The same applies with attribution in general (e.g. in decision making).
As in, you’re also skeptical of traditional Shapley values in discrete coalition games?
“Completeness” strikes me as a desirable property for attributions to be properly normalized. If attributions aren’t bounded in some way, it doesn’t seem to me like they’re really ‘attributions’.
Very open to counterarguments here, though. I’m not particularly confident here either. There’s a reason this post isn’t titled ‘Integrated Gradients are the correct attribution method’.
[Not very confident, but just saying my current view.]
I’m pretty skeptical about integrated gradients.
As far as why, I don’t think we should care about the derivative at the baseline (zero or the mean).
As far as the axioms, I think I get off the train on “Completeness” which doesn’t seem like a property we need/want.
I think you just need to eat that there isn’t any sensible way to do something reasonable that gets Completeness.
The same applies with attribution in general (e.g. in decision making).
As in, you’re also skeptical of traditional Shapley values in discrete coalition games?
“Completeness” strikes me as a desirable property for attributions to be properly normalized. If attributions aren’t bounded in some way, it doesn’t seem to me like they’re really ‘attributions’.
Very open to counterarguments here, though. I’m not particularly confident here either. There’s a reason this post isn’t titled ‘Integrated Gradients are the correct attribution method’.