I agree that whiteboard coding interviews that assess mastery of algorithms and data structures with low applicability to the role are not optimal for hiring.
However, I don't really agree that this is a "diversity" problem, except perhaps weakly so in that it optimizes against people who don't want to study algorithms for interviews. But interviews by definition optimize against some subset of the general population; unless you're defining diversity so loosely that it can be satisfied by the sets `{studies algorithms for interviews}` and `{doesn't study algorithms for interviews}` (and in that case, how are they different)? That's sort of like saying a tech company shouldn't optimize its interviews for people who like to watch baseball versus people who don't - they absolutely shouldn't do that, but that's meaningless as a diversity metric, in my opinion.
The article touched on this a bit. If you have a nonstandard background, you are less likely to have prepared for these kinds of situations because it's less likely you have parents, mentors, or peers to learn these things from.
However, I don't really agree that this is a "diversity" problem, except perhaps weakly so in that it optimizes against people who don't want to study algorithms for interviews. But interviews by definition optimize against some subset of the general population; unless you're defining diversity so loosely that it can be satisfied by the sets `{studies algorithms for interviews}` and `{doesn't study algorithms for interviews}` (and in that case, how are they different)? That's sort of like saying a tech company shouldn't optimize its interviews for people who like to watch baseball versus people who don't - they absolutely shouldn't do that, but that's meaningless as a diversity metric, in my opinion.
How are you defining diversity?