> Obligatory reminder that there's an inherent tradeoff between bias and variance in any ML architecture. It should also be stressed that in this context "bias

> Obligatory reminder that there's an inherent tradeoff between bias and variance in any ML architecture. It should also be stressed that in this context "bias" is a kin to interpolating between data points where resolution in the training set is relatively low, thus leads to a simpler model that does not exactly match the test set. What this implies is that this "bias" does not reflect a preference, let alone an intention to favour one group over another. It just means that a combination of the test set being too sparse or with too few dimensions, the choice of model being too inflexible, and the problem domain having too much uncertainty at that resolution. Critics may single out the model, but the core of the problem actually lies on the data and the problem domain.