Machine learning, as well as human learning, largely work off of correlations observed in the source data. In some cases, the data itself is a flawed source for

Machine learning, as well as human learning, largely work off of correlations observed in the source data. In some cases, the data itself is a flawed source for the learning it's being used for, because human biases have already influenced the dataset. An example is arrest records, which is the result of discretionary decisions made by officers whether or not to pursue an arrest. But in other cases, the source data is less in question, but the results of the learning are nonetheless undesirable. We ought to be able to distinguish, in our professions and our political lives, between the two scenarios. It's counterproductive to conflate the two to strive for a goal, because we risk using mechanisms that never discover and won't correct the true origin factors.