From a statistical perspective, in a fair random lottery, would not the shape of the probability distribution of attributes of winners necessarily match that of the resulting qualified applicants? If fairness is defined in this approach as a process that does not add information to the system, and in this case actually removes both information (bias) and noise (bias) equally, all it would serve to do is further obfus…
In a threshold-based admission lottery, everyone more than k sigma from the mean (for example) is collapsed into the same category, such that information distinguishing them is lost. But the implicit premise to this system is that you can't accurately measure the distinctions between those deviations anyway.
Given that premise, you're not adding noise to the system, though you are removing information. I think the claim under question is that trying to precisely measure people more than k sigma from the mean is intrinsically noisy and prone to spurious correlation with academic success. So then you'd also be removing noise under this system.
So I think your point of contention should be with the premise if you disagree with it, because I don't think we can really argue about statistical properties of the lottery distribution until we first settle on the underlying axioms.