Algorithms _can_ be prejudiced. See [1]. Not being defined with bias or prejudice does not make them impervious to it. If a dataset is biased, as you have stat

Algorithms _can_ be prejudiced. See [1]. Not being defined with bias or prejudice does not make them impervious to it. If a dataset is biased, as you have stated by "not trusting judges" elsewhere, then the algorithm will follow that. If the dataset contains a feature that is highly indicative (be it racial information or otherwise), your model may come to depend entirely on it. The presence or absence of other data may be irrelevant, especially if you don't perform regularization or another similar tactic. Racial (or specific feature) profiling is a very complex topic. If there's open legal questions as to whether policemen are allowed to use it, I don't feel algorithms should be allowed to. [1]: "How big data is unfair: understanding unintended sources of unfairness in data driven decision making"[1], by Moritz Hardt, a Google machine learning researcher who has published on the topic - https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de#.6divbmgwn https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de...