"Bias laundering" happens when we choose to ignore the biases of automated decision systems because of the illusion that all algorithms must be objective since they're "driven by math" or "run by a computer".
Algorithmic biases could be hard-coded by the implementer, or could come from a biased choice of features, or could come from biased data (all data being biased in some way), or could simply arise from spurious correlations (overfitting). Math/computers are a detail in the story.
In general, automated decision systems tend to inherit the biases of the human-driven process that they replace. Unfortunately, these biases start to acquire a veneer of objectivity, and become harder to inspect, or fix.
With humans at least, new generations bring change. Algorithmic bias may prove to be more entrenched than human-driven bias, due to the greater indirection and continuity brought by datasets and algorithms, as opposed to someone's judgment...
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