Narrowly scoped to GIGO, the concept can be traced more generally back to Charles Babbage in the 1800s.

> On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

I couldn’t tell you when the discussion about bias and unreliability in ML training sets started, not my field of expertise.

GIGO, which in this context would translate to something like "using a bad dataset means there is a problem with the data, not that the tool is racist". I'm not an expert, but I think the argument here is it's far far too easy to have a "garbage" dataset with these tools. I.e. one that only functions correctly for some racial groups (like this one does). That's only racist if you then try to use it in a situation where it comes in contact with multiple racial groups - like, virtually anywhere in the real world.