False positives in classification or prediction are in practice unavoidable with any probabilistic technique for any non-trivial task. Including certain feature

False positives in classification or prediction are in practice unavoidable with any probabilistic technique for any non-trivial task. Including certain features where they are forbidden by regulation is obviously not done. The problem arises that your techniques can potentially derive those features anyway from correlated proxies. Meta-physically you are using a probablisitc inference technique to derive from unknown-knowns in your data a given known-unknown predictor. You can try to normalize for known-knowns, blurring the biases you don't want to account, but you can't control for unknowns. In some cases it is easy to detect bias in the model when there was discrete data for the feature you try to control. E.g. if your data would have an explicit 'gender' field, then you can test how much the outcome of the prediction is not correlated to the specific value of that field. If the data does not have such a feature, but derives it implicitly though e.g. food purchase preferences, that is much harder to detect. You can try to control for that if for whatever reason you deem the model not in line with bias preferences, but due to the implicitness it will be very hard to isolate the impacts. I feel you will never get a model satisfactory to all, as in the end what is and what isn't accepted as 'neutral-data' is not a scientific but a political statement. There is nothing wrong with that. All our ethical choices are political. Just don't expect universal acceptance.