Alright let me try to be real. After reading http://www.sciencemag.org/news/2017/04/even-artificial-intel... and https://en.wikipedia.org/wiki/Implicit-association_test (in that order thank you), it looks like the implicit association test is a good way to magnify small differences (I'm thinking floating point error) in how much you associate one of a few words with a certain other. So, presumably, quantification would really be a natural way to eliminate those types of subtle differences.

I'm glad we could at least get you to read it, but you're still not caught up. The effect is many orders of magnitude larger than floating point error.

The notion that implicit bias could be corrected by quantization (not "quantification") is an interesting hypothesis, with a low prior probability, which could be tested by experiment and easily published if it is true.