Algorithmic biases can be particularly difficult to eradicate. Policy makers may require rules that forbid decisions based on race or gender, but it is often not sufficient to simply omit that information in the data provided to an algorithm. The problem is that other pieces of information may be correlated with race or gender, particularly when considered in concert. For example, if you build a machine to predict where the next domestic violence event will occur, the machine may choose an apartment over detached housing since those who share a wall are more likely to report the event. This
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