Off-topic-ish, but only about 10% of Irish people have red hair.
This is not wrong, but it boils down to "don't make mistakes when optimizing functions". It's offensive because it's coming from someone who has nothing to contribute except "do your job better". It's like saying we have a problem with planes crashing, so engineers have a moral duty to build better planes. As though they weren't trying to build planes that stay in the air already. You can make serious sounding statements, or you can impose fines, or you can randomly pick people to publicly shame, but none of those things are positive contributions to hard problems.
Mechanical engineers are taught from the beginning of their careers to think carefully about the consequences of their decisions.
Computer engineers, not so much. We're taught how to optimize, and make machines that optimize, and make optimizers that optimize our optimizing machines, but it's pretty rare to encounter a data ethics course, and they're almost never program requirements.
Facebook got in trouble for algorithmic redlining in 2013, serving loan sharky ads to black people (which its algorithms learned via proxy features, like zip code). Does Facebook hire third-rate engineers who would point the engine on a plane the wrong way? No! Facebook has some of the best people in the industry! But even the best people in the industry were and in many cases still are not fully considering the consequences of our designs, because we weren't trained to.
Wrong. We had two courses at my school. From the ABET Criteria for Accrediting Engineering Technology Programs: "(e)Include topics related to professional and ethical responsibilities".
The issue here is proxy features, and that it isnt as obvious if your statistical model is doing something wrong. You identified zip code and race. What about number of cats? Would you have a particular gender and marital status in mind if a given data sample had a large value in the numberOfCats column?
So you ban making decisions based on number of cats. But amountOfKittyLitterPurchased, numberOfCatPicturesViewed, numberOfDislikesOnDogPictures, etc could be used as a proxy for numberOfCats. Try to come up with an exhaustive list of all features which could be used as a proxy for some protected class. Starting with politically incorrect jokes will get you part way there, assuming you possess an exhaustive list of all politically incorrect jokes. But you will need to be pretty creative to come up with more.
https://www.abet.org/accreditation/accreditation-criteria/cr...
As a software person, I have absolutely no issue with telling other software people to do their job better, and I see no shortage of people that aren't even trying on that front.
Its your comparison between a process that takes safety, reliability and a reduction of harm as a core, first value, and a process that cares first about money, and second about novelty and somewhere way down the line people, maybe, that is offensive frankly.
Machine learning is going to be very good at identifying proxies for features we don't like, even if we don't directly provide those features. Weighting red hair color could be a proxy for the Irish, targeting hair texture could be a proxy for african americans, targeting zip codes has always been a proxy for race, etc.
If we want machine learning to ignore these kind of things it's probably going to have to be a separately weighted training goal that racial groups and so on need to have equal outcomes in the final results.
That's really what AOC is going for here. The black box will happily learn racist ways to weight things all on its own, we can't just throw up our hands and say oh well, if the algorithm is denying black people for home loans then we just have to accept it.
Because, in the short term, being racist is an effective greedy strategy. It's not your business's problem that, say, black people have higher rates of default (or whatever, example). But it also leads to bad social outcomes in the aggregate, and we've agreed that shouldn't be a factor in home loans. And we have to find a way to make sure that The Algorithm (tm) doesn't just find a way to machine-learn redlining or some other proxy for race or other protected characteristics.