Race and ability to repay a loan, are - and this is scientifically proven - uncorrelated. What is correlated is your social status and your ability to repay a l

Race and ability to repay a loan, are - and this is scientifically proven - uncorrelated. What is correlated is your social status and your ability to repay a loan. However, there is a problem because the population of the US is not uniform. Because of history, some races were less wealthy than others. So far, this makes sense I suppose. Now, talking about machine learning. The big specificity of machine learning (when it is implemented with neural nets) is that it is trained on data only. The machine doesn't know about biology, and it doesn't know about history. Therefore, the model sort of converges to a place where effectively it "believes" from the data it has seen that there is a correlation between race and ability to repay a loan. So basically, the historical bias that is in the data translates into a logical bias. And so, because of history, you're less likely to get a loan if you're a black person than if you're a white person. That is what I call "amplifying the status quo" Now, as we live in a society that holds equality of chances as one of its core values, I think it is good that people who work in machine learning look at these bias, and have a criticism towards the results of the algorithms in terms of societal impacts, and not only in terms of economical gain. This has nothing to do with totalitarianism. Yes it is not libertarian, but we don't live in a libertarian world.