Ironies of Automation
Ironies of Automation
In 1983 I was just joining the US Air Force, and still deeply involved in electronics (rather than computers). I had written a few programs in BASIC and assembler on a COCOII with a tape drive, and at least some of the electronics I worked on were used vacuum tube triodes, plate oscillators, and operational amplifiers. This was a magical time, though—a time when “things” were being automated. In fact, one of the reasons I left electronics was because the automation wave left my job “flat.” Instead of looking into the VOR shelter to trace through a signal path using a VOM (remember the safety L!) and oscilloscope, I could sit at a terminal, select a few menu items, grab the right part off the depot shelf, replace, and go home.
Maybe the newer way of doing things was better. On the other hand, maybe not.
What brings all this to mind is a paper from 1983 titled The Ironies of Automation. It might often seem, because of our arrogant belief that we can remake the world through disruption (was the barbarian disruption of Rome in 455 the good sort of disruption, or the bad sort?), we often think we can learn nothing from the past. Reality check: the past is prelude.
What can the past teach us about automation? This is as good a place to start as any other:
There are two general categories of task left for an operator in an automated system. He may be expected to monitor that the automatic system is operating correctly, and if it is not he may be expected to call a more experienced operator or to take-over himself. We will discuss the ironies of manual take-over first, as the points made also have implications for monitoring. To take over and stabilize the process requires manual control skills, to diagnose the fault as a basis for shut down or recovery requires cognitive skills.
This is the first of the ironies of automation Lisanne Bainbridge discusses—and this is the irony I’d like to explore. The irony she is articulating is this: the less you work on a system, the less likely you are to be able to control that system efficiently. Once a system is automated, however, you will not work on the system on a regular basis, but you will be required to take control of the system when the automated controller fails in some way. Ironically, in situations where the automated controller fails, the amount of control required to make things right again will be greater than in normal operation.
In the case of machine operation, it turns out that the human operator is required to control the machine in just the situations where the least amount of experience is available. This is analogous to the automated warehouse in which automated systems are used to stack and sort material. When the automated systems break down, there is absolutely no way for the humans involved to figure out why things are stacked the way they are, nor how to sort things out to get things running again.
This seems intuitive. When I’m running the mill through manual control, after I’ve been running it for a while (I’m out of practice right now), I can “sense” when I’m feeding too fast, meaning I need to slow down to prevent chatter from ruining the piece, or worse—a crash resulting in broken bits of bit flying all over the place.
How does this apply to network operations? On the one hand, it seems like once we automate all the things we will lose the skills of using the CLI to do needed things very quickly. I always say “I can look that command up,” but if I were back in TAC, troubleshooting a common set of problems every day, I wouldn’t want to spend time looking things up—I’d want to have the right commands memorized to solve the problem quickly so I can move to the next case.
This seems to argue against automation entirely, doesn’t it? Perhaps. Or perhaps it just means we need to look at the knowledge we need (and want) in a little different way (along with the monitoring systems we use to obtain that knowledge).
Humans think quick and slow. We either react based on “muscle memory,” or we must think through a situation, dig up the information we need, and weigh out the right path forward. When you are pulling a piece of stainless through a bit and the head starts to chatter, you don’t want to spend time assessing the situation and deciding what to do—you want to react.
But if you are working on an automated machine, and the bit starts to chatter, you might want to react differently. You might want to stop the process entirely and think through how to adjust the automated sequence to prevent the bit from chattering the next time through. In manual control, each work piece is important because each one is individually built. In the automated sequence, the work piece itself is subsumed within the process.
It isn’t that you know “less” in the automated process, it’s that you know different things. In the manual process, you can feel the steel under the blade, the tension and torque, and rely on your muscle memory to react when its needed. In the automated process, you need to know more about the actual qualities of the bit and metal under the bit, the mount, and the mill itself. You have to have more of an immediate sense of how things work if you are doing it manually, but you have to have more of a sense of the theory behind why things work the way if it is automated.
A couple of thoughts in this area, then. First, when we are automating things, we need to be very careful to assume there is no “fast thinking” when things ultimately do fail (it’s not if, it’s when). We need to think through what information we are collecting, and how that information is being presented (if you read the original paper, the author spends a great deal of time discussing how to present information to the operator to overcome the ironies she illuminates) so we take maximum advantage of the “slow path” in the human brain, and stop relying on the “fast path” so much. Second, as we move towards an automated world, we need to start learning, and teaching, more about why and less about how, so we can prepare the “slow path” to be more effective—because the slow path is the part of our thinking that’s going to get more of a workout.