✊🏽 ✊🏾 ✊🏿 Why We Matter: Meme Styles and Tracye Shaw

Meme Styles is the Co-Founder and Chief Technology Officer for Measure, and she is also the Co-Founder and Chief Technology Officer for Measure.

Meme is a really great organization because we are in the high-tech industry.

Their organization is based around data and how you use evidence -based reasoning to approach racism and issues of social justice.

So I think the work they do is absolutely fascinating. So a little bit about Meme.

Meme founded Measure in 2015 to promote the use of evidence-based projects and tools to tell real-life stories behind the numbers.

As a catalyst for systems change, Measure has grown to a fully operational nonprofit social enterprise that provides free data support for Black and brown-led organizations, while charging white-led organizations the full rate of Measure's services.

This basically contributes to an anti-racist revenue model and allows Black and brown organizations to have parity in access to data.

So, so far the organization has provided over 1300 free data support hours to Black and brown-led organizations.

They are also responsible for strategic partnerships with the University of Texas, Texas Southern University, and even more.

And their goal is to disrupt traditional research in exchange for Black and brown-led lived experience protocols.

So Meme Stiles is a real powerhouse. She is an ARO fellow, past chairman of the Miss Juneteenth organization in Austin.

The Austin Police Chiefs, she's also received the Austin Police Chiefs Award of Excellence, Austin 40 Under 40.

She is also the past chairman of African TV 5 and the Austin Black Chambers 2017 Community Leader of the Year.

I know, it's a lot, right? She holds a Bachelor of Science in Communications, Master of Public Administration with a concentration in national security, and has also been certified in performance measurement through George Washington University College of Professional Studies.

And on a personal note, she's the wife of a U.S. veteran and a mother of four.

And I have to say, I am really impressed. You just had a baby a few weeks ago and are out here joining us.

So I really appreciate you taking the time away from your baby to join us.

No rest for the weary, absolutely. Awesome. And Nick, Nick is the Chief Technology Officer of Measure.

He majored in sociology and anthropology and has an established career in IT.

By profession, Nick Collins is a solutions architect with over 14 years of demonstrated experience, specializes in application delivery networking.

He also has vast experience in cybersecurity and managed services.

He also has extensive experience in financial and government environments, and subsequently their regulatory and compliance requirements.

Measure allows Nick to combine both his passions for social justice with his professional experience to make meaningful change by leveraging data and education to mobilize communities to eliminate social disparities.

Nick is a husband and a father to a wonderful 10-year-old girl who loves jazz, football, and technology as much as he does.

So tonight, what's even more special about tonight is that Mimi and Nick have been working on a special project called Techversity.

And they're going to be sharing that with us today.

So the work that they've put into this project is coming out today just for us.

And obviously they'll share it more after this, but this is the debut of Techversity and I'm really excited.

So with that, I'm going to hand it over to Mimi.

It'll take about 30 minutes to go through all of the project.

And then we are going to take questions from the audience. And then I'll also ask a few questions of Nick and Mimi.

So with that, Mimi, you can take it away. Thank you, Tracy.

And absolutely, feel free to put questions in the chat. I think that's always kind of the best part whenever we do these presentations.

It's like, you know, what ideas does it jog inside of your mind as we're kind of walking through some of this information and then some of this incredible data that you're going to hear from Nick?

I think I forgot to mention, too, that email, if you want to send us an email, is livestudio at Cloudflare.tv.

Again, that's livestudio at Cloudflare.tv.

Fantastic. Well, I'm going to go ahead and attempt to be as tech savvy as I claim to be, share my screen the appropriate way.

Let's see here. Almost there.

Presenting. And boom, we should be live here. Does that look good to you, Tracy?

You are good to go. Nice. Awesome. Well, thank you guys so much for giving myself and Nick a couple of moments of your evening, of your time today to talk a little bit about this presentation, techversity, addressing the lack of diversity in the tech space.

And so, again, my name is Mimi Stiles. It really is Mimi Stiles, too.

I tell everybody that I'm the original meme, although the meme came later, and so people are like, oh, are you sure it's a meme or is it Mimi?

It's Mimi.

So thank you for bearing with me on that. And I just want to, you know, I get to be the president and the founder of this amazing social justice organization that literally allows people of color to be the data collectors, to be the analyst, to use our lived experience and to quantify it in a way that really does facilitate change.

We've seen some incredible things happen because we're using data as, you know, in order to drive change.

And so we're going to talk a little bit about that.

Again, feel free to post some questions in the chat. But first of all, let me kind of tell you about the organization and who we are and what we do and why we do it.

And so again, you know, it's our target problem. I always look at it as an opportunity, though, is that lived experience and quantitative data about complex social problems, huge problems, right?

Whether it's the over criminalization of black girls in school or whether it's traffic stops being done more disproportionately on people of color or, you know, or if it's the reality of like maternal health care for people of color.

We feel as if that data, quantitative data about these problems are not used effectively for change.

And that's due to a couple of reasons that we specifically like to address.

And the first thing is historical and structural racism, right?

And the lack of or the lack of accessibility of equitable data tools.

And then also the lack of village support. And so at Measure, we work to solve this issue by providing free data support to black and brown led organizations.

That's our solve. And so what we've done here and what I'll explain to you what you see on your screen right now is that we have four tools at Measure.

And each one of our tools have gone through an extensive community led process just to make sure that we are authentically representing the people through our work.

Making sure that we routinely even reevaluate our own tools, you know, to make sure that the process is truly anti-racist.

So often even nonprofit organizations that are working to be anti-racist and so forth don't do the evaluation and the groundwater analysis that they need to do in order to look at themselves in the mirror.

And as evaluators, as social scientists, as techies, you know, and so forth within our organization, we have made that a priority.

Again, we are led and ran by mostly black women and also this incredible black man who's also on our leadership team.

But that doesn't always matter.

You have to take the time in order to do your own evaluation of your work.

And so again, what you see here are four of our evaluation tools that we use with our clients.

And so and you'll also see what they actually receive when they register for our support.

Again, for black and brown led organizations, this is 100% completely free.

And then we ask white led organizations or organizations that have the economic means to pay for it to do so.

Because that then allows us to provide this free service to black and brown led organizations who do need it.

And so we realized very early on at Measure that without an equitable framework built into tools, built into tech tools, built into public administration tools or models, we risk the continual perpetuation of racism within our largest institutions and social services in America.

Again, we realized this very early on as a grassroots organization.

As a matter of fact, I was asked to be on a panel about community policing back in 2015 or so.

The time keeps flying by. But I was to be on that panel and we were talking about community policing.

And I looked around to my left and to my right.

And I saw the chief of police. I saw the mayor. I saw city council members and so forth.

And then myself, I knew how I was supposed to show up in that space.

I was supposed to be the angry black woman activist who was to speak truth to power.

And let me tell you something. I still did that. Tracy, I did that.

But I did it in a different way. I said, you know, Tracy, I said, hey, you know, what is the research methodology behind, you know, behind the numbers?

Behind the survey that says that black and brown people or people in general in Austin, rather, are happy or are satisfied with the way that they are being policed?

What's the data?

You know, where are the KPIs that are saying, you know, that are justifying community policing if that's actually a priority for you?

And quite honestly, I didn't get much of an answer there.

You know, it started to be this echo.

The mayor said, and mayor Adler will tell you, he said, you know, yeah, you know, like Mimi said, where's the data?

And the chief started to ask the same questions.

And so I like to say that data started to become very sexy at that point. You know, on that panel, when it comes to understanding public safety and so forth.

And so, again, I was challenged at that point by chief, you know, by chief Manley and by chief Acevedo, who Manley was actually kind of coming into that space around that time, to go create a solution.

And the solution for me was, and with my partners, was the measure care model.

And so this, that's our bread and butter. That's our biggest, that's our big approach to tackling very systemic issues using data.

Let me just talk one more time about the measure care model, just so I can give you guys a little bit more background about that before I get into some deeper stuff.

But the care model really is about bringing together a care team.

And that care team are people from all walks of life that typically fall within these four categories, community, advocacy, resilience, and evidence.

And so whichever, you know, whatever makes you feel most comfortable, whether you're a person that loves to, you know, that loves to use data and evidence and you would fall under that E.

If you are a person that is willing to advocate with the community to address social disparities, you'll fall into that A.

The R is folks that like to generate solutions that strengthen community resilience, right?

And that C again is, you know, making sure that community is involved from the very beginning.

And so again, we have these four tools that we are able to, you know, evaluation tools that we're able to offer to our clients.

Do you think that the use of data also makes social justice or racism issues more easier to access from different parties?

What I mean by that is like a lot of those issues are just fraught with emotion.

They are every, it touches to the core and people on either side of it and data doesn't have emotion.

Data, the data is what it is, right? It's hard to argue with data.

It's hard to get angry at data. I mean, it's just there in black and white.

Do you feel like that makes it easier to advance the causes that you are advocating for?

Do you think it actually makes it harder since it does in some ways divorce emotion from it?

You know, it's a little bit of both there, right?

I mean, data, the word that we are all for is equity. And in order to be equitable, we have to have numbers.

We have to understand the problem through numbers.

Data allows us to have these very clear conversations, you know, at the table without that emotion or without that, you know, that experience.

But at the same time, lived experience is just as important as the numbers.

I always say, Tracy, I say, you know, unfortunately, I have to prove my experience in the workplace or I have to prove my experience when I go to the hospital or I have to, my son has to prove his experience with being pulled over several times through data.

So it's unfortunate, but it's the unfortunate reality that we live in.

It's a little bit of both.

Yeah. And so what I'll do now is I'm going to talk a little bit about the, about racism.

I'm just going to be honest here. Is that okay? You know, we're going to get a little bit real.

And then I'm going to pitch it on over to Nick. But so according to the National Action Committee on the Status of Women, women, you know, so when you talk about racism and when you talk about anti-racism, you have to understand what anti-racism is, right?

And so it's the active process of identifying and eliminating racism by changing systems, organizational structures, policies, and practices and attitudes so that power is then redistributed to and shared equitably.

So it's about this active process of redistributing and sharing power.

So that means that when you're in the boardroom and you look from your left into your right and you don't see anyone that looks like myself, Tracy, or Nick around you, then that power has not really been redistributed.

It's time to speak up and to be anti-racist.

So the act of being anti-racist is actually doing something about it.

And these four levels, and I guarantee you that there's more than four levels to talk about, but the four levels that I just want to quickly go over is the first one, individual or interpersonal racism.

This is the racism that it refers to an individual's racist assumptions or beliefs or behaviors.

It could be conscious or unconscious.

And so interpersonal racism occurs between people, right?

Once we bring our private beliefs into our interactions with other people, that's when racism then turns more interpersonal.

So you'll see this gentleman right here.

He's kind of looking at this other guy. He may not say anything.

He may say something, but he may not say anything. It's between those two people.

And then you have on this next bubble, you see some kids, white kids in the back, and they're looking at this brown boy in the front, and they may be saying something mean.

And that's internalized supremacy-type racism, right? And so this is internalized supremacy racism is the personal conscious or subconscious acceptance of the dominant society's racist views, their stereotypes, their biases of racial and ethnic groups.

It gives the rise to what we've seen, you know, just very recently.

It gives this rise to patterns of thinking and feeling. It's what we've known for 400 years.

So this is the more prevalent under, you know, racism that you see, that overt feeling and reality of racism.

And so then there's at the bottom there is that institutional racism.

And so sometimes this can be a little hidden, and you don't really know that it's happening.

And this is where it's time to go out and root out, you know, racist actions that you may not even realize.

So institutional racism really refers specifically to the ways in which institutional policies and practices create different outcomes for different racial groups.

And so it's a pattern of social institutions like the government or like schools or like your company or like banks or like courts of law whose effect is to create advantages for whites and oppression and disadvantages for people that are not white or people of color.

And so that's that institutional-type racism. And then you see over here what we've been fighting very, very hard for, for many, many of years.

And I tell all this, I tell folks this story. My grandfather in the 60s and the 70s, he was the police commissioner in Los Angeles, well, northern LA.

And inside of his home was my father and his uncle, I'm sorry, his brother, my uncle, and they were both Black Panthers.

So you can kind of understand the dynamics that kind of went on inside of that house, right?

Fighting for, fighting against the systemic structural racism and that's the normalization, really, and making racism legitimate.

It's historical, it's cultural, it's institutional.

It routinely advantages whites while producing cumulative and chronic adverse outcomes for people of color.

That's structural racism.

So I hope that kind of helped you to kind of understand these different levels of racism.

I think that context is always really important. And I also want to just, you know, encourage folks that are listening and watching this today to evaluate your own thoughts about what racism is.

And I put this little activity, if you're comfortable, you know, because we, you know, we want to make sure that you're comfortable, right?

And so, not really, I'd never really make people feel too comfortable to feature them with you.

But anyway, if you are, answer the following prompt.

I mostly feel blank when discussing race because. And allow yourself to kind of evaluate your own feelings and thoughts.

And I think I'm actually going to pitch it on over to Nick just so that I don't go over time here.

No problem, Amy.

So, as you were discussing earlier, Tracy, as far as using data and how it could possibly divorce people from emotion.

I think that the way I like to look at it is that you use data as a conduit for candid conversations.

Between people that have different shared experiences.

So, if I'm talking to somebody that has a shared experience like myself that's within my community or cultural identification, typically I don't need to use data.

It's kind of like preaching to the choir.

So, you find the inherent, you know, value in this when you're trying to reach across the aisle and you're trying to speak to somebody else that might have a different perspective.

Good, bad, or indifferent, you know, it will take people from all walks of life to kind of inch this thing forward.

And we found that data is the best conduit for doing that. So, with that said, we wanted to kind of go over the lack of diversity in tech and to kind of look at why we think there's a lack of diversity.

And there's, you know, a few misnomers that we might cover here as well.

So, since tech giants, for example, like Apple, Facebook, Google, and Microsoft started releasing these diversity reports back in 2014, tech companies have been increasingly getting under pressure to diversify their workforce, and that workforce is predominantly white, Asian, and male.

So, for example, Google provided data that showed that only 1% of its tech staff are black, 2% are Hispanic, and the one well -represented minority group was Asian, who made up 34% of the total workforce, while the remaining 63% were white.

So, Apple CEO Tim Cook, he wrote an open letter to his employees promising that the company would be as innovative in advancing diversity as they were in developing products.

And as we all know, since 2014, Apple has come out with AirPods, the Apple Watch.

They've made some innovations from their model of phones from 6 through 11.

Microsoft, in kind, has acquired LinkedIn. They expanded into the cloud with their offerings from Azure.

Facebook now, I believe, is at roughly 1 billion monthly active users.

And Google has achieved quantum supremacy, which, if you know anything about quantum computing, that's huge.

So, with all these innovative achievements, we still have not seen these companies find a way to innovate a viable route to diversifying their workforce.

Next slide, please.

So, the type of research that we were using for sources of data for this came from large tech companies' own diversity reports, as well as something called the EEO1 data reports that these companies submit to the U.S.

Equal Employment Opportunity Commission.

So, it's self -reporting. Between 2014 and 2019, which was the most recent years for self -reporting, the share of U.S.

technical employees that were black or Latinx rose by less than 1% at Microsoft or Google.

If we were to look at Apple, at their most recent EEO report, the percentage of black technical workers was unchanged at 6%, which is less than half of black America's total population, which stands at roughly 13%.

If we look over at Facebook, at their most recently filed EEO1 report, the technical workforce is 23% female, which is up from 15% in 2014, and that's definitely going in the right direction.

And Google also reports similar gains amongst female employees as well.

Next slide, please.

So, it's been commonly held as a belief by most people that, you know, when you're having these kind of water cooler discussions about the gender gap in tech, that it's primarily a pipeline issue, and that there's just simply not enough girls studying math or science.

And there's been a recent study that was conducted by the National Girls Collaborative.

It used data from the National Science Board that indicated an equal number of high school girls and boys are actually participating in STEM electives.

So, if you look at the National Center for Education Statistics, they provide data that shows that top universities actually graduate black and Hispanic computer science and computer engineering students at twice the rate that the leading technology companies that we were talking about earlier are actually hiring them.

So, although these companies state that they don't have qualified pool of applicants, or that's something that I've heard myself when I talk to people about this, the evidence just does not support that claim.

Furthermore, the NGC study also finds that 50% of the introductory computer science students are actually women, yet the U.S.

Census Bureau's reports from last year show that twice as many men as women with the exact same qualifications were working in STEM fields.

Next slide, please. So, if the lack of diversity in tech is not a pipeline issue, then why don't we see a greater representation of minorities of women in the STEM industries?

And the answer, I believe, is that we don't see more progress simply because the pipeline concern is not the primary reason for the discouraging statistics, but rather there's a bigger issue, and that's culture.

So, we can attempt to solve these problems by educating more women and minorities, trying to challenge hiring practices, all of which are, you know, those are important initiatives, but the underlining issue that must be addressed to solve this, in my opinion, is the hidden and often overt discrimination that prevails in the tech industry.

So, the reality that we see when we look at all this data and go through some of these research reports is that gender and racial bias is ubiquitous in the technology industry, and it forces a lot of talented female and minority employees to leave.

A lady named Karen Snyder actually worked with Fortune Magazine, and she was a former senior leader at Microsoft and Amazon and started her own company, and she went around and interviewed 716 women that held tech positions at 654 companies across 43 states, and on average, these women worked in tech for seven years, and then they just simply left.

So, they would get the jobs, be in the industry, and then choose to do something else, start their own business, et cetera.

So, when Karen asked, why are these women specifically, you know, women of color specifically, why are they opting out?

Well, 192% of them that were interviewed, excuse me, I'm misrepresenting that, 192 women, 27% of the total women interviewed, they cited discomfort working in these companies and that overt and implicit discrimination was a primary factor in their decision to leave.

Several mentioned discrimination related to their age, their race and sexual orientation, in addition to gender and motherhood.

They also stated that a lack of flexible work arrangements, the unsupportive work environment when it came to, you know, childcare and salary that was inadequate to pay for childcare.

All those things contributed to them and their decision to leave and try to find greater passion elsewhere.

And then a subset of this study and interviews were female scientists.

So, 100% of the 60 women scientists interviewed in this report reported gender or racial bias, and nearly half of all the black and Latina scientists reported that at some point in their careers they had been mistaken for administrative or custodial staff.

So, the majority of black, Latina and Asian American women that were interviewed also stated they felt compelled to always have to provide more proof, as Mimi was talking about earlier, trying to provide more evidence to their coworkers that they were as competent as their male peers.

For example, if you've got to go to the hospital, you know, you're really having some of the issues, say, you were having, et cetera.

And more than half of those participants reported receiving backlash when they expressed anger or assertiveness at work.

And finally, 64% who were mothers experienced discrimination in gender stereotyping.

Next slide, please. I know that was a whole lot of data there and a lot of stuff to cover, and we can provide some links and follow-up info for that if you guys would like to look at that yourself.

So, our route to resolution, how do we get to where we want to be? And we found that, you know, companies can hire more minorities than women, but without addressing that critical issue pertaining to culture, they'll come in and then they're going to leave and you're back to square one.

So, we will not experience any diversity until we identify an evidence -based way to change culture.

And one way to do this is through a metric-based bias interrupters.

It's an approach or rather a tool that was brought up through a University of California Hastings study called the Boosting the Retention of Women in STEM Pipeline Study.

And that study suggests that introducing a new approach to organizational change to interrupt gender bias called metric-based bias interrupters would accomplish this with data as opposed to emotions.

I do want to pause real quick and just note that this bias interrupters model is wholly created and owned by UC Hastings and not affiliated with Measure M in any way.

So, with that, bias interrupters are going to be tweaks to your business systems, whether it's performance evaluations, promotions, compensations, et cetera, that are going to interrupt and correct implicit bias in the workplace.

And they often do so without having to mention the word bias.

So, what's going to be needed with this is bias, excuse me, evidence -based tweaks that interrupt the constant transmission of bias in basic business systems.

And these are going to interrupt and try to change systems, not people. So, we're not trying to have people go and take trainings to, you know, make themselves more diversity trainings or things that try to change who you are as a person.

Instead, we're trying to change systems and interactions between people and let people be who they are, you know, within reason.

And see if that approach works a lot better based off of the data provided by the University of Hastings.

So, as you see there, if you want to take a screenshot, you can go ahead and go to those two links and learn more about these bias-based interrupters and also learn more about the study because I find that with a tech audience, a lot of us out there do want to read and go through the stuff ourselves, not just take somebody's word for it.

And I wanted to make sure that, you know, if this is something you're truly interested in, you have a way to review that material.

And that's all I have for those slides.

And so, again, we're not really in groups here, but these are two pretty interesting questions that you can ask yourself that you can use for your own self-evaluation as an evaluator.

So, number one is, what did you hear? And then number two is, what will you do differently in response to this information?

And when you ask yourself those questions, I think you come into the realm of anti-racism.

You know, what will you do differently is what really changes the world.

And so, we also wanted to make sure that we left you guys with some information on how to contact us.

Here is our email and our website. Our website is active. It has a lot of the information that we've, or a lot of the data and things that we've collected over the last couple of years, reports that we've put together.

And it's also an opportunity for people to volunteer.

It's something that we, volunteers keep us moving at Measure.

And very soon, we are going to be recruiting for who we call certified Measure educators.

And these are volunteers that are trained on our tools, the ones that I told you about earlier, and that just want to use the power of evaluation and the power of data to make change.

And so, feel free to email us if that's something that you're interested in.

You can also go directly onto our website and choose to volunteer there and everything else.

You can also donate on our website.

I mean, you know, obviously, we are a nonprofit organization that needs support.

We've done a lot of extensive study on, you know, just the inequities that Black-led organizations get when it comes to philanthropic support.

So, we totally, you know, are very appreciative of every donor that we have at Measure.

Thank you all very much.

I'm going to pause for a second and see if we have any questions come in.

But while we're waiting to see if questions come in, I wanted to dig a little deeper on why you started Measure.

Because, you know, you first came on my radar actually after there was a teenager here killed in Austin named David Joseph.

And it was a horrific, it was just a horrific killing, to be honest. But I remember you were part of the APD kind of workforce that was going on at the time.

Was that your first foray into working with data and communities and kind of institutions?

Or did you have a history doing that before? How did you get into this line of work?

Yeah, so, yes, so the horrific murder of David Joseph, you know, really kind of thrusted me into this world of activism within Austin.

I was first, so when I first started my work here in Austin, I was with the Austin Justice Coalition.

Yeah. And so I worked with Chas Moore. We had put on like a national summit and and, you know, kind of just just really working, working, working in order to address, you know, the issues of police brutality and police violence.

Right.

And so we, you know, led marches. We public die ins. I mean, just, you know, and then when when David Joseph happened and, you know, bless his mom, Katie, who now lives around the corner from me, you know, I'm saying like I I saw my son and David.

Right. And and his amazing brothers, too. Right. That are now part of my family, I feel like.

And so that was where, for me, I felt like, OK, something has to give because we are and David was not a major hashtag, as we see so many now are.

And so that was where David really wasn't and I didn't understand how a 17 year old black boy could be running down the street with no clothes on, obviously no threatening, you know, skinny skinny 17 year old, and then for Officer Freeman to feel threatened.

Also a black man to feel threatened by this young boy running, you know, and so that definitely was.

That was where I decided, OK, this has to something has to give.

Right. And so I did start a lot of my work around that time. It was a little bit before then, though.

But when David happened, it just rocked. It rocked my world.

Yeah, that was something that I just couldn't get out of my head when I when it when it when it flashed across the news.

It's not something you can easily forget.

Like there's no there's no way to explain that kind of outcome. So, you know, how did you know what was the reaction from the organizations that you were talking to who had the inherent kind of racial issues were you well received?

Was it hostile?

Was it a little of both? You're saying like the police department, because we held the police department accountable.

I mean, that's where I remember.

Yeah, I went on a on a, you know, on a march for justice. I'm still there.

Right. I still don't think justice was served at all. And I always hesitate about even talking about it right now because it was so traumatic.

Right. But, you know, for I felt as if that officer needed to be fired and also prosecuted.

And that officer was fired.

We were able to have that happen very quickly. And, and, and in a way, the police department, you know, they worked along with us, they listened, you know, Chief Acevedo at the time, you know, went on when I was on a press conference with him, myself and Chaz and my husband and several other people.

And he said, you know, Black Lives Matter.

And we thought that that was important at that time.

You know, the mayor, the same, the same response was received from the mayor, but we still sought justice.

And that justice to me, you know, is not in the form of a civil lawsuit and money being transferred.

It's about prosecution. It's about holding the system accountable.

And unfortunately, we still lack real true justice.

And as you all know, it has transcribed even since then. Yeah, you said something that I want to pause on for a second.

You know, you mentioned when Art Acevedo said, you know, Black Lives Matter, that that was important.

And that touches a chord with me because what I have found is that when you're advocating for greater diversity, whether it's women, whether it's black or brown, it resonates more when it comes from someone who doesn't look like me.

Right. And that's an unfortunate state of being.

But the reality is, if one of my white male counterparts posted, you know what, women thrive here and we do really well here, we get a better outcome from that.

How do you, how do you navigate when to really press an ally, like you really need to be here and say this?

And when do you wait for them to volunteer?

Like, what does that look like for you? You know, I'm, I'm very and that's a really good question.

First off, Tracy, but I am very clear about centering black voices and centering the experiences of black and brown people.

Yeah.

And so I'm not really one to have any real true expectations because we've been so disappointed for a very, very long time.

Yes, yes. I'm kind of taking the same tactic.

Absolutely. So I mean, I appreciate, I appreciate allies and co -conspirators and people that are just, you know, as, as, as angry about racism as I am.

And when they show up in the way that's authentic and not in this way of like, I want to be, you know, I want to save the black race or, you know.

And so when, when people show up, whoever it is, I mean, I take a very human approach approach to this thing called anti-racism too.

Whoever it is that shows up in the fight to end systemic and structural racism, more kudos to you.

I'm not going to press anybody.

I wouldn't, you know, press people that look like me or people that don't look like me.

It's those that are in this fight for humanity that I, that I appreciate.

And, and again, we have to realize that this structure of race, it's completely made up.

It has very real consequences, but biologically, I believe I'm black, you know, I'm black because I believe I'm black, you know, you're white because you believe you're white.

It's, it's, there's no real biological framework around it.

Right. And so Baldwin taught us that. He really made that very clear. And so, and so anyway, I hope that answers your question.

It does. It's a hard one though.

It's a very hard space to navigate, especially if you are one of the only black people or voices within like an organization structure.

You know, you're, you have these expectations for people to show up, but sometimes they just don't.

Yeah.

Yeah.

Because of their position, their platform and towards doing what is right universally.

Gotcha. Sorry, go ahead. Oh no no I was just saying he's absolutely right like holding people that are in positions of power, and especially elected positions of power, that accountability is crucial.

Yeah, I agree. So Nick, do you have you navigated it the same way do you have any expectations from allies or are you in the same boat with Mimi.

Um, I think my expectations from allies again split there amongst among your relationship so my friends and family members I think my expectations are a little higher because I think we have something in common as to why we commune, conversate and hang out with each other.

But you know, allies that I find for example like Chief Manley or people that work in public positions or folks that I try to work with as allies rather, I find that it is difficult at times because you know they can't move unilaterally.

So a lot of times you get conversations and you get promises made, but you also have to realize it's a large organization.

It's not something that just because the person's at the top, may or may not agree with you that they can just unilaterally change stuff so I think as long as your heart and mind is in the right place.

That should hopefully lead you to constantly try to take that step in that initiative.

Got it.

Okay, so we had a question come in from Fallon, who says, how can employees of tech companies use data to hold them accountable to their goals, whether it is DEI related or otherwise.

Can you share some examples of the metric based bias interrupters in action.

What, if anything, can tech companies do with data to move the needle on diversity, equity and inclusion.

There's a lot in there.

One at a time. So I think let's take the first part of that. And how can employees of tech companies use data to hold the companies accountable to their goals, whether it's DEI related or otherwise.

And I think there is some diversity goals right we want to reach 6% or we want to reach 15% you know there are always the goals that are out there.

Yeah, no that's that's that's a great question. And so, you know, of course, I would have ideas and when it comes to diversity, equity and inclusion the work that you that we're talking about here today everything else I mean I'm not really sure because I'm not I don't work within that that industry, but what I would say is that do just what we're doing right here, start having these conversations I, a lot of times what we do at measure is that we have, you know, like, you know, just data gatherings we call it community data gatherings where we pull the data.

So have, you know, question your organization about their hiring practices question about the mobility of people of color inside of the organization from one level to the next, you know, start making those questions and then bring folks together and have a community data gathering.

I think that's a very quick and, you know, solution to having clarity and transparency within an organization.

And so once the numbers are out there it's hard to refute that there might be a problem.

And so when you understand the scope of the problem, if there's a problem, there may not be, you know, then you can then start to tackle it.

And so, yeah. Yeah, I do think the acknowledgement of the data is an important piece and Nick said something a minute ago that I encounter, and just from day to day, you know, most companies want to do better, not all of them, but most want to do better.

And what I find is there could be one person in an interview process, you know, who's a blocker, frankly because not because they even know that they are, but it could be a lack of training, it could be something unconscious in their demeanor, it could be anything.

And so, part of what I feel like is my responsibility is making sure that that person, if they're on my team that they are well trained if they're not on my team making sure that, hey, HR business partner let's talk about how we do company wide interview training where we're removing that from the process.

How do we kind of D kind of cleanse the interview process of the bias that may be there without you even realizing it.

I think you touched on something there in the tech field specifically with hiring from the HR perspective I think one thing that I try to help them institute at my company that I'm at right now was working with HR to ensure that a lot of the screening also involves people with a tech background.

So you see a lot of times that people in HR for, you know, plausible deniability for lack of a better term wants to see certain pieces of paper for your school degrees on there, etc.

And if you've worked in IT long enough you know there's people that are qualified that have certifications, actual experience that may trump somebody that has just, you know, a piece of paper behind their name.

And so oftentimes people that are in HR have a culture and that goes back to what we're talking about a culture of I need to check this box before I even let them in the door and then you find out people, for example, Edward Snowden.

I had barely graduated high school but was working for a contractor of U.S.

government and was put into a position because of his aptitude and what he was able to accomplish.

And so you risk missing out on people like that and also people of color of that caliber when you have folks in HR that are not in tune with certain realities in IT and hiring practices.

So that again leads to culture and I think the other part of the question was about the metrics and the bias-based interrupters.

And to address that I would like to say that measure, for example, we got our name because we, you know, said how can you manage something if you don't measure it, right?

So the very first thing you have to do with the bias -based interrupter system is develop key metrics to pinpoint where those bias exist and assess the effectiveness of the interrupters within the toolkit.

So I don't want to get too into the weeds about that toolkit just mainly because it's not something that we created but I would advise that people go there and just kind of get ideas.

And then you can present this to your company saying, hey, if there's a diversity board or some kind of group that works on diversity at your tech company, I think showing up with some statistics and also, again, with the data from bias-based interrupters website that we presented, you can say, hey, there's a new approach that we might want to look at.

And I think you can get a lot more buy -in when you show up with a solution as opposed to just also citing a concern.

So cite the concern. Propose this as a solution.

And, again, highlight the fact that the bias-based interrupters model uses data and it tries to change business systems, not people, because a lot of the resistance you're going to see and I've seen anecdotally is that people do not want to have implicit bias training.

People don't want to, you know, they have a knee-jerk reaction to certain things that they feel are trying to change who they are.

And so you can use this, again, as a conduit to have these candid conversations and then say we need to assess what's working and then go from there.

Yeah, you know, that's interesting because, you know, I think some companies now have taken the tactic of advancing diversity, equity, inclusion by saying, and this is true, that diversity makes us better.

We produce better products.

We have better insight. We are better able to address all types of people.

All of those things are true. But I think sometimes that allows them to sidestep, you know, that it's the right thing to do.

It's not just the profitable thing to do.

It's not just the most productive thing to do or the thing that allows us to reach more people.

It's simply the right thing to do. And I think I wonder if you feel like that lets them off the hook or if that's a better way to approach it.

It's really true.

I do agree. Go ahead. This is an opinion question. I don't think there's a right or wrong, but I just find it interesting.

No, I totally agree.

I think that's my preferred way to approach it, Tracy. Go ahead, Mimi, I'm sorry.

No, no, it's totally fine. I mean, it's hard to change the hearts of men.

I mean, period. When I say men, I mean man, like all humans. And so, I mean, if, you know, if that's the way that they have to approach it in order for change to happen, I mean, that's it.

It's just, it's unfortunate, right, that you have to really beat empathy.

Yeah, I mean, it is effective, right? I don't actually have a real issue with it, but I just thought it was, it's an interesting shift to steer away from the moral righteousness, I guess, of equality or equity and to shift it more to a productive statement.

It's effective, but it's an interesting shift.

We have one more question. And I think this one actually might be a good one to end on.

We have about five minutes left here. And this question is from actually a Cochlear employee.

Hi, Shea. And in what ways have you seen Austin change since you've started Measure?

Our theme for the month is why we matter, which is a powerful statement.

What does that statement mean to you?

You know, I've seen a lot change in Austin since I've started Measure, and that's just within the last few years.

What I've mostly seen, though, is the community of Black and brown people that are organizing in ways that are so incredibly intentional to fight back against racism.

And so, and I think it takes time to get there, like to build the capacity for organizations like mine, organizations like the Black Leaders Collective, or organizations like GAVA, or organizations like, you know, just Community Advocacy and Healing Project to come together and to work so well.

And we really saw the evidence of that just within the last couple of days, the response to this incredible weather that we just experienced, okay?

We saw that when communities come together that are resilient and that are ready to mobilize, they are unstoppable and they can begin to heal themselves, right?

And we've also experienced where we can't always depend on the system, right, on the government to show up, right?

And so what ended up happening was that you had organizations like Community Resilience Trust mobilize very quickly.

And so that, I believe, is something that has changed from when we first began this work, when I started this work, you know, during the height of the Black Lives Matter movement, it wasn't really about healing ourselves, and it was a lot of trauma.

You know, matter of fact, I ended up having a heart attack, you know, during that time physically, just because there was so much trauma.

And we didn't think about taking care of our bodies and drinking water, as my friend Fatima would say.

And then now we're in this space where many, you know, a few years later, where we're centering our experiences in a way that's healthy, and we're working together and we're mobilizing together.

Now, at the same time, you still have to look at the data, right?

We still have to understand that we have vast disproportionality within Austin.

There are still people of color that are being pulled over disproportionately.

We're still criminalizing Black girls, you know, 10.6 times more often than white female students, you know, that are receiving a suspension at school.

We're still having these issues of, you know, where we can't get it right, right?

Where we have the haves and the have-nots.

So there's still those problems of data, that data shows us the disproportionality.

But it's really about the awakening of working together. Community has really developed, at least for me, within the last few years.

You know, go ahead, Nick.

Keep going. I just would add that one thing that I think has changed immensely since I joined with Mimi back in 2015 is the Austin Urban Technology Movement, which is another organization that's Black-led, getting more people in Austin of color involved in tech.

And so I've seen where there's more people that look like me now in spaces like this.

We have more conversations like this with more companies.

And that's been really heartwarming is that, you know, Cloudflare, as well as other companies, have welcomed this kind of a conversation.

And they're usually Austin-based companies or companies that have an Austin presence.

And it's just really good to see that.

And that's something that I honestly did not see at all back in 2015.

So that change has been huge. You know, it's funny that you say that.

I grew up here. And I don't remember there ever being a really cohesive, strong Black community here, kind of outside of the community that was in St.

John's, which right now has been more or less displaced, right, from gentrification.

But, like, the work that Michael Ward is doing at Autumn is amazing.

And there are so many great leaders in the Black community in Austin now that I don't remember ever having that experience here.

Because I, you know, I went to high school in Round Rock.

And I can remember being president of the engineering club.

And I would be it, you know. There were more than 600 people in my graduating class.

And I can remember looking across and not seeing really no more than maybe five or six Black people around.

So, to me, it's a profound difference from when I grew up here.

K-A-Z -I, none of those things really existed then.

So, it's a really profound difference. Now, I know I said I wasn't going to ask anything else.

But we have about two minutes left. And one of the things we didn't touch on is how measure has evolved over the last few years and some of the new work you're doing.

Can you talk a minute about the sexualization of young Black girls?

Because I love the work that you're doing there. And that's something that's really hard to get attention for or sympathy for, but which is a huge problem.

So, can you talk a minute about that? I definitely can. And I'll talk very quickly about this.

So, adultification bias. It's a social or cultural stereotype that's based on how adults perceive Black girls in the absence of the child's behavior or their verbalization or anything like that.

So, it's basically this looking at Black girls as less innocent or needing less comfort or needing less care or more culpable for their actions.

And what adultification bias does is that it then can lead to, you know, us thinking Black girls are more hypersexual or us thinking that they, you know, deserve harsher penalties.

Or we saw what happened with like Cyntoia Brown, you know.

So, they then become no longer the victims, right?

And so, what Measure has done is that we've launched this program called the Innocence Initiative.

And this was incredible. I mean, oh my gosh. It was supported by Impact Austin.

They really kind of helped us spearhead it in the very beginning.

St. David's Foundation came on and Greater Mount Zion came on. And now what we're doing is that we are creating new policy, right?

We're actively creating new policy with this legislative session.

We are working with Austin Independent School District to root out disproportionality and discipline actions.

We are, you know, we're even putting together really amazing events called like the BU Summit, which is going to be taking place here on March the 13th, where it's just about empowering Black girls.

And so, it's all about how do we disrupt adultification bias?

How do we do that meaningfully so that we can protect Black girls?

Yeah, awesome stuff. I can't thank you all enough for coming. This has been great.

Thank you.