Such articles are crafted to create outrage among the uninitiated. They like to pretend that "bias" did not exist before, and AI is introducing it. Every time we, as humans, make any decision, we're "echoing biases we're fed". It's 100% intractable to rationally think about every decision, so we almost never bother, and when we do, we second guess like crazy because rational decisions often do not agree with our biases. And guess what, most of the time our biases are _right_. It wouldn't make any sense from an energy conservation standpoint to have them otherwise.

What we should be aiming for instead is not the total lack of bias (an impossibility if you are to learn anything at all from the data), but _explainability_. A system must be able to show me the set of statistics that led to a particular decision. I.e. women in Los Angeles area are known to be much safer drivers according to this subset of data, so we offer a lower insurance rate to women in Los Angeles area, to use just one hypothetical example. Such systems are a rarity nowadays, and research into them is relatively sparse.