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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...
Indeed. The biggest inheritance is the society bias: data is a reflection of society and not the other way around
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