> A human factoring this things in his head is not magically more objective or less biased. I get your point, and I think you make some good arguments, especia

> A human factoring this things in his head is not magically more objective or less biased. I get your point, and I think you make some good arguments, especially regarding the small number of data points needed. In fact, in this regard I think that machine learning (or statistics anyway) can be a very useful tool for informing decisions. I think it should be regarded similarly to how experts are used in court. But I think you're missing mine. It is not that humans are better or worse at having bias when considering factors. It is that some factors are inherently biased. And it is the choice of what we do with those biases that we call "values". Now, do you think "values" are things that can be encoded in a machine-precise fashion? Well, it's what we call "law". However, law is obviously _not_ something that can be applied "by algorithm," because otherwise we would have nothing to discuss when it comes to hearings. > But I'm not talking about predicting if someone is a criminal . I'm taking about things like loan defaults and health risk. Here the company has pretty objective ground truth. If women really are less likely to get into car accidents, then it's just a fact. Theres no hidden bias here. That is true. I think that categorizing people is something that will never go away. Whether it feels "right" or not is certainly debatable. People with pre-existing conditions aren't exactly happy with the current state of insurance. Do you think black people like it when they can't get a loan because they are black? Do we want society to work that way? You say that the machine can be used to make a better decision -- simple, remove the "skin colour" category. But perhaps that leads to a decrease in the confidence interval. Insurance/banks don't do that, because they consider it important information -- hence, (maybe) the need for regulation. I think this EU regulation is going to be a catalyser for some very interesting discussions around these issues. Basically what I'm saying is, talking about algorithms vs. humans is a side issue, when the real discussion is, on what kind of data do want decisions to be made?