How I'm fighting bias in algorithms

MIT grad student Joy Buolamwini was working with facial recognition software when she noticed a problem: the software didn't recognize her face -- because the people who coded the algorithm hadn't taught it to identify a broad range of skin tones and facial structures. Now she's on a mission to fight bias in machine learning, a phenomenon she calls the "coded gaze." It's an eye-opening talk about the need for accountability in coding ... as algorithms take over more and more aspects of our lives.

Hello, I'm Joy, a poet of code,

on a mission to stop
an unseen force that's rising,

a force that I called "the coded gaze,"

my term for algorithmic bias.

Algorithmic bias, like human bias,
results in unfairness.

However, algorithms, like viruses,
can spread bias on a massive scale

at a rapid pace.

Algorithmic bias can also lead
to exclusionary experiences

and discriminatory practices.

Let me show you what I mean.

(Video) Joy Boulamwini: Hi, camera.
I've got a face.

Can you see my face?

No-glasses face?

You can see her face.

What about my face?

I've got a mask. Can you see my mask?

Joy Boulamwini: So how did this happen?

Why am I sitting in front of a computer

in a white mask,

trying to be detected by a cheap webcam?

Well, when I'm not fighting the coded gaze

as a poet of code,

I'm a graduate student
at the MIT Media Lab,

and there I have the opportunity to work
on all sorts of whimsical projects,

including the Aspire Mirror,

a project I did so I could project
digital masks onto my reflection.

So in the morning, if I wanted
to feel powerful,

I could put on a lion.

If I wanted to be uplifted,
I might have a quote.

So I used generic
facial recognition software

to build the system,

but found it was really hard to test it
unless I wore a white mask.

Unfortunately, I've run
into this issue before.

When I was an undergraduate
at Georgia Tech studying computer science,

I used to work on social robots,

and one of my tasks was to get a robot
to play peek-a-boo,

a simple turn-taking game

where partners cover their face
and then uncover it saying, "Peek-a-boo!"

The problem is, peek-a-boo
doesn't really work if I can't see you,

and my robot couldn't see me.

But I borrowed my roommate's face
to get the project done,

submitted the assignment,

and figured, you know what,
somebody else will solve this problem.

Not too long after,

I was in Hong Kong
for an entrepreneurship competition.

The organizers decided
to take participants

on a tour of local start-ups.

One of the start-ups had a social robot,

and they decided to do a demo.

The demo worked on everybody
until it got to me,

and you can probably guess it.

It couldn't detect my face.

I asked the developers what was going on,

and it turned out we had used the same
generic facial recognition software.

Halfway around the world,

I learned that algorithmic bias
can travel as quickly

as it takes to download
some files off of the internet.

So what's going on?
Why isn't my face being detected?

Well, we have to look
at how we give machines sight.

Computer vision uses
machine learning techniques

to do facial recognition.

So how this works is, you create
a training set with examples of faces.

This is a face. This is a face.
This is not a face.

And over time, you can teach a computer
how to recognize other faces.

However, if the training sets
aren't really that diverse,

any face that deviates too much
from the established norm

will be harder to detect,

which is what was happening to me.

But don't worry -- there's some good news.

Training sets don't just
materialize out of nowhere.

We actually can create them.

So there's an opportunity to create
full-spectrum training sets

that reflect a richer
portrait of humanity.

Now you've seen in my examples

how social robots

was how I found out about exclusion
with algorithmic bias.

But algorithmic bias can also lead
to discriminatory practices.

Across the US,

police departments are starting to use
facial recognition software

in their crime-fighting arsenal.

Georgetown Law published a report

showing that one in two adults
in the US -- that's 117 million people --

have their faces
in facial recognition networks.

Police departments can currently look
at these networks unregulated,

using algorithms that have not
been audited for accuracy.

Yet we know facial recognition
is not fail proof,

and labeling faces consistently
remains a challenge.

You might have seen this on Facebook.

My friends and I laugh all the time
when we see other people

mislabeled in our photos.

But misidentifying a suspected criminal
is no laughing matter,

nor is breaching civil liberties.

Machine learning is being used
for facial recognition,

but it's also extending beyond the realm
of computer vision.

In her book, "Weapons
of Math Destruction,"

data scientist Cathy O'Neil
talks about the rising new WMDs --

widespread, mysterious
and destructive algorithms

that are increasingly being used
to make decisions

that impact more aspects of our lives.

So who gets hired or fired?

Do you get that loan?
Do you get insurance?

Are you admitted into the college
you wanted to get into?

Do you and I pay the same price
for the same product

purchased on the same platform?

Law enforcement is also starting
to use machine learning

for predictive policing.

Some judges use machine-generated
risk scores to determine

how long an individual
is going to spend in prison.

So we really have to think
about these decisions.

Are they fair?

And we've seen that algorithmic bias

doesn't necessarily always
lead to fair outcomes.

So what can we do about it?

Well, we can start thinking about
how we create more inclusive code

and employ inclusive coding practices.

It really starts with people.

So who codes matters.

Are we creating full-spectrum teams
with diverse individuals

who can check each other's blind spots?

On the technical side,
how we code matters.

Are we factoring in fairness
as we're developing systems?

And finally, why we code matters.

We've used tools of computational creation
to unlock immense wealth.

We now have the opportunity
to unlock even greater equality

if we make social change a priority

and not an afterthought.

And so these are the three tenets
that will make up the "incoding" movement.

Who codes matters,

how we code matters

and why we code matters.

So to go towards incoding,
we can start thinking about

building platforms that can identify bias

by collecting people's experiences
like the ones I shared,

but also auditing existing software.

We can also start to create
more inclusive training sets.

Imagine a "Selfies for Inclusion" campaign

where you and I can help
developers test and create

more inclusive training sets.

And we can also start thinking
more conscientiously

about the social impact
of the technology that we're developing.

To get the incoding movement started,

I've launched the Algorithmic
Justice League,

where anyone who cares about fairness
can help fight the coded gaze.

On codedgaze.com, you can report bias,

request audits, become a tester

and join the ongoing conversation,

#codedgaze.

So I invite you to join me

in creating a world where technology
works for all of us,

not just some of us,

a world where we value inclusion
and center social change.

Thank you.

(Applause)

But I have one question:

Will you join me in the fight?

(Laughter)

(Applause)