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I don't think he was mixing his terms. He was clearly talking about the everyday concept of bias, not statistical bias. I don't think this author laid it out

I don't think he was mixing his terms. He was clearly talking about the everyday concept of bias, not statistical bias. I don't think this author laid it out very well, but the problem of bias in machine learning is real and difficult to handle. The Amazon resume fiasco is a textbook example of what can go wrong with practices I see all the time. In my experience ML practitioners rarely stop and ask themselves the hard questions about this. They either don't think of it at all or they don't think we should be asking those questions. "The model is accurate" shouldn't be sufficient.

charlysl 8y ago
Well, maybe I misinterpreted what the author meant. In fact, I was a bit casual myself: statistical bias, which is what I meant, would have been more accurate than what I wrote, machine learning bias (which might be interpreted as the bias in "bias and variance", which has nothing to do with the discussion). The thing is, if the training data was generated by human behaviour, warts and all, then it is going to represent our prejudices. If this is a concern, then it seems to me that ML might not be the right technology to make decisions that have a political dimension. If we want the system's predictions to be PC, then a rules based system might be more appropriate, blast from the past, unless we start labeling samples, with their output as an extra feature, as "PC" and "Not PC".