Yes, and it's pretty scary how many technologists seem to be surprised by this. If we train bots using data derived from humans, the expectation is that they will inherit biases from humans. There's nothing about a silicon brain that automatically bestows perfect objectivity, only perfect obedience.

I struggle to think of a single person with the faintest understanding of what machine learning algorithms are being surprised by this. Who are these "technologists" you're speaking of?

Almost everyone I know with at least a faint understanding of ML is surprised by models picking up racism etc when there was zero intent to do so, because of systemic racism etc in available data. Or at least surprised by how much can be picked up. You're bubbled if no one you know is surprised.

"because of systemic racism"

Sometimes data might be 'racist' (i.e. human written corpus text)... but sometimes data is just data.

Are facts racist?

I would seem the world is rather diverse, i.e. 'people are different' and as we are different, AI is going to pick up on that. That's the whole point.

Now ... some bad examples like in this example taking positive/negative inferences the wrong way. OR actual systematic racisms showing up in bad ways i.e. maybe some groups are more likely to be monitored than others, thereby showing up more frequently in mad terms etc..

Why is this surprising? ML models are just recognizers and bias on the basis of ancestry is observable in all human cultures at all times.

If we nobly insist that the models describe the world as we wish it were and ought to be, then we won't be describing the data accurately. Maybe that trade-off is worthwhile if it somehow reforms human attitudes along lines we find more agreeable?

Conversely, almost everyone I know with at least a faint understanding of ML is entirely unsurprised about this.

Then again, my personal social bubble leans heavily liberal and hard left. And I think that has a lot more to do with it than with how much people understand ML. When you explain this sort of thing to people who have no idea about ML, in very simple terms ("we give the robot the text that humans wrote, so that it can pick up the patterns" etc), they see why it does that very quickly, as well - if their politics makes them aware of bias in general.

Hmmm...I'm no expert, but my master's thesis topic in the 90's was on neural networks that use R-squared (a measure of correlation), and when I saw the news about Microsoft's chatbot going Nazi, I was not at all surprised. Not saying no one you knew was surprised, but I had "at least a faint understanding of ML", and the primary thing I learned about it was that it learns what's in the data, whether that's the part of the data that you intended it to learn or not.

Tay was trolled hard by 4chan, that's why she went hardcore Nazi almost immediately. It was amusing, but not a fair & controlled experiment by any means.

The real world is neither fair or a controlled experiment.

Which is why I'm surprised about all this "AI is biased" outrage. A decent algorithm will learn what's in the data. Cast on a wide enough scale, the data is roughly what the world is. If your bot learns from newspaper corpus, then it learns how the world looks through the lens of news publishing. If news publishing is somewhat racist, and your algorithm does not pick on that, then your algorithm has a bug in it.

It seems to me like the people writing about how AI is bad because it picks up biases from data are wishing the ML would learn the world as it ought to be. But that's wrong, and that would make such algorithms not useful. ML is meant to learn the world as it is. Which is, as you wrote, neither fair nor a controlled experiment.

Well put. The people complaining about how AI is bad are the same people who push "diversity hires" to try to pretend that the population of software developers is equal parts male/female, and white/black.

It’s because most tech people have the default position that racism is not really a big deal, an edge case in modern society. That certainly is the message the political center and right is pushing.

Stuff like this puts the lie to that, though.

Given that the data showed a massive range by name within the same race and a much smaller skew between different races, couldn't this data be said to support that conclusion?

Disclaimer: I don't know enough about the data or the algorithm to determine this mathematically but I think worth pointing out. Would have been nice to see some statistical analysis instead of just assuming the charts speak for themselves.

This thread reminds me of the nature of political discourse at the moment, for example with regards to political correctness. The loudest and most popular voices are pushing simple fixes to intractable problems, and more sensible voices mentioning the truth of the matter are buried.

"There's nothing about a silicon brain that automatically bestows perfect objectivity"

Systems that are perfectly objective about recognizing patterns will definitely be biased in their predictions, that's the whole point.

But technologists shouldn't be surprised by this. "Garbage In, Garbage Out" has been a mantra in machine learning since forever.

Indeed. Before there were computers, in fact. In his 1864 autobiography, Charles Babbage wrote:

"On two occasions I have been asked, — 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."

Ergo human beings are inherently garbage.

The data is correct and a-biased. If you ask 100 people around you, they are, on average, more likely to have had a negative burrito experience, than a negative pasta experience.

The learning algorithms are crude and dumb. They will simply fit to any data you provide it (you choose how many Mexican restaurant food reviews you train your sentiment classifier on). Then they count how many times the words "mexican" and "man" and "mexican man" appear with a positive or negative label in the train set. And objectively try to give the best probability for that.

Current sentiment analyzers are not AI: no common sense, no understanding, no reasoning. We are just rushing to replace looking a job candidate in the eyes with running some 1960's logistic regression over their cover letter. Let's hope for their sake they did not manage a Mexican restaurant.

The world's largest and most positive burrito experience was so enormous that it had to be photographed from an airplane.

http://costena.com/famous.html

On May 3rd, 1997 La Costeña of Mountain View, California created the world's largest burrito. The burrito weighed in at 4,456.3 pounds and was measured at 3,578 feet long. It was created at Rengstorff Park in Mountain View.

Well, not so bad if we use the mirror to reflect on ourselves and our biases, and work to negate them. Fairly bad if they're used for recommendations and for rankings.

That was my thought too. You can't manage what you can't measure. This is a tool for measuring the amount of racism in our society. It's a good thing, not a bad thing :)