The article implies that both algorithms and the data are at fault, which I don't think is true. It's really just the data, the algorithm reflects the 'truth' i

The article implies that both algorithms and the data are at fault, which I don't think is true. It's really just the data, the algorithm reflects the 'truth' it finds in the data. Interesting talk relating to the topic: https://www.youtube.com/watch?v=jIXIuYdnyyk https://www.youtube.com/watch?v=jIXIuYdnyyk Many approaches in fair machine learning that try to 'de-bias' the algorithm basically just do stuff like reducing the accuracy in the advantaged group to make the algorithm seem more fair - that is hardly what you want and will just make you susceptible to charges of discriminating against the majority or employing affirmative action. Probably rightfully so, because that's what you do. It's absolutely fine if that's the intent, but then you should have a public discussion where you are open about the fact that you manually tinkered with the parameters to prefer fairness over accuracy (which can probably be a valid goal). I think finding the problems with the data is very important though. Everyone wins if the quality of your data increases: the algorithm can become both more accurate and also more fair. And it can also identify societal causes for this biased data, for instance police being more sensitive to crimes of minorities, which will then feed back to the innocent algorithm. A related point is of course that we should be wary of putting too much power and trust into faceless algorithms in the first place. Also some interesting collection of papers on the matter: https://fairmlclass.github.io/ https://fairmlclass.github.io/