It may be hard to believe
that products based on racial stereotypes survived
well into the 21st century.
There was always these friends I had breakfast with
and they always would take out
their Aunt Jemima jug.
I haven't really heard of any other syrups,
so I usually just gravitate towards that one.
It just seems like a super exploitation
of a stereotype.
What do we want? Justice!
And when do we want it? Now!
It took widespread protests in 2020
to send some companies a wake-up call.
But even as some of this controversial branding
slowly disappears off store shelves,
there could be an even more insidious
and harder to fix form of racial bias
coded into the technologies that have become ubiquitous
in everyday life.
Start your day with natural freshness
from Darlie.
One of the most popular toothpaste brands
in East Asia, Southeast Asia,
is called Darlie Toothpaste.
Darlie is owned by Colgate-Palmolive
and a local partner,
and has a long history,
goes back to, I think, 1930s.
And it originally started out as Darkie toothpaste.
The packaging was pretty explicit.
It had a picture of minstrel singer
in blackface on the package
with the big smile with white teeth.
This came about at a time when movies
like "The Jazz Singer" with Al Jolson were very popular.
♪ Smile, Darkie, smile for me ♪
Darkie Toothpaste
gives you a cool, fresh, tingling taste.
You see how this is working in terms of marketing.
There's nothing wrong with wanting white teeth,
but when you contrast that
with this jet-black person,
the message is very clear.
If you don't want to have dark teeth,
you don't want anything dark,
use Darkie.
It will solve that problem.
They changed the name in English to Darlie,
so one letter gets rid of the problem.
They also got rid of the packaging,
so it was no longer just a picture
of a man in blackface.
On the box, it was somebody who was in a top hat,
and there was a black and white contrast,
still getting the point across,
like, look "black, white,"
but it wasn't blackface.
That said, though,
they didn't change the name in Chinese.
The issue of having these kinds of brands
with these racist legacies,
it's a bit more complicated here in Asia
than it is in other parts of the world,
in part, just because
of the different racial dynamics in Asia.
You don't have a lot of Black people.
And so, people just don't think about
these issues the same way.
So there's less pressure domestically
to do anything about them.
Meanwhile, across the world,
the United States has its own version
of the Darlie story,
in a product familiar to many people
on their breakfast table.
There were two Black families
in the town I grew up in.
And Aunt Jemima was literally
the only Black person I knew most of my life.
We used to use it a lot
when we were kids
and making pancakes in the morning
and using the syrup too.
I had good memories from my childhood,
using it all the time.
Well, I don't necessarily think Aunt Jemima was racist.
I felt like it was just a brand that named it by that,
and nothing more than that really.
At the time of Aunt Jemima's inception, 1889,
this was something where it was marketed
to White folks.
You have this very recent memory
back then of just slavery,
what it meant to people
and what subservience meant
in terms of not only a proposition
but a role in the American way.
The gentleman who came up with the idea
for the Aunt Jemima motif and brand, Chris Rutt,
he attended a minstrel show in Missouri
in the 1880s,
where he saw a White man in blackface performing
a minstrel role as a mammy
to target and make fun of African Americans,
but also African American women.
And it's out of that performance
that Rutt got together with his collaborator,
a guy by the name of Underwood,
to come up with this marketing strategy
for the Pearl Milling Company's brand
of self-raising flour.
In the 1920s,
Quaker Oats bought Pearl Milling Company,
increasing the market share and cultural presence
of the brand.
Around the same time,
mob violence and new forms of repression
against African Americans were becoming more widespread.
In the small Missouri town where the milling company
behind Aunt Jemima's was based,
a 19-year-old Black man was lynched in 1933
after he was accused of making a pass at a White woman.
That's the immediate context,
because if you think about it,
in comparison to the sort of brutal lynching of
a young African American man,
Aunt Jemima's something very different.
It's a contrast to that sort of threat of Black masculinity.
Aunt Jemima is the embodiment of
the sort of safe, loyal, trusting house servant
of slavery times.
So that imagery of Aunt Jemima was
both reassuring and nostalgic to White Americans,
and particularly White Southerners.
Nostalgia is an important emotion.
People buy products on emotion.
For generations,
frosty mornings have seemed warmer
with stacks of...
Aunt Jemima Buckwheats.
Quaker tweaked the brand image over time,
replacing the kerchief on the Aunt Jemima character's head
with a plaid hairband in 1968.
And again, in 1989, by adding pearl earrings,
a lace collar
and then-fashionable perm hairstyle.
It's got more maple in it.
In the early '90s,
a commercial campaign starring performer Gladys Knight
tried to further normalize Aunt Jemima
by showing how much African Americans themselves loved
the product.
But even after the brand was acquired
by PepsiCo in 2001,
some still felt that the minor branding shifts
over the years were mostly a convenient
corporate distraction from a lingering racial bias.
Aunt Jemima was a very strong brand,
strong enough where it was never discontinued.
And when we look at these products,
the ethos of them
and just being easy, ready-made products,
that was the whole design.
To bring in these caricatures,
to convey this ease
as if you have this person, "Mammy,"
in your kitchen, cooking the pancakes for you.
It's that easy that
it's as if someone else did it for you.
So racism is commerce,
and in terms of this marketing,
unfortunately it was hinged on a lot of this.
Aunt Jemima buttermilk pancakes.
Perfect pancakes in 10 shakes.
This is a classic example
of a corporation commercializing
and capitalizing on racial stereotypes,
and making in the process
billions and billions of dollars in profit.
Aunt Jemima is history.
Quaker Oats announcing today that it is retiring
both the logo and the name
of the 130-year-old brand.
Quaker, the latest company
to take action as demonstrations protesting
racial inequality continue across the country.
PepsiCo has recognized
that we are in one of those moments,
and that's to not lose market share,
and to seem as though it's continuing to be relevant,
they need to make this attempt.
And so, they've done that with
the Pearl Milling Company branding.
Now the Pearl Milling Company branding sort of harken backs
to the very beginning of this in St. Joseph, Missouri,
that era in relation to racial violence.
I don't know that it goes far enough
to separate it from that racist
and oppressive past
that so many African American people were exposed to.
While Aunt Jemima was probably
the most notorious mainstream brand
to face a reckoning after the 2020 protests,
a few others, such as Land O'Lakes and Uncle Ben's,
have also come under scrutiny
in the wake of the renewed conversations
around systematic racism.
As corporations continue to navigate
how to address racial stereotypes in advertising
while still protecting profits,
there's a whole other arena now
where the problem of embedded racism may be
even harder to detect
and harder to stamp out.
I think my blackness is interfering
with the computer's ability to follow me.
As you can see, I do this,
no following.
Not really,
not really following me.
I back up,
I get really, really close
to try to let the camera recognize me,
not happening.
Now, my White coworker, Wanda,
is about to slide in the frame,
you'll immediately see what I'm talking about.
Wanda, if you would, please.
Sure.
It stems from the roots,
and AI is just the next branch.
Everything is the exact same
when it comes to the application,
the technology behind it.
Because when you look at who's in the room,
it's not Black people who are testing
these AI algorithms.
The system I was using worked well
on my lighter skin friend's face,
but when it came to detecting my face,
it didn't do so well,
until I put on a white mask.
There's a really groundbreaking piece
of research that I think many people sort of associate
with this concern and movement
around AI fairness and bias,
and that's the 2018 research done
by Joy Buolamwini and Timnit Gebru.
They were trying to figure out
whether popular facial-recognition programs could
correctly pick out the gender of different photographs.
And what they found was that
the facial-recognition software that they looked at,
which was from Microsoft, IBM, and Face++,
misidentified darker-skinned women far more frequently.
And a few months later,
Buolamwini along with Deborah Raji,
published a subsequent paper that looked
at Amazon's cloud facial-recognition program,
which is called Rekognition.
They were already pitching it for use by police
and by immigration services,
and we pretty much said,
"Well, these products are already out there in the market.
Do they actually work for faces
that look like ours?
Darker, female faces."
What we found was that these models were performing
at less than 70% accuracy
for darker female faces,
while performing at pretty much 100% accuracy
for lighter male faces,
revealing a discrepancy in the performance
of these systems for different demographic groups,
and in particular, sort of endangering the lives of
those that would be impacted
by the deployment of these systems.
Eventually Microsoft fixed
its facial-recognition software,
and big tech companies,
including Amazon and IBM,
said they would stop selling facial-recognition products
to the police.
Some local governments have also passed laws regulating
facial-recognition software,
or banning its use in policing,
but there's no federal law regulating its use.
At the same time, there are other companies
that sell their facial-recognition software
to police departments.
And there've been at least three cases
of Black men who are suing
because they say that they were wrongly flagged
by this facial-recognition software.
Police came after them because
the software told them that they were a suspect
they were looking for.
This is what happened in the case of Robert Williams,
who was one of the first recorded cases
of a false facial-recognition match escalating
to a false arrest.
I picked that paper up
and hold it next to my face.
And I said, "This is not me."
I was like, "I hope you all don't think
all Black people look alike."
And then he says, "The computer says it's you."
Facial recognition is not
the only example of algorithmic bias.
Machine learning, a subset of AI,
powers many technologies.
Huge data sets are used to train machine-learning models
to make predictions.
So we want a computer, for example,
to learn the difference between a dog and a cat.
We can show the computer a lot of examples
of what a dog looks like,
show the computer a lot of examples of
what a cat looks like.
And as a result of that,
the computer kind of figures out that,
well, pointy ears means a cat.
Whiskers might mean a cat.
Floppy ears means a dog,
and uses these features in order to differentiate
between any new image that it might see
of what could potentially be
a dog or a cat.
If all of your cats are white
and all of your dogs are gray and black,
what happens if you put in a photo
of a black cat?
All of the cats
that this algorithm has learned are cats
are white.
Everything that it's seen is black is a dog.
And so, the algorithm may decide,
oh, this is black, it must be a dog.
In reality, it's a black cat.
Sometimes a data set
that lacks diversity is the problem.
Other times, the design of the model is the issue.
It can be a completely unaccountable process.
These data sets are so large.
The AI system is analyzing data
that they haven't,
they can't even know the full scope of.
It's sort of on the order of millions
and millions of examples.
The algorithm is sort of identifying patterns
in order to create a relationship
between inputs and outputs,
but those patterns are not necessarily things
that humans can understand or see very well.
These algorithms should be able
to learn over time,
they should be able to incorporate new data
from the people using it.
Even if you create an algorithm,
you check it for bias and it's fine,
the new data, the way people use the algorithm,
could work the algorithm over time
and make it biased.
You know as an example of what could go wrong,
a ProPublica article looked at software
that's used to predict future criminals.
Who is more likely or less likely to commit a crime?
And a ProPublica investigation found that that algorithm,
which was pretty widely used,
was biased against Black people.
And the issue here is that that algorithm gets used
for all sorts of things.
It gets used for deciding who gets what sort of bail,
who gets what sort of sentence,
who gets parole.
And the algorithm was considering Black defendants
as a greater risk than White defendants.
And ProPublica tracked some of these cases,
and it actually turned out that
the White defendant did re-offend
and the Black defendant didn't.
The unwillingness on the part
of some tech companies to fully disclose
their algorithms makes it hard to hold them accountable.
In April, the U.S. Federal Trade Commission warned
that it could crack down on companies
that use biased AI software.
But overall, regulation has been slow.
So we're only going to see these AI systems being
more and more prevalent,
invisibly ubiquitous in society.
If we don't do a good job of really thinking about
how we're going to hold them accountable now,
it's going to be even much more difficult later on
to try to reel things back.
Just as with the ongoing fight
to create more equitable political
and socioeconomic systems,
overcoming racism in product design may require
more than simply renaming,
rebranding or recoding.
These changes, although helpful,
only scratch the surface of the deeper problem.
It just says a lot about corporate America
when Black people ask for equality for hundreds of years,
and then they're heard
in a span of three months last year,
and the solution was changing a box of pancakes.