All sides agree there is an issue around fair implementations of ML algos in the real world. There are two ways this can happen: 1) The model uses biased data

All sides agree there is an issue around fair implementations of ML algos in the real world. There are two ways this can happen: 1) The model uses biased data when it could have used unbiased data. 2) The model uses biased data when no unbiased data exists and is incapable of correcting for these discrepancies. The first case is most clearly and engineering/implementation issue, the second is obviously not. Biased data is a known failure case of ML, its the responsibility of researchers to design for it.