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Computer vision started in the 1960s and computer vision
systems keep getting better.
So especially in the last 10 years,
there's been massive progress in the field.
And the systems have become much more ready
for tackling some of the applications we
may want them to tackle.
I'm Olga Russakovsky, I'm an assistant professor of computer
science here at Princeton.
I work on computer vision.
Computer vision is a part of artificial intelligence or AI.
And AI has many different definitions,
but one way to think about it is it's building smart machines.
And so computer vision is a study
of building smart machines that are able to reason effectively
about the visual world.
Computer vision can be used for any number of applications.
So you can imagine a robot that's assisting the elderly.
You have a robot that's, for example,
helping do disaster relief, so being
able to go into areas that are too dangerous for humans.
You can imagine a robot that's delivering supplies
or delivering medication.
All of these robotic applications
require computer vision.
So we're on the road to amazing breakthroughs,
amazing applications of computer vision.
But we're starting to realize that there's
an issue that we really need to think very carefully about.
And this is the issue of bias in these systems.
So for example, face recognition technology
just works much better on people with lighter skin.
And there is a very complicated question about why that is.
But at least part of the answer is the fact
that large-scale modern datasets despite being large scale,
actually are missing certain groups of people,
and that is dangerous.
For example, you could imagine if a computer vision
system is trained to recognize pedestrians.
And you will deploy this in an autonomous car.
If people with darker skin are underrepresented
in computer vision datasets computer vision systems just
don't work as well for them.
And when these systems are being deployed to make life or death
decisions, like in autonomous driving scenario, that
leads to very tragic errors.
And I said, Oh my God, this is the topic we need to work on.
So we did that with Revise, which
is a tool that my colleagues and I built and published
this year that analyzes a computer vision dataset
and tries to identify patterns in the data that
may be problematic.
And then it poses a set of questions
to humans asking, 'I've identified
this pattern in the dataset.
Is that OK?'
And proposes some potential suggestions
for how to mitigate these patterns if the user decides
that they're undesirable.
So, now we ask the question of who are these people,
and right now we know that the group
of people making these decisions is very homogeneous.
10% to 15% AI researchers identify
as female, only 10% to 15%.
We know that out of tenure-track engineering
faculty in the U.S., less than 4%
are African American, less than 4% are Hispanic.
And this matters because that means
that this technology, which is going to affect all of us,
is currently being built and deployed
by people who don't necessarily represent all of us.
So a few years ago, my Ph.D. adviser Fei-Fei Li,
my longtime friend and mentor Rick Sommer
and I started the AI4ALL foundation,
focused on identifying, educating, mentoring
and supporting the next generation of AI leaders.
So we go out and strive to find people
from groups who are traditionally underrepresented
within AI who are high school students or middle school
students.
And get them excited and engaged in this field.
AI4ALL is my passion, it is something
that I care very deeply about.
And we won't stop our work until the field is actually
representative, until we actually
have a diversity of voices in the room building these AI
systems.
I'm Olga Russakovsky, I'm a forward thinker.