Transcriber: Loo Woo Reviewer: Elisabeth Buffard
The Greyhound bus pulled out from the terminal
on Washington Boulevard in downtown Detroit,
carrying a load of passengers
from the Motor City all the way to the green sides of Seattle, Washington.
With one suitcase and a paper bag filled with a few sandwiches in 1953,
my grandfather hopped off that bus and straight to the YMCA,
where he paid $10 a day for a room and ate French fries for a meal
before beginning his job as an aircraft technician at Boeing,
earning $2.38 per hour.
My grandfather was many of early innovation workers
who left their families and their home countries
to put widgets and thingamabob together
for automakers and airline industries and early hardware companies
that lay the foundation for the computer systems and video games we use today.
As our family’s resident technophile
and the man who bought me my first computer in the 90s,
Grandpa would never make the history books for his contributions
to the industry at large.
But his story and the stories of thousands of tech workers of color
who helped to lay the foundation for the advancement of technologies
that defined society.
These stories are critical
to how we determine who gets to design the future.
So Grandpa got his job at Boeing after spending two years
in a training program at a radio and electronics trade school.
Right here, if you see, second row on your left,
the handsome gentleman at the top corner. You can spot him. That’s my grandpa.
After earning his certificate, he quickly migrated west,
similar to how his parents had left Alabama and the Jim Crow South,
heading north for the promise of better pay
and safety from racial violence.
And a decade before my grandfather landed at Boeing,
stenographer Florise Spearman became the very first African worker,
African American worker at the company
and a trail of African American tech workers followed suit.
During this time, corporate companies like Boeing and IBM and JCPenney,
all of them had strict policies prohibiting the hiring of black people
and immigrants of color.
But thanks to federal law and labor activist pressure
and the demand for skilled labor following World War Ⅱ,
workers of color had the first opportunity to contribute to industries
and earn a living that were defining an American innovation.
And yet we still get to name these guys as household names,
as leading faces of innovation.
Show hands,
how many of you all can identify who everyone on this screen is?
Okay. It looks pretty unanimous. Okay. All right.
You don't have to be a techie in order to know who these guys are, right?
Now, what about this crew of folks? Anyone know who these folks are?
Well, let me help you out. So, Aicha Evans, on my right,
is CEO of Zeus, of a self-driving car company, worth $1 billion.
CEO Donnell Baird
of Black Power helps lower income communities reduce energy costs
and help to train green workers.
And Jasmine Crowe, the CEO of GoodRx,
runs a software platform that helps to divert food waste from restaurants
and grocery stores and directs them to food insecure communities and families.
So for many of us millennials who can remember a time before the Internet,
if you were in learning anything about computer programming
or anything about learning code,
you were told to aspire to become the next Bill Gates
or even the next Steve Jobs.
But rarely was there reference to people who looked like me
or came from communities that I came from.
According to EEOC data,
Black and Hispanic workers make up 15% of the tech industry,
and women make up 28%.
And yet we’re still referencing the folks who look like
the guys I showed you on the screen earlier.
That’s the dominant story.
That’s the dominant profile.
It wasn’t Gates. It wasn’t Jobs.
It wasn't Zuckerberg who made me want to fire up a computer
and made me want to start creating my own websites and games
in my early years of learning how to code.
It was my grandpa and the mentors of color and the folks from all backgrounds
who showed me that I too could be an innovator.
So seven years ago, I launched a daily tech news publication called The Plug.
We cover trends within the black innovation economy.
It was a hobby that helped me to stay sane while working in the tech industry.
But back then, there weren’t many stories published
or any kind of investigative research being done.
That was taking a look at black and brown narratives
of founders and builders
or those who were even leading advanced technologies or capital.
Despite the fact that they were building future focused work around the world.
Eventually, my hobby turned into a business.
My team and I started to build years of databases and taking stories
and all of these things that analysts and reporters were missing.
Our viral stories included documenting black owned co-working spaces
during the time when the […] of the world
were the only ones being seen.
We published a series of databases
of patents earned by historically black colleges and universities
since the 1970s until present day,
demonstrating how these institutions have long been contributing to innovation
and also supporting and raising up generations of black engineers
and scientists and inventors.
We even built an early database tracking AI researchers of color in the US, Europe,
as well as Africa.
And when we landed a licensing deal
and became the first black business publication to syndicate our data stories
on the Bloomberg terminal.
Celebrating this accomplishment
had proven that a narrative was sorely missing
and the knowledge base of innovation happening in real time.
It also kind of proved that we’ve been okay as a nation
and as an innovation industry with using incomplete data
without asking who might be missing from the story.
Now, I believe it’s time that we rethink where genius comes from
and where it comes from and what it looks like.
When we question the data and the profiles of one singular story,
we then rethink who we empower to design our story in our future.
Our most advanced tech tools are trained on the data that exists today,
but we rarely ask if said data is audited or interrogated
for which stories it actually neglects.
So let's talk about the next the recent AI ChatGPT tool.
We can ask this system to create reports.
We can ask them to help us give a list of the top 100 inventions
that would have changed society and our lives.
And the potential for these systems to perpetuate misinformation
and erase the histories of diverse peoples is significant.
There are no checks and balances around whether or not
the root data has been tested for flawless accuracy or even for bias.
So imagine the negative consequences that can take place
when professionals and investors start to task this technology
with making decisions on how to invest in the future,
if key parts of the information are actually missing.
We see this reality play out in real time with advanced technologies
that skip gathering data and stories from diverse sources.
So let’s take, for instance, facial recognition technology,
which was largely built by white guys in the lab,
and then when tested by Joy Buolamwini at MIT,
she discovered that the technology
could not identify darker skin or female faces.
So now we have folks like the ACLU
who are pushing back against some of this technology,
especially in law enforcement,
as it has often misidentified suspects,
mostly black males, and have led to many wrongful arrests.
And in its simplest form,
when representation doesn’t take place in data,
we get some culturally tone deaf technologies.
Like sometimes like when I try to wash my hands in the underrated sink
and I got to turn my palms over a couple of times.
I mean, I still wash my hands, we’re all still washing our hands, right?
Following Pandemic Robocalls!
A 2019 talk in Harvard University by Princeton University professor
and sociologist Dr. Ruha Benjamin.
She asserts “technology inherits its creative biases”.
Social norms, values, structures all exist prior to any given tech development.
So it’s not simply the impact of technology,
but the social inputs that make some inventions appear to be inevitable
and desirable.
So how do we limit the occurrence of biases in the technology that we build?
When we are intentional about how we collect and use stories and data
to see what’s been overlooked.
We spend more tim understanding and building
the value creation of technology that challenges the status quo.
This allows us to design a much more equitable future
that solves more problems than just for one group of people.
One of the stories that we covered at the plug
was of Su Sanni, of Dollar Ride.
He’s a technologist who would spend the summers as a kid with his uncle,
who was a van driver.
He drove the dollar van in the boroughs of New York City.
So today, Sanni built a company, Dollar Ride,
which developed technology to help some of these small business owners
use technology to connect transit riders who are typically underserved
by local public transportation.
So this came in handy during the first year of the pandemic,
when majority of the communities who were considered essential workers
and they worked in service based jobs, - they saw the shutdown of the MTA -
it was Dollar Ride that was able to help quickly coordinate shuttles
with local transit authorities and small business owners
to help get people back to work.
Long after the trains stopped running,
another story reminded of
is that of Yvette Ellis and Camille Terry of Charger Help.
These two black women in Compton
whose background in government workforce development
gave them a strong foundation for building a technical training model
to help put people to work in the green collar workforce.
They’re building a scalable business that leverages contracts
with electrical vehicle machine manufacturers
and training up Americans from all types of backgrounds
to become machine technicians.
And in their model, they’re turning everyday people
into innovation contributors,
where those who are certified can earn upwards of $35 per hour,
repairing the machines that are fueling the world of new electric car vehicles.
Today, we have to ensure that our research goes deeper,
our data collection much wider,
and our questions about the stories we’re inputting into our systems
much more inclusive.
So how do we do this?
So first, we have to expand our data set beyond the status quo.
If we’re leading studies, if we’re training systems,
we have to ask who was not represented in this data.
If we need more inclusive data, we have to find the right partners.
We have to take the necessary time
to get the stories of others that might be affected by the outcome of this work.
And then secondly, we removed bias language.
We have to remove the deficit narratives.
It’s nearly impossible to imagine a future of possibility and opportunity
when our language is rooted in negative stereotypes,
in negative stereotypes and stories.
And then finally, we start to course correct our history
by finding new stories to tell.
Where there are points of discrimination in our history,
we can find a people in communities
whose contributions have largely been unacknowledged or overlooked.
And by telling these old and new stories, we remove our assumptions
about the capabilities of communities and people
who are different from us,
and then we build through a lens of respect and recognition
which ultimately help us to build
better and more meaningful products and solutions,
fitting more than just one archetype.
I am grateful for my grandfather
for taking what was a three-day bus trip to a city he didn’t know with no family
and no chance to get it wrong.
He was my first example of genius in a world full of narratives
that would never leave him as such.
We have to set the standard.
We have to set the standard to understand
what stories are missing and which information arcs
are overlooking the data so that we can build
a much more interesting and dynamic future.
Thank you.
(Applause)