Learning, as in what we do, is not learning associations in datasets.
It is learning "associations" between: our body state and the world as we act. It's a sort of: (action, world, body, sensation, prior conceptualisation, ...) association. (Even then, our bodies grow and this is really not a formal process.)
This is, at least, what is necessary to understand what words mean. Words are just tools that we use to coordinate with each other in a shared (physical) world. You really have to be here, with us, to understand them. Words mean what we do with them.
Meaning has a "useful side-effect". It turns out when we are using words their sequencing reveals, on average, some commonalities in their use. Eg., when asking "Can you pass me the salt?" I may go on to ask, "and now the pepper". And thus there is a statistical association between the terms "salt" and "pepper".
But a machine processing only those associations is completely unaware there is anything "salt" to pass, or even that there are objects in the world, or people, or anything. Really, the machine has no connection between its interior and the world, the very connection we have when we use words.
When a machine generates the text "pass me the salt" it doesnt mean it. It cannot. There is no salt it's talking about. It doesnt even know what salt is.
A machine used to make decisions concerning people, unaware of what a person even is, produces new unethical forms of action. Not merely just "being racist because the data is".