It’s Good* That Word Embeddings Are Sexist

A lot of news has been fluttering around about word embeddings being racist and sexist, e.g:

This is a good thing, but not in the sense that sexism and racism are good. It’s good because people who work on quantitative problems don’t believe things are real without quantitative evidence. This is quantitative evidence of that sexism and racism are real things.

Per my initial reaction, I was surprised how much alarm there is about this. When you live in a world that is glaringly *ist, take data from that world, and learn in an unsupervised manner, you’re going to acquire *ist knowledge. But then again, I’ve done my graduate education in a linguistics department with a strong group of sociolinguists. I was exposed to these ideas years ago and have been taught to have an awareness and sensitivity to these issues and to be critically aware of how language can construct and reinforce racist and sexist norms, especially though prescriptivism.

I suspect a lot of the shock is coming from the stronger CS end of things–a side of the university that is more strictly quantitative. My undergrad was in physics, which I suspect has a similar distribution of social science coursework–namely, just what the university requires. A student might have to take sociology or anthropology, and that’s only if the university requires it. My undergrad did not; I took macroeconomics en lieu of either of those.

When you’re in a quantitative program, there exists a lot of hidden assumptions. One is that quantitative analysis is the only way to do anything–any other way of approaching any problem of any kind is bullshit. This is because any other approach can involve biases that a researcher is unaware of. Abstraction and measurement help to remove the preferences of the researcher from the process, mitigating the effect of their biases. The procedure and the numbers are what count.

Hard-core context control.
Hard-core context control.

This works great for particles in a vacuum, for problems where the context can be completely controlled, but the assumption that these standards can be universally maintained bleeds into other problems for which doing so realistically is impossible. However, the air of non-bias around quantitative methods remains despite that the conditions that purged that bias in the first place are lost.

This assumption of non-bias holds into AI research–that a machine built on quantitative principles will be capable of arriving, logically and deductively, at perfect, non-biased truth–the objective truth that’s obscured by those pesky, confounding social factors.

If only Tay had taken advice from Dr. Dre: "I don't smoke weed or sess / Cause it's known to give a brother brain damage / And brain damage on the mic don't manage nothing / But making a sucker and you equal..."
“I don’t smoke weed or sess / Cause it’s known to give a brother brain damage / And brain damage on the mic don’t manage…”

This hope is at ends with AI’s Dark Secret–the one that never seems to make it into the press with its claims about AI’s up-and-coming “singularity”–solutions to the most interesting problems in AI rely entirely on training data. Some of this is supervised, some it is unsupervised, but it all still relies on the data it’s fed. With that, it comes to replicate whatever it’s been provided: garbage in, garbage out.

And so, this is where the shock comes from. For the first time, white, male quantitative researchers are smacked upside the head with the reality that the world exhibits sexist and racist tendencies. The data they’ve provided is digested and learned into biases. It turns out, building that perfectly logically deductive system, free from bias–a consciousness liberated from the social confines of human existence–isn’t just hard, but possibly impossible.

This isn’t a bad thing–perhaps disappointing to a slowly dying vision of AI. The upside though is that the majority of the evidence up to the present for *ist tendencies in society has been qualitative. You have to trust individuals synopses of their aggregate subjective experiences that privilege and bias exist. Right here, we’re seeing quantitative evidence that supports their testimonies.

There’s a two fold effect there: hopefully, it opens up quantitative researchers to acknowledging better the validity of qualitative research. Simultaneously, it confirms the discoveries of a lot of that qualitative research through discovering the same things from a totally different angle. That sort of independent confirmation is ideal in scientific work, and this convergence is just exactly that. We’re seeing decades of social science research supported by evidence from entirely different methods. In a discipline filled with men, this is unequivocal evidence that there are issues that need to be addressed, derived from the methods within that discipline. Sexism and racism suck, but with AI finally bumping into them and providing firm support for them as real issues, perhaps we can have better luck garnering public support in the larger social sphere.

“Bing bing, bong bong bong, bing bing.”

In the class I’ve been teaching this summer, for the last few days, we’ve been using a parsed version of the Donald Trump speech corpus that Ryan McDermott posted to Github a few days ago. One of my students mentioned that Donald Trump had made a speech where he said, quote, “Bing bing, bong bong bong, bing bing.”

I was wondering if this particular speech were actually in the corpus. As a teaching activity, we started searching for instances of /[Bb][io]ng/. I also wanted to see what the parser would do with a string like “bing bong bing bing bong”. There’s a possibility that the parser would assume this is a normal sentence and produce something like:

[NP bing bong] [VP bing [NP bing bong]]

Another student asked why were we doing this–searching for such an obscure, non-sense lexical item, when we could be searching for something that is actually meaningful?

The answer I had, in part, was that it’s not that obscure. As it turns out, these items are quite characteristic of Trump’s speech. In this corpus alone—which lacks the famous original “bing bing, bong bong” speech cited above—it appears 24 times (16 if you remove duplicates), often in clusters of three:

“And that’s what we ended up getting–the king of teleprompters.  But, so when I look at these things here I say you know what, it’s so much easier, it would be so nice, just bah, pa, bah, pa, bah, bing, bing, bing.  No problems, get off stage, everybody falls asleep and that’s the end of that.  But we have to do something about these teleprompters.”

“I hear where they don’t want me to use the hairspray. They want me to use the pump because the other one, which I really like better than going bing, bing, bing, and then it comes out in big globs, right? And then you’re stuck in your hair and you say, ‘Oh my God, I have to take a shower again. My hair’s all screwed up.’ ”

“You know, in the old days everything was better right? The car seats. You’d sit in your car and you want to move forward and back, you press a button. Bing, bing. Now, you have to open up things, press a computer, takes you 15 minutes.”

“You know, when you have so many people running – we had 17 and then they started to drop. Ding. Bing. I love it. I love it.”

“On the budget – I’m really good at these things – economy, budgets. I sort of expected this. On the budget, Trump – this is with 15 people remaining – Trump 51%. Everyone else bing.”

“In Paris, I call him the guy with the dirty filthy hat. Okay? Not a smart guy. A dummy. Puts people in there – mastermind – bing, bing, bing, it’s like shooting everybody. You’ve got to be a mastermind.”

“I was like the establishment. They’d all come to me, and I’d give them all money I write checks sometimes to Senators whatever the max – bing, bing, bing.”

The communicative goals of these tokens could constitute an entire discourse paper, but let’s just stick with the basics now. He seems to use it to indicate some kind of quick, repetitive action. It doesn’t seem to have a particular sentiment associated with it: bribing senators, competitors dropping out of the race, committing mass murder, moving the chair conveniently in a car, being annoyed with pump style hair gels, politicians reading off teleprompters.

It’s undoubtedly characteristic of his speech, though. To say that it’s a mere aberration–something to ignore–is prescriptive.  If we look at counts of lemmas throughout the corpus (using SpaCy—a little easier to break out than digging through CoreNLP’s XML), the lemma “bing” appears 11 times, the other 13 times being lemmatized as “be.” In those cases, the lemmatizer assumed “bing” was a VBG, essentially a misspelling of “being.”

Of the whole corpus, compared with all 24 counts of “bing,” Trump said “bing” more often than he said:

  • situation: 23
  • donor: 21
  • dangerous: 21
  • migration: 20
  • weak: 20
  • economic: 19
  • freedom: 18
  • mexican: 18
  • illegally: 14
  • muslim: 13
  • god: 11
  • kasich: 11
  • bernardino: 10
  • criminal: 9
  • hispanic: 9
  • chinese: 8

 

Among many, many other word types. You can get the full list of lemma counts here (when I get around to posting it), though note that “bing” appears at 11 in that list because a lot of the results were merged with “be” erroneously.

To go back to the critical student’s original question, though, it’s a difference in expectations, I suspect. While NLP tools are helpful, they don’t totally address the problem of meaning in text. Meaning is still in large part up to the programmer using the tool, not the tool itself. There’s still a lot of work to be done in that regard, in any application. Sometimes “bing bing bong bong” is really the best we can do.

 

Buzz in the Flesh: A Microcosm for Science in America

I had the opportunity a few months ago, largely thanks to @sociolinguista, to see Buzz Aldrin speak. It was pretty cool; even at 85, he’s still charming and sharp. Now-a-days, he’s mostly advocates for Martian exploration and colonization, and this comprised the bulk of the discussion. The session was part interview–done by Aldrin’s own son–part Q&A.

Before Buzz came out, a video explained his grand plan to reach Mars. Then, Buzz talked about optimal plans, etc. putting stations at L1, and choices for transfer orbits and hyperbolic intersects.

As much as I respect Buzz’s plans, I wonder what good they do. After all, the problem isn’t having a science plan. Planning is fun; there are entire video games where you plan and complete space missions. The problem is money and public interest. We have a public that doesn’t know or care, and as a result, there’s no money for the program.

As someone who studied physics (and has played enough KSP that it’s unhealthy), I understood what he was going on about. I don’t know that the majority of people in the audience did. There’s ways he could have helped, but didn’t bother, either by replacing jargon with a few extra words, or just taking a moment to explain some key concept briefly. A few extra words can go a long way.

Scientists have a duty, both to do honest science, but also to explain that science to others. That’s been done rather poorly over the last 50 years, and now we’ve got a significant segment of the public actively ignoring us, because no one really explained to them what’s going on in a way they could understand. It’s not that they can’t understand, it’s that we have to do a better job in helping them to do so.

 

Master Key

When it comes to infosec, the magnitude of ignorance amongst people astounds me. People like this actually get taken seriously, requesting backdoors in encryption algorithms so government officials can take a peek once they get a warrant. That sounds like a good idea when he frames it that way, but encryption, data, and computers in general are really abstract. Let me give you an analogy that’s a little more concrete, and then I wanna poke at why they even want this shit in the first place.

Let’s say FBI Guy were proposing a mandate for a national master key. Any door in the country, and with a warrant, an officer of the law could get a copy of the national master key and open the door to the house.

Totally creepy, of course, knowing that at any time some guy could just show up with a magic key that opens the door to your house. Even ignoring the potential for abuse–“pretty please, we promise not to abuse our national master key privileges”–there’s the inevitability that someone could figure out what the national master key is. If there’s one of these things built into every house in the country–even if there’s a special master key for each house–there’s some pattern to figure out. Someone’s gonna want to find out that pattern, because all the national mandate has done is create a puzzle to crack.

And these kinds of puzzles always get cracked. Especially when the prize is so big–access to literally every house in the country–it will get cracked. The solution will get plastered all over the Internet as a big “fuck you” right back at the people that failed to grasp the consequences of their poorly planned policies. It’s happened before, and it will happen again.

If the consequences are so bad–the neutering of every lock in the country–why does the NSA, FBI, and seemingly every other triple letter agency want something like this?

Roughly speaking though, the FBI already has that national master key–a state monopoly on coercive force. With a warrant, they can kick down your door, shoot your dog, throw you in jail, and throw all your personal belongings into duffel bags to get torn apart in a forensics lab.

They can’t do that with encrypted data, not without millions of computer hours for decryption. That’s not as easy as kicking your door down and stealing seizing all of your shit. That’s what they really want; from their point of view, encrypted data is a domain beyond the reach of brute force, and they want to reel it back in.

Maybe, in the end, they shouldn’t be focused on breaking encryption, but strengthening it for everyone, including themselves. While the FBI was busy petitioning for laws that break encryption, another massive government data breach was revealed, probably including personal information about Mr. Steinbach–the very official begging for weaker standards. We’re stuck with 20th century barons imposing 20th century standards on 21st century problems.

Arranging Fundamentals

A friend of mine who does some work I shouldn’t talk about–DC living for you–once explained something that struck me as odd. If you have two unclassified documents, document A and document B. Staple document A and B together–now you may have a classified document. Just the juxtaposition of two pieces of information is information, enough to change the status of the documents.

This was so striking, in fact, I dwelled on it for a bit, and began to realize, that is literally what I do as a computational linguist–re-arrange strings in meaningful ways.

Once you start thinking about this, it appears in a lot of things, aside from the arrangement of textual information. Geographic location is just this in action: real estate is valued by what real estate is next to it; if I own a car on a different continent, that car is essentially worthless unless I’m on that continent; I’m the Emperor of the Moon of the Wholly Circumferential Lunar Empire, yet they refuse to give me a diplomat plate.

Why should information feel so different? After all, re-arranging information is fundamentally what you learn to do in school, and I’ve been in school for 21 years.

I suppose it’s just that. Especially having studied physics, one learns to boil any problem down to its principle components, down to the key relationships that apply, and to demonstrate with those relationships how the facts have come to be. You become a master at re-arranging information in the right way, and when you become a master at re-arranging information, re-arranging information feels cheap; knowing the principle components is the key to finding the solution.

I suppose this is why the juxtaposition of two documents as something meaningful is so striking–if you have access to the two documents before, you have access to the principle components. As a master of re-arrangement, nothing else is required.

This couldn’t be further from the truth, though. The arrangement of things does matter, and it’s often incredibly complex. If the fundamental components were all that mattered, then if you memorized this chart of the fundamental particles of matter, you would know everything there is to know about everything.

Standard_Model_of_Elementary_Particles.svg

 

But knowing this chart, you don’t know everything about everything. Their combinations allow a certain freedom, and how those uncertainties left by that freedom are realized are also interesting.

Those uncertainties are just arrangements, but they’re important. They explain how cats are different from birds, why cruising in the passing lane makes you a complete asshole, and why I keep writing this essay despite having far more pressing shit on my plate. All of these things, in many ways individually, are due to arrangements of arrangements of arrangements of fundamental particles, so far removed that the black box of the atomic nucleus bears little (obvious) bearing on the outcome of their combination, aside from making it possible amongst another infinitude of possibilities.

This line of thinking is common outside of physics. For example, after recent events at UVa, some have argued in favor of shutting down fraternities, indefinitely. Counter-arguments in the comments, however, went along the lines of “well, if you kick them out of the frats, they’re still rapists.” They’re treating the members of the organizations as fundamental, principle components, and are arguing that by dividing the principle components up, you do nothing to negate the evil contained in those components.

There’s a lot of places this could go, but I’ve made my point here, loosely enough. Arrangement is information, and it matters. Fundamental components are good to know–they give space for juxtaposition to happen–but the interesting stuff happens in how things are arranged. It’s why we’re more than quarks.

Thirteen Years Later

They hate our freedoms.”

There’s little that pisses me off more than this sentence. It’s been used to justify thirteen years of warfare and widespread state surveillance, and it’s complete bullshit.

They hate our freedoms as much as we hate theirs; it’s really got nothing to do with why what happened did. It’s the conception of justice as vengeance. It’s the belief that our way of life is average, normal, and optimal, and imposing it on others is, in a universal sense, justifiable. It neglects centuries of Western powers rampaging through the Middle East in pursuit of religious or economic gain and in the process leaving power vacuums and anger, leading to further intervention, leading to further power vacuums and anger.

No matter who is responsible, the cycle of suffering will continue. While someone over here mumbles “mrrca” repeated, asserting their own patriotic righteousness, just that same mumbling sounds like “Allahu akbar” on the other side of the globe. They both believe in the absolute truth and justice of their own side, and to push on other another reinforces and maintains that sentiment. The equal and opposite reaction is evil, and the push becomes good. The justification is built on a selective compassion for dead compatriots. Death begets death, the slaughter continues, and as a solution, violence allows for one and only one victory condition: suppression or annihilation of the opposition.

So, here we are again, bombing what festered in a power vacuum we created. Nor will this be the last time. Nor will the next time be the last time.

Bombs are expensive. As long as there is someone to drop the bomb on, there are those who sell bombs. Bombs are good for business. Bombs guided by expensive electronics are even better.

George Orwell rolls in his grave.