Let’s try the experiment. Two startups short on cash. One hires your way and one does not. Which one do you think is more likely to survive?
That's not the point though. The point is to remove bias.
It's easy to solve for one variable; beyond that, one has to balance things so that primary goals (hiring capable and personable talent) are still achieved.
"We want the best talent."
"No we don't. We want the to remove bias."
"Isn't talent more important?"
"Removing bias is finding the best talent."
That was like reading a conversation with an AI chatbot. We all agree we want the best talent then, yes?
So asking whether removing bias is "more important" than improving decision making is a non-sequitar: removing bias is a tool towards that goal. In a sense it's "less important" because other tools might do the job better, and in a sense it's more important because simply desiring to improve decision-making does nothing without tools to implement that desire.
That said, I wouldn’t blank out schools and employers. Discrimination by school and employer may or may not be wrong for the business. But those aren’t protected categories.
The problem is that those institutions aren’t perfect in their hiring/accepting either, and some initial mistake by the college is propagated and amplified by subsequent decisions deferring to it.
No idea how to solve this. Maybe the data could be coded into some numeric or ordinal value. I bet the actual terms like “Harvard” and “MIT” are more impressive than a completely equivalent abstraction such as “Ivy League” or “top tier”.