The thing is the article is starting out with the sort of network of semi-causal explanations that aircraft designers have and pointing out that this isn't simple causality, isn't just a line of causal events.
But then the article indeed jumps from that observation to the claim that causation isn't something we should worry about - which feels just wrong to me (and I think a lot of people).
I mean, all sort of intuitive explanations feel like causal explanation but, of course, are much more sketchy. But this feeling like causality matters imo, the whole network of explanations that satisfy a human together seems to make things robust, not fragile, whereas AI predictions and models are renown for being fragile. But still, I think a big part of the situation is we haven't characterized fully what humans do here.
>The thing is the article is starting out with the sort of network of semi-causal explanations that aircraft designers have and pointing out that this isn't simple causality, isn't just a line of causal events.
I'd argue that it absolutely is simple causality. Sure we have to flail around in the dark a little to get to a better plane, but they physically test things during development. That's simple causality right there. I guess I'm arguing that engaging in active (not watching someone else do it) trial and error is a case of causal reasoning.
Then would it be better to say that these models allow you to make causal changes?
That is, sure, they don't tell you why the specific lift properties are there. However, they do enable a practitioner to make a change and know what impact it will have on the lift.
But then the article indeed jumps from that observation to the claim that causation isn't something we should worry about - which feels just wrong to me (and I think a lot of people).
I mean, all sort of intuitive explanations feel like causal explanation but, of course, are much more sketchy. But this feeling like causality matters imo, the whole network of explanations that satisfy a human together seems to make things robust, not fragile, whereas AI predictions and models are renown for being fragile. But still, I think a big part of the situation is we haven't characterized fully what humans do here.