'cause it's pretty rare for us to sort of sit down

and talk a little more candidly but Greg was looking up

something in the day we wanted to share.

- Yeah, I looked up the top 25 science YouTubers.

This is based on subscribers.

Putting the list together was kind of striking.

- You have Vsauce, The Slow Mo Guys, Mark Rober,

In a Nutshell, The King of Random, Crazy Russian Hacker,

Crash Course, AsapScience.

- [Mitch And Greg] That's us.

- If you couldn't tell from the photo.

- [Mitch] Smarter Every Day, Veritasium, Sci Show,

Doctor Mike, Minute Physics, CGP Grey Vsauce2,

Backyard Scientist, Vsauce3, Numberphile,

It's Okay to be Smart, 3Blue1Brown, Tom Scott,

Real Engineering, Minute Earth, Simone Giertz,

and PBS Space Time.

- There's a lot to talk about here

when it comes to gender, gender non-binary people.

- The first woman on the list, solo woman at least,

is number 24.

There are no Black people.

There are no Indigenous people.

There are no people with disabilities.

- The fact that this list is all white, I was surprised,

but I also then wasn't surprised.

This is an issue that we wanna talk about today

in the STEM community.

- And specifically in science and in science communication

on YouTube because that's the bubble

that we all kind of live and work in

and engage with every day.

- And what we wanna talk about today is race

in regards to the very significant shift

in I think a lot of, especially white people's ability

to talk about institutional racism.

We wanna talk about it with some science today,

but also give a tangible goal that we can all do

to change the institution of science.

- If you took science courses,

think about the history you learned.

Whose faces were represented?

Who was even allowed to participate in science?

Were women allowed?

Were people of color allowed?

- Did queer people exist?

They did, they did. (indistinct)

- But there's that conversation if you think

the foundation of science was kind of born out of whiteness

and born out of this sort of Eurocentric view

of how people lived there.

- Early science and taxonomy led by Carl Linnaeus

incorrectly posited that humans fell

into four distinct subspecies:

homo sapiens europaeus, the people of Europe who were white,

serious, and strong;

homo sapiens asiaticus, the people of Asia who were yellow,

melancholy, and greedy;

homo sapiens americanus, the people of America who were red,

ill-tempered, and subjugated;

and homo sapiens afer, the people of Africa who were black,

and passive, and lazy.

And Carla Linnaeus even speculated privately

that they might not even be human at all.

There was a historic moment when science started

to distance itself from religion and hold its own power,

and the power was incorrect and racist.

The king of France at the time ordered this taxonomy

to be adopted as the correct classification system

of humans, and as recent as 1921,

the American Museum of Natural History held an exhibition

on eugenics which falsely claimed that Black people's brains

were smaller than white peoples.

Charles Darwin's son attended the event

and adopted the idea of racist eugenics.

The proliferation of this false science

led to the U.S. borders closing to people from Asia, Africa,

Southern and Eastern Europe

because ill-informed and racist scientists of the time

thought people from these countries were mentally defective

and biologically undesirable.

People really do have a fundamental belief

that science isn't biased.

We just have to say we disagree with that.

- Even if the idea of science in and of itself

is this pure form,

the second you introduce humans into that

you introduce bias.

- Look at the question, look at the discussion

at the end of every study,

that's when people start to fling around their feelings.

- The way that we study science

and the things that we learn about,

if they're led by a homogenous group,

we're really only learning about one lens of life.

- A good scientist takes in information

and changes their worldview.

So they are adaptable.

So I do think that as a science community, we can, you know,

fight institutionalized racism potentially more easily,

'cause we are willing to take in the facts

and to make changes based on them.

- Here are some examples of research

on anti-Black racism and STEM.

- Studies have found that white doctors are less likely

to prescribe Black patients medication for blood clots.

They believe racist myths about Black people.

For example, that they have higher pain tolerance

and thicker skin.

They themselves don't feel like they're being racist,

but within their institutions,

they are acting with institutionalized racism.

- There are even infamous scientific experiments

that have used Black bodies without their consent

for the benefit of science.

So the Tuskegee Experiment is a famous example

where they use around 600 Black men with syphilis

and told them they were getting treated,

but they actually weren't so that they could study

what happens when syphilis goes untreated for 40 years.

And this was without consent.

- The most recent "Nature" magazine,

and there's a study about how they're conglomerating

a bunch of genomic research, fascinating research

to understand humans and to understand diseases in humans.

And at the end they say there were very few Black people,

very few South Asian people.

That's an example of today

an institution finding breakthroughs and research

and very important genomic information to help

our health that is ignoring a whole population of people

due to a system.

- There's also a lot of research on research.

So minorities are less likely

to have their research published.

They're less likely to be given raises or rewards,

even when they're at the same level

as their white colleagues.

There's also something really interesting

called the Diversity Paradox.

So they found that minorities are more likely

to have innovative ideas,

they're bringing in new perspectives for science,

but they're less likely to be given academic positions.

Studies have shown that white people get

53% more callbacks than minorities,

and then even in those callbacks,

they've started to do studies of, okay,

who actually gets the job when they're equally equipped

or equally qualified for the job.

White people end up getting the job

143% more often than minorities.

Yes, we can say we just wanna hire the best person

and put the most talented person in that position,

but I think we just have to acknowledge

that we all have biases that are unintentional,

that impact what it means to be the best candidate.

And if we can't acknowledge that root problem,

then we'll never even be able to address

sort of the more surface problems.

- Now we should talk about something that we can do.

If we are in the sciences, when we enter rooms,

when we enter meetings, if everyone happens to be white,

I think that's an important time to talk about anti-racism.

I think a lot of work can be done in rooms of white people

'cause we know those meetings are happening, okay.

We are in them all the time.

A fellow science communicator and friend Dr. Esther told us,

"It is not up to marginalized people to be burdened

"with making systemic change in institutions."

- I think as creators, we can all be putting in more effort

to highlight other voices that are different from ours.

How can we as a community, including us,

look to people that have different perspectives?

- [Greg] Dr. Esther we'll teach you fascinating stories

about marginalized figures in STEM,

Jordan Harrod can teach you about

how AI preserves systemic racism,

Mike Likes Science can teach you a rap about coding,

Jabrils can show you how AI can draw

new Pokemon with math,

Jaida Elcock on TikTok is so funny

and will teach you about animal facts,

and Anna Gifty, another amazing friend on Twitter,

will blow your mind and she actually was able to make us

even like economics.

- But we also think we really need

to talk about the institutions.

So obviously we make a lot of content on YouTube,

but also Facebook and Instagram and all of these platforms

that play a role in this.

And I think as viewers and as creators,

there are ways that we can influence

and impact those groups as well.

- In 2019, Google's workforce was 54.4% white,

39.8% Asian, 5.7% Latinx, 3.3% Black,

and 0.8% Native American.

So these stats are for America.

2019 Google's leadership was 66.6% white,

28.9% Asian, 3.3% Latinx, 2.6% Black, 0.7% Native American.

And also staggering, Google's workforce is only 31.7% women

and their leadership is only 26.1% women,

assuming gender binaries.

They didn't have information

about gender nonconforming people.

In 2019, Facebook's workforce was 44.2% white,

43% Asian, 5.2% Hispanic, 3.8% Black, 3.1% mixed,

and 0.7% other.

Looking at leadership, 65.4% were white,

24.9% Asian, 3.5% Hispanic, 3.1% Black, 2.9% mixed,

0.3% other.

This shows you how the higher you go up

in all of these institutions, the whiter it gets.

So we need to talk not only about hiring diverse people,

but also creating better promotion initiatives

to get different people to the top

creating cultures where people of diverse backgrounds

actually wanna work at these companies.

Like Google, Facebook, Twitter, Silicon Valley startups

should have to openly disclose which universities

they are recruiting from.

In America, how many are they hiring

from predominantly white schools like Stanford or MIT

compared to HBCUs like Howard or Alabama A&M?

Facebook has currently been accepting

paid political advertising on their platform,

regardless of if it has false claims or hate speech.

They say they do not want to be the arbiter of truth,

but their algorithms already control our truth.

They decide what we see, what goes viral,

and what shows up on your timeline.

And 90% of hate speech on Facebook is identified by AI.

Who designs this technology is integral to it working.

What I am asking for is public information

about the breakdown of race and gender representation

in specific jobs.

'Cause I'm curious who is building these algorithms

that are controlling what we all see.

It's about arming ourselves with that information

to challenge when we're in those meetings,

to talk to people in positions of power at Facebook

or Instagram or Google.

- At large, we also see in science when you have

more diverse voices, you get different perspective.

- Yeah, so that's a really amazing study.

Over 2.5 million papers they looked at

and they studied the last names of the papers

and they found that the more diverse those last names were,

the more likely those journal articles,

scientific articles, were to be cited.