The artificial intelligence we're building is biased.
And the short explanation for that is it's our fault.
We're biased and our machines are learning from us.
But how do we become biased?
Joanna Bryson thinks we might be able to learn that from machines.
— Well, the only reason that AI is so powerful at understanding ourselves is the fact that we are predictable.
Joanna is a computer scientist who studies artificial intelligence.
Last year she basically fed the entire internet into a machine.
OK, not everything.
But about 840 billion words of it.
Tons of stuff: your tweets, the Declaration of Independence, our League of Legends threads, and more.
She wanted to see if the machine could form biases just based on the patterns it could see in our language.
— So basically words mean more like the same thing if you use them more like the same way. Kind of obvious.
So you can talk about going home and feeding your dog or going home and feeding your cat.
And you're never going to talk about, you know going home and feeding an orange or something.
Joanna fed the machine all this language and then told it to create clusters of words that were related to each other.
Then she compared her language association data to the results of a hidden bias test.
This is a test people take to measure the biases we might not be aware of.
Joanna was completely taken aback by how closely her machine's bias tracked to the results of that test
which is called the Implicit Association Test and has been taken over 17 million times.
That means Joanna's machine's bias matched millions of people's aggregate biases almost perfectly.
— Even though it's a giant spread sheet that counted a lot of words
it has this knowledge about the fact that flowers are more pleasant and insects are less pleasant.
We think flowers are more pleasant than insects.
So did the machine.
But we also think woman when someone says nurse.
So did the machine.
Anything. Any bias that we have.
So did the machine.
— Most of us believe that we are consciously in control of our beliefs our attitudes
our behavior, our actions, our judgements.
That's Tony Greenwald. He co-created the Implicit Association Test in 1995.
— And the research says there is something going on automatically behind the scenes, unconsciously.
The I.A.T. involves showing test takers a series of images and words and measuring how quickly they associate the two.
The idea is that the test can reveal biases we've unwittingly picked up
and that may have nothing to do with what we actually believe.
— So explicitly we can say that men and women both deserve careers
But implicitly you are exposed constantly to the Brady Bunch or whatever.
And the men are more likely to have careers.
And so it's a little bit easier, a little bit faster to talk about men's names
in professional positions, than women's names.
Since it's invention the Implicit Association Test has been criticized
partly because it's not good at predicting an individual's prejudiced behavior.
But Bryson was shocked to see how closely her machine's bias
tracked the results of the test on a large scale.
— The fact that the signal was so strong was just astounding.
And it made us realize this is a really important result.
It got her thinking.
Maybe our brains are constantly processing language in the background like a computer.
And maybe that process creates our biases.
— Well, the only reason AI is so powerful at understanding ourselves is the fact that we are predictable.
We are algorithmic. And we can be explained in these kinds of ways.
In other words maybe your biases aren't responsible for our language.
Maybe it's the other way around.
— Everyone thought, oh, this means machines are prejudiced and that's what got all the headlines.
But the people in science recognize that this means this is a new possible explanation
for why people are prejudiced or how we transmit prejudice to our children.
Not intentionally, but just by letting them hear the language.
What's really a weird thing — you started out writing a technical paper
And suddenly you're talking about fundamental things of ethics.
Her work also suggests that AI's bias is not just inherited from the people who built it.
— There were some people saying of course AI is all biased
because it's written by white guys in California.
It is true that you have things that pass you by because it's not your lived experience.
but even if you just completely, fairly, take a sample of all the words out there
you're going to wind up with this and get a biased machine
which is just that you've absorbed the biased culture.
And that means just making sure developers are more diverse
a good thing for a bunch of reasons - won't necessarily make our machines fair.
Bryson's research tells us a lot about how machine's learn.
And opens the door for a lot more research on bias and AI.
But she says, it also tells us a lot about how we learn.
And that bias can be harder to shake than we think.