Machine learning and AI algorithms are increasingly trusted to help with, or
to make, extremely important decisions.
And so we like to make sure that as much as possible that they're free
of undesirable forms of bias, such as gender bias, ethnicity bias and so on.
What I want to do in this video is show you some of the ideas for diminishing or
eliminating these forms of bias in word embeddings.
When I use the term bias in this video, I don't mean the bias variants.
Sense the bias, instead I mean gender, ethnicity, sexual orientation bias.
That's a different sense of bias then is typically used in the technical discussion
on machine learning.
But mostly the problem, we talked about how
word embeddings can learn analogies like man is to woman as king is to queen.
But what if you ask it, man is to computer programmer as woman is to what?
And so the authors of this paper Tolga Bolukbasi,
Kai-Wei Chang, James Zou, Venkatesh Saligrama, and
Adam Kalai found a somewhat horrifying result where a learned word embedding
might output Man:Computer_Programmer as Woman:Homemaker.
And that just seems wrong and it enforces a very unhealthy gender stereotype.
It'd be much more preferable to have algorithm output man is to computer
programmer as a woman is to computer programmer.
And they found also, Father:Doctor as Mother is to what?
And the really unfortunate result is that some learned word embeddings would
output Mother:Nurse.
So word embeddings can reflect the gender, ethnicity, age,
sexual orientation, and other biases of the text used to train the model.
One that I'm especially passionate about is bias relating to socioeconomic status.
I think that every person, whether you come from a wealthy family,
or a low income family, or anywhere in between,
I think everyone should have great opportunities.
And because machine learning algorithms are being used to make very important
decisions.
They're influencing everything ranging from college admissions, to the way people
find jobs, to loan applications, whether your application for a loan gets approved,
to in the criminal justice system, even sentencing guidelines.
Learning algorithms are making very important decisions and so I think it's
important that we try to change learning algorithms to diminish as much as
is possible, or, ideally, eliminate these types of undesirable biases.
Now in the case of word embeddings, they can pick up the biases of the text used
to train the model and so the biases they pick up or
tend to reflect the biases in text as is written by people.
Over many decades, over many centuries,
I think humanity has made progress in reducing these types of bias.
And I think maybe fortunately for AI, I think we actually have better ideas for
quickly reducing the bias in AI than for
quickly reducing the bias in the human race.
Although I think we're by no means done for AI as well and
there's still a lot of research and
hard work to be done to reduce these types of biases in our learning algorithms.
But what I want to do in this video is share with you one example of a set
of ideas due to the paper referenced at the bottom by Bolukbasi and
others on reducing the bias in word embeddings.
