The autopilot problem: driving without experience
Consider a mixed system, in which an automated system is paired with a human overseer. The automated system handles most of the routine tasks, while the overseer is tasked with looking out for errors and taking over in extreme or unpredictable circumstances. Examples of this could be autopilots, cruise control, GPS direction finding, high-frequency trading – in fact nearly every automated system has this feature, because they nearly all rely on humans “keeping an eye on things”.
But often the human component doesn’t perform as well as it should do – doesn’t perform as well as it did before part of the system was automated. Cruise control can impair driver performance, leading to more accidents. GPS errors can take people far more off course than following maps did. When the autopilot fails, pilots can crash their planes in rather conventional conditions. Traders don’t understand why their algorithms misbehave, or how to stop this.
There seems to be three factors at work here:
Firstly, if the automation performs flawlessly, the overseers will become complacent, blindly trusting the instruments and failing to perform basic sanity checks. They will have far less procedural understanding of what’s actually going on, since they have no opportunity to exercise their knowledge.
This goes along with a general deskilling of the overseer. When the autopilot controls the plane for most of its trip, pilots get far less hands-on experience of actually flying the plane. Paradoxically, less efficient automation can help with both these problems: if the system fails 10% of the time, the overseer will watch and understand it closely.
And when the automation does fail, the overseer will typically lack situational awareness of what’s going on. All they know is that something extraordinary has happened, and they may have the (possibly flawed) readings of various instruments to guide them – but they won’t have a good feel for what happened to put them in that situation.
So, when the automation fails, the overseer is generally dumped into an emergency situation, whose nature they are going to have to deduce, and, using skills that have atrophied, they are going to have to take on the task of the automated system that has never failed before and that they have never had to truly understand.
And they’ll typically get blamed for getting it wrong.
Similarly, if we design AI control mechanisms that rely on the presence of a human in the loop (such as tools AIs, Oracle AIs, and, to a lesser extent, reduced impact AIs), we’ll need to take the autopilot problem into account, and design the role of the overseer so as not to deskill them, and not count on them being free of error.