot during the less critical part of the
operation, and being watched over by the surgeon at every step. This approach
is fast and accurate, but it also means that any mistake becomes very visible,
and usually leads to the surgeon taking over the controls right away. It is the
best way of learning, but also the scariest one. The risk was real because
residents would be compared based on very few operations, and surgeons would
prefer to continue working more with those who had done best – whether it was
because of skill or because of luck.
The
difficulty of learning when robotic equipment takes over a production process
but still needs human control is not just in hospitals. The self-driving car is
an example, and there are many examples of more subtle processes. The software
that recommends whether an applicant can get a mortgage or not, and at what
interest rate, is an invisible robot that can be controlled by the bank
employee. But as the software gets smarter and the employees get less training,
they have less foundation for checking its work, or even to be employed at all.
Robots are smart, but organizations need to use them smartly.
There is also a TED talk on this research.
