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    $\begingroup$ An alternative to removing everything that helps the model discriminate between (for concreteness) gender might be to train your model with gender, then when predicting (or whatever) run the prediction twice, once with each gender, averaging the results. $\endgroup$ Commented Jan 19, 2019 at 0:26
  • $\begingroup$ @jbowman Resulting in little interpretive consequence, and perpetuation of in-built biases over time. $\endgroup$ Commented Jan 22, 2019 at 2:21
  • $\begingroup$ The Amazon case does in no way conclusively show bias. It could simply be the phenomenon known as stereotype accuracy. Sometimes traits actually correlate with demographic variables. Here is an example. You know that person X is young and middle class. How likely are they to commit a violent crime? I now give you another piece of information: their sex. Does this change the likelihood? Of course. Is that bias? Of course not. It's what is known as stereotype accuracy. $\endgroup$ Commented Jan 22, 2019 at 8:42
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    $\begingroup$ @Eff and this is how discrimination happens... Females earn less on average, so let's pay them less! The whole point of not having discriminative algorithms is that you should not use such information for making decissions, even if on average it seems to work. Moreover, if often works because of the social bias (e.g. we tent to pay more to males, African Amercians are more likely to go to jail for exactly the same crime as compared to Caucasian Americans etc.), so the stereotype is accurate because there is stereotype, not because of the nature of stereotyped group. $\endgroup$ Commented Jan 22, 2019 at 9:01
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    $\begingroup$ @Tim Nope. While there can be some truth to what you're saying, by and large it is not true. I urge you to read the book "Social Perception and Social Reality: Why Accuracy Dominates Bias and Self-Fulfilling Prophecy" by Lee Jussim. In this major book the author basically reviews the entire body of scientific literature on stereotypes, bias, self-fulfilling prophecies, etc. He shows that the evidence overwhelmingly shows that what you're describing is the minority of what is occurring. $\endgroup$ Commented Jan 22, 2019 at 9:06