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Isn't the whole point of machine algorithms to find the best spot between variance and bias? In which case, every algorithms will have some bias. IMO, the focu

Isn't the whole point of machine algorithms to find the best spot between variance and bias? In which case, every algorithms will have some bias. IMO, the focus should instead be on not overselling algorithms as being infallible rather something which will have some bias and needs overriding time to time. If a system is fully automated without checks and balances we might have serious problems. A good non-ML example was discussed couple of days ago on HN where a person was terminated by a machine without much oversight: https://idiallo.com/blog/when-a-machine-fired-me https://idiallo.com/blog/when-a-machine-fired-me

pliny 8y ago
>Isn't the whole point of machine algorithms to find the best spot between variance and bias? In which case, every algorithms will have some bias. This references two different, almost opposite, meanings of the word bias. The meaning in TFA is being individually subject to judgements about groups (eg an individual AA prisoner being denied parole because AAs as a population have higher rates of recidivism) whereas in the context of ML it refers to a model ignoring important features of a dataset.