I think you've very severely missed the point. Differences like those you describe generally reflect biases in our socal structure, which are then (correctly, but problematically) reflected in statistical models and algorithms of all stripes. This becomes more problematic when we overinterpret the resulting findings, or uncritically incorporate the resulting predictions into real systems.
A good example is the criminal justice system in the US. Minority communities are much more intensively policed, prosecuted, and jailed. A model will, without care, use race or poxies thereof to predict "criminality" without understanding or accounting for the bias inherent in the label. If that is used in policy, it runs the risk of amplifying existing social problems and injustices.
We alway have to ask: "bias for what?", or the conversation will be hopelessly confused.
A good example is the criminal justice system in the US. Minority communities are much more intensively policed, prosecuted, and jailed. A model will, without care, use race or poxies thereof to predict "criminality" without understanding or accounting for the bias inherent in the label. If that is used in policy, it runs the risk of amplifying existing social problems and injustices.
We alway have to ask: "bias for what?", or the conversation will be hopelessly confused.