AI as Augmentation, Not Automation: Brenda Wilkerson on Serving People Over Profit

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Always make the joke and it's not really fun, but I say to people who know me, if you hear that I did something, it probably wasn't me because 1 in 3 black women are not identified correctly where 99.97% of white men are. That means they did not test it's the data, right? It's always the data. It's always the data.

Step into the world of success with your brand Amplified, the ultimate podcast designed to unravel the intricacies of thriving businesses. I'm your host, Anica Jackson, and I'm on a mission to uncover the stories, strategies and secrets that have propelled entrepreneurs and business leaders to success. I can't tell you how thrilled I am to have a woman who is in charge of an organization that I admire so much. Brenda Darden Wilkerson, thank you so much for being on your brand Amplified today and bringing your wisdom and insights to us.

I am beyond excited to be here. Anika. Thank you for having me and I just can't wait for this conversation.

Same same we were talking about. We're both mothers, we both have podcasts and you've done so much more. Now. I lived in Chicago for a very small sliver in time, but you really that's one of the places where you really made your mark with an IT background in all kinds of fields. And you found the original computer Science for All program in the Chicago Public school schools which inspired Obama's administration to have a national initiative. This is something that I share with my students a lot. At usc we talk about what it means to have digital Equity. What is digital inequality? And it can mean so many different things, depending on age, demographic, where you live, resources. So can you please talk about that aha moment that inspired you to create this platform?

Yeah. So I think it was a series of things that happened in my life, I'd have to say, that finally led me to a place where I knew this was something I had to do. You know, my background, I'm the child of educators. And so there were certain things that you were going to do, right? You were going to be going to college. My parents were very educated and they picked out, there were four of us, and they picked out what each of us would do. Doctor, lawyer, engineer. And then there was like this extra child. They couldn't really figure out what she was going to do. My younger sister, but I was supposed to be the doctor. So they got, they got their lawyer and they got an engineer or they got an accountant. Actually, my brother was an accountant before he actually got into tech. But I was designated to be the doctor. So. And I think where that would have fit my personality is that I am definitely someone is a problem solver. And it's got to be about people. You know, I'm not about a tinkering, even though I know how to tinker, but at the end of the day, it's supposed to be about, you know, how can this make life better for someone or some group of people. And so I didn't do that. Obviously I'm not a doctor. We can talk another day about why I changed my mind because I had definitely, I had gotten really far into to pre med before. I was like, yeah, nah, I'm not doing that. And so then I'm looking around going, what in the world should I do? And because my university didn't have a pre med major, I had to figure out what degree would give me those requirements. And so I started with biomedical engineering, which would have, you know, also kind of covered that other problem my mother wanted solved. And you know the interesting thing about that, we had to all take two programming classes. And I never had programming. And so I was in this interesting space because I had been the top of my class all the way through school. That's just my parents, like, you, you will or you won't live here, right? Kind of thing. And all of a sudden I'm in a place where, I don't know, something really critical. And everybody else did. And for the most part, they were all boys, right? And you know, the teachers were male. It was just male, male, male. And the Interesting thing about that dynamic is who gets the concern and care when you don't understand who gets the, oh, it's okay, we'll bring you along. And it normally isn't the one that's left out, right? And so I was all of a sudden in this place where I just didn't know anything, but obviously I learned it. And when I decided I don't want to be a doctor anymore, I was like, what could I do? I mean, I wasted so much money. I was a math major. Oh, no, no, chemistry. You know, I did all these different things. And finally I said I was kind of good at that computer science stuff. And that's how I ended up there. So after going through, you know, a career of the same sort of like. Cause I was like, I knew who I wanted to be and I knew what I wanted to do. And there were all these twists and turns. When I finally got into education, which is a whole nother story, and I found out that at that time, the bls, I forget, that's the government listing that I think is no more, but basically talks about all of the jobs and the, you know, the likelihood that those jobs will continue. Had put programming on the down. It was going down. And I was like, well, that's dumb, you know, and that's not true. But it wasn't called programming anymore, so they didn't know. Right. And so as I got into education, my bosses were trying to. They basically, it looked like, anyway, brought me in to close down what they thought was it. They didn't have no computer science in Chicago public schools at the time, but they wanted to kind of close down it. And I thought, how could you be closing this down? And what I ran into was basically the same thing that I had had when I was growing up. You know, I lived in all black neighborhood. And I always like to say we were all black, we were not all poor. There's always this assumption that we were poor people. We were not. But policy, as policy is now there's decisions that get made about who gets what programs. We didn't have computer science. So I'm acing all of my literature, I'm acing all of my sciences, I'm acing everything. But there was no computer science to ace, right? And so now I show up at the third largest district in the country and there's no computer science. And they're trying to shut down. What they said was it, which in the marginalized neighborhoods was actually something like, you know, Microsoft Word, which was another heinous Heinous. Right, heinous. And, you know, so all of a sudden, there I am like, we can't be close. It's not going down. It's almost like what's happening now. Right, right. So at that point, problem solver people are not doing well all the way around the ecosystem. So here are these students who are not getting opportunities. Here's the ecosystem that is not getting the wealth of innovation and knowledge that they would get if they had a broader set of the population at the helm, even at the table. And I went, oh, no, how can this still be happening? This is what happened to me. Right. And so that was the impetus for trying to go in and do something about it.

Amazing. And obviously you did something about it, so let's not gloss over that. What was it like convincing people that this is something that was so important and vital and that it wasn't just one program. Right. It wasn't just Microsoft Word, that it wasn't just it. That it was a whole other area that would actually bring new horizons to the student population.

Yeah. It was hard, in a word, because all of the biases that still surround a lot of tech came into play. They looked at me like, well, what do you know about that? Like, I'm a computer scientist, you are a computer scientist. All of that. Right. And then externally and internally, there was this issue with when you have a large urban district, people start to make assumptions about what the kids are like and what the kids can't do. And I always have to share with people. It was not the kids. It was the grownups. It was the grownups who were like, they can't do that. And really what they were saying is, we can't do that. Right. And so there were all these barriers that they would put up, but, you know, all you need is a couple of pilgrims who are willing to try, and then you just roll.

Amazing. And that's. Well, obviously, we'll talk about your podcast, too. But it reminds me of Nicole Hill, if. I don't know if you've heard her podcast. Our ancestors were messy. And so she. Yes.

Yeah.

So she goes back and she's like, people think that, yes, there were really obviously big, bad social issues, but within community, there was community and there were gossip columns. There was this and there was that. And so many times people just group a population together and say, you're monolithic.

Right, Right. Which is the most outrageous thing. And past what it robs of that group of people, it robs the rest of society in general, in totality. Of what those people could bring forth. Right. I believe that one of the purposes of society is to build the citizenry that will help that society continue. And a lot of that should happen. It happens at home. It happens in our places of worship. It also should happen in education. And so when you immediately put a label on someone that says what they can't do, whether they're. Whether it's racial, ethnic, gender, physical ability, background, geography, I mean, there's so much. Right. And, you know, I was just talking to one of my daughters and I said, you know, I just would be amazed. I'd like to see a world where we just dialed toward true equity, even of thought, even 10 degrees. Can you imagine? You know, it was. I'm going to give a really silly example. One of my favorite old movies is Back to the Future. And I don't know if you remember if you saw that movie, but when Marty was in the past, he made one little. He made something different and it totally changed, right? Yes.

My daughter and I just watched rewatched all of those movies.

Love it. I love it. I mean. Cause I love Doc. I mean, I kind of feel like he'd be my uncle or something. But yeah, it's. We, I think, many times give up on what we think are big challenges. And we start to just say, oh, this is just the way it is. This is just the way it has to be. Because we don't realize that just little changes get you started. And once you start, you get a momentum that until you get to the place where there's backlash, normally can't be stopped.

I'm drawing so many parallels between my students at USC right now. Many of them because I teach grad school. And I was really surprised because, I mean, I didn't grow up in Los Angeles. But being here long enough to know that, and I'm sure a lot of other people. University of spoiled children. Right? And it was completely different experience. Most of my students, it's majority women, mostly minority students. Many times this was a school they never thought they could get into. And part of my role as an educator is to say, you are here for a purpose. And guess what? We're going to talk. We're going to get into the deep issues on digital injustices, equity, algorithmic bias, all these things. Because part of our job, and meaning me and the students, is we have to be the change. We have to be the ones asking the hard questions and making sure our data is good, making sure that systems are being built with us at the beginning of the process, not as the token person coming at the end and rubber stamping something. And that takes me, I think, really well into asking you about your work at Anita B. Because that is a huge part of what you're doing from the school systems into leading an organization of over 400,000.

Yes, yes. We should always be. One of the things that my parents raised me to believe and to be was someone who always left a situation that I was brought into, better if I could. And so that's what we're trying to do. That's what Anita meant to do. I wish I had known her. When I get to watch the few videos of her, I see her fire. I think we had the same fire. And it's about what's right. This is just right. It's just right to have something that impacts everyone's life in just about every way possible, to have someone representative of each of us at the table to consider how the tech that impacts every part of their life might impact them. You and I, I know you're brilliant. I think I know a thing or two. I don't know everything. I can't be the one sitting at the table making the decisions about how something that even I dream up would impact someone else. And that was one of the things that we did in my computer science journey. It's something that I've always done. It's something we do at Anita B is we, you know, we're Silicon Valley based or we started out being an organization. I'm from the Midwest and I had to learn that, that whole ethos. But what I didn't want to do was to just export that everywhere. I didn't want to assume that that groupthink that was there fit in New York or fit in Texas or fit in Maine or places that at the time when I first started, people were not thinking, oh, that's not really where tech is done. Well, tech is done everywhere. Tech is everywhere. And we're international too. So when we, you know, we have an organization in India, but we have, we have communities around the world, 50 different communities. And so when I go there, you know, I'm like an anthropologist. It's like, okay, what is going on here? What is it that you need? What are you thinking about? You know, like, you want to ask questions. It's a curiosity that I believe that many times we miss in tech because we've been told that we're the titans, we know everything, right? And that has gone to our heads. And we don't take the time to understand every Single day that tech is the most important purpose of tech is in service of people, period. If it's not serving people, it's bad tech. It's just this. And so we try to do that. We try to. And it's harder, right, because companies try to scale, right? They like find a model and they just kind of want to scale. That means you got one idea and you want it to work for everybody. It just doesn't. Right. So you mentioned earlier your place, the experience that you have even in life. Anywhere is different based on your age, you know, your background. All of those intersections are impactful. And so we take that into consideration when we work with not just the women themselves, but also with the places where they learn, where they work, where they get funded. And it's like a whole. You're in there cooking up something different because one size does not fit all. Exactly.

I was on the Intuit Small Business Council for several years and of course we saw during the pandemic, we had thought small businesses would have seven more years to get online and really get that straight. And instead we saw a lot of businesses close brick and mortars that didn't have a good website. Maybe they just had a quick page up, right. But nobody could order. Like businesses had to learn really quickly. And those that didn't no longer exist. Then the next evolution, generative AI comes to the forefront. People don't know AI has been around for a long time. We already use recommendation engines for machine learning, all these other things, right. But then we saw, wow, now this is a great way to kind of create more equity. But small businesses have to be trained on how to use these tools to streamline processes so that they have more profitability, so that they can hire more people, so they can stay in business, so that they aren't one of those, you know, the majority of small businesses that fail in the first few years. So I think about those parallels and how there's people doing different parts of the work, but specifically women in tech is an area gender equity, right. Thinking about how we look at our lenses and we know we're moms, women, we think about things differently, but we also, we're trying to be inclusive, put the best people in the best positions, meet people where they're at. We operate in different ways, but to me that does equate more success. So can you talk about the business and innovation imperative, not just the fairness issue and why it's so important that the work that you're doing at need to be your partners, you know, continues.

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Well first I want to say unfortunately that business imperative just gets ignored. It's like it's facts, it's data, it's long running data. It's like somebody did one study. No, no, no no. It's fact that the more diverse your teams and all the way into the C suite, it impacts your bottom line positively. If we talk about founders and we saw this in education, I'll give you an example in education. But founding companies that are founded by women many times get to profitability faster or they are become profitable over those that are founded by men. These are just facts. But at the end of the day it's about power. And so we get stuck in this conundrum because the facts are there. And so that is why it is so incredibly important in our work. You know, we start One of the things I like to say is a lot of this stuff starts in education. I'd love your opinion right there. When we work with companies and we try to work with them around equity, around equal pay, all of the things that we've been fighting for decades, decades and decades, and we do that. But a lot of those habits start in education. As I said, I was the woman in computer science. My counselors tried to talk me out of it the whole time I was there. I mean, I was a STEM person. It was like, why do you want me in these other things when I have a mind for these things? And so those habits, we try to go to the root, and so we go and try to address the systems. This is systems problems. This is systemic. It's been for a long time. And until we make a decision that we care enough and it doesn't totally impact our profitability, we don't have enough reason right now. It seems to change. Right. It's like the companies. Yeah. Profitable enough. Right. So it doesn't bother them that they could be a little more profitable if they did that, because that goes against what they really want to do anyway. It feels like it's giving up power. But, you know, I like what you said earlier about some of the ways that we have been conditioned as women or the roles that we've had as women really does set us up to be more successful in some of these roles, because we do think about everybody. One of the things that I worry about with agentic AI, it feels very male. It feels very. I can do it all by myself. I don't. I don't need a community. I'm going to win. Right. Versus women. We take care of the family, we take care of the neighborhood, we take care of people, systems, et cetera. We are understanding more. The long game. The long game says, what happens when you put a large percentage of your population out of work? When we find purpose in work, what happens when we don't. A large percentage of people can't find purpose? What happens to society then? And those are the types of things that we think about. I'm not against AI. I'd love AI to be an augmenter, not a replacement 100%. And so I feel like that some of the work we're doing is in this disruption. It's huge disruption. Right. Is to help companies think about how to augment their workforce. Not that you won't lose some people, but how do you augment. How do you give people a higher purpose when they don't have to do all of this stuff that you could automate, that it makes sense to automate. And so we like to be thought partners. Yeah.

Well. And that's how I talk to my students about AI. I mean, I forced them to use AI tools for assignments now, because look, you need to understand how this can help you as a thought partner, not as a replacement to you. It doesn't replace our need for human connection or human creativity strategy. And I think about so many different examples. One, I was in Vietnam recently and we met with different businesses. There was a company that did a lot of work in health, tech and biotech. They took all 4,000 software engineers at their organization and over two years trained them in different AI modalities. They didn't create the certifications, they used Google certificate. You know, they used all the certifications that are already out there. But they said we're going to train our workforce for the next phase instead of saying, oh, you guys are obsolete now. So they're preparing people. And I just thought, why doesn't every company in the United States do that? Right? And then I also know of examples of companies that offer near shoring employees with AI. But their case studies all point to, hey, this dental office could afford to have 60 of our people working for them because they were a fraction of the cost, blah blah, blah. But we grew them from three offices to nine offices and they staffed 150 more people on their side. And it was using AI systems as part of the process. But that shows AI doesn't have to be replacement. Actually, it can help you grow the number of people working in your organizations if you use it the right way. And that I think is one of the big issues is people are just going by the news cycle, going by, oh, we have to implement this even though we have no idea how to use it. Guess what, chatgpt, having it write stuff for you is the worst case. That's not going to help propel your business.

No. And it's not. It's one of the things that I fear because one of the things that, that I got growing up is you will know how to think, you will know how to think, you will know how to discern. And I think these are just life skills, let alone what I do at work. Right? And so if you're taught to just take what is handed to you by this thing that is based upon data that is existing, where does the creativity of the human creativity come? And there's so many other things that I could say about that, but I feel like we have an opportunity. That's one of the things we try to do, is to find that space where we can encourage people to use it in a way that does. Like the example you gave. But there's another thing That I think would be amazing. You know, for a while we were talking about work life balance. Right. Could we not create amazing work life balance if we learn to use those tools as augmenters and not as a replacement? I was at an event last week and I had a. There was a gentleman there who was starting to me that, oh, yes, I'm not hiring any new graduates. I mean, they don't know anything. The AI can do everything that they do better. And I spent 20 minutes last week putting a proposal together that got me $2 million. I, I don't need any other people. And I just said, okay, so what happens when you're not there anymore? What happens? Yes. I mean, you're not thinking, you're not thinking ahead. Who buys your products if no one's working? I mean, these are just simple questions that are economic, but they're also people oriented. It's like what happens to folks who all of a sudden are outside of a system? And there's been no transition. We've had disruptions in the past. Right. But there was a time where the new. There were still people who were doing new things and others who could watch them and grow into it and perfect it a little before it became the thing. This is different.

Yeah. I mean, and that thinking about the trades, apprenticeship, you can make good money as an apprentice. And you know what? You're going to have a job that's not going to be replaced by AI, at least not anytime in the next few decades. And you're going to make six figures immediately. And we need to be training our workforce the same way from the beginning, from not even grad school, but from those really early days, exactly where we learn.

You know, because I was learned, I was taught, keep your eyes on your own paper, that mentality. And then you go to work and it's all about collaboration. Right. And so there's work to be done. If we thought that was good work. I think there's a lot of times it's like, especially now, there's like this sort of like pressure on education to not be as important as it is when we could be really putting time and effort into taking it to the next level. I'm glad to hear that you are having your students do that work while you can help them think about how to use those tools.

And I can see the aha moment and I can see the shift and the just I can see the growth that they are now confident where they might have walked into my first class, being scared, not wanting to use AI or They were told in undergrad they couldn't use it to now going, I know how to speak about this technology. I know about where data is labeled and how that's not equitable to people, the harm it can cause people. And that I know about. You know that there are over 100 kinds of cognitive biases and that those all feed into the algorithms. I know how to make systems change, and I also know how to use these tools and talk about them confidently when I walk into a job interview or when I decide to start my company. This is my high horse. I could talk about this.

No, I love it. I love it. It's what education is supposed to do for people.

Yes, that critical thinking component. But I do want to talk about some of the work. You're on the board of the Algorithmic Justice League. Amazing. I love that name. And you also had inaugural Responsible AI Forum. You're working on how to advocate for legislation and how to help your members think about that. So I'd love for you to talk about this work because this is. It's so critical. We see different. Of course, we're in the United States. So while EU might have a really great AI policy, other countries are forming them here. Some states have them, some states don't. Depending on who's in office, we get mixed messages about what's going to happen at the federal level, which can all cause more harm. So we need to be the advocates that we seek. So can you talk about some of the work you've done there?

Yeah. Joy Buolamwini, the powerhouse that she is, is the president, founder, CEO of the Algorithmic Justice League. If you don't know her, you should know her. I'm saying that to all your listeners, she is an amazing advocate. And her life is such an amazing picture of what equity should look like. So her dad is a scientist and her mother is an artist. And so she's been this combination of the two that, you know, she's a poet, she's an artist, she's A computer scientist. PhD computer scientist and all of that. So all of that, really, the people side, the beauty that can come from a system that's not cold and just efficient. She gets that because. And it was sort of like what I did with my undergraduate degree. I wanted. I'm a computer scientist, but I wanted to read Shakespeare. You know, I wanted that as part of my education. I was like, why do I do something? Why do you want me to be in charge of creating systems for people? And I'm not studying people at all. And so she's a picture of that. Right? She's both sides and her life, just who she is, sort of got her into what she's doing. So she's a dark side skinned woman. And when she was at MIT doing her master's, I think they had a project that they had to do around computer vision and. Right, you know where I'm going with this. And so she's working with her lab partner who I think was Corrine. It could see her face, but when Joy would put her face into the camera, nothing. And I don't know what made her think of this, you know, again, being an artist, somehow she had one of those faces like, you know, you would have in the theater in her office. She picks it up and puts it across her face that it read. Now if she hadn't been dark skinned, maybe she would never have gone down that pathway. Because she even says, I didn't pick this, it picked me. But look how important it is to have people of all colors, stripes, sizes, whatever, to be part of something, to be able to call out what no one else noticed. And she also noticed that all of the major AI systems were using the same software. So it wasn't like you could go from Microsoft to Amazon to others and get a different version of it. In fact, they were using the same base code. So they're like, this is good enough, just scary enough. And then they just replicated it. It's like they all went, you know, the same GitHub code and got it and said this was good. And so part of what she's done is been an advocate for all of the biases that have negatively impacted people, people getting arrested, who their faces weren't recognized. I always make the joke and it's not really funny, but I say to people who know me, if you hear that I did something, it probably wasn't me. Because 1 in 3 black women are not identified correctly, where 99.97% of white men are. That means they did not test. It's the data, right? It's always the data. It's always the data. I remember years ago when self driving cars were really becoming more like. In the conversation there was, I think it was a ProPublica article and they talked about how those cars couldn't see black people. And the author of the article said, that's probably not a good thing. And I was like, is that tongue in cheek? You can't see people in the algorithms, but the algorithms work on the data. And behind all of this have to be people. We cannot just advocate our responsibility to say what if? Where does this work? Where does this not work? What are the potential harms? And that's what Joy does very well. She has testified before Congress, she's helped people think about different legislation or policies. And in my mind, in the tech world, we can't have one without the other. We have to have the wonderful tech. I'm a nerd, I'm a geek like anybody else. I miss, I don't get to do it as much. Darn it. But now what we've dedicated our lives more to is really trying to help people think about the policies and shine a light on the potential harms so we don't have them.

Yeah, it's just incredible. I mean the example you gave is so huge. I think about other examples like even running your hand under the water faucet.

Right.

I mean I'm fairly light skinned Asian, but I even have issues with that sometimes.

Right.

Think about like backdrops for photos. There's so many, so many examples. And what's astounding to me as we're having this conversation is we think about things in a very US centric way. But if you look at the rest of the world, there are far more of people who look like us.

Absolutely. We are the global majority. So many times we even refer to ourselves as minorities. But I've learned to say as part of the global majority because it's true. Right. And as long as you, when you minoritize a group of people, no matter what, like I just, I don't know if you know Haben Girma, she is a blind and deaf lawyer who wanted to be a computer scientist but they were like, they wouldn't let her in school for that. So she's amazing. You should look her up and listen. I was listening to something that she was talking about, but because she's disabled, there were so many things people just would not do and did not find to be important. So I think the larger conversation when you start to talk about algorithms and you talk about AI is we have to watch what's centered, what's normal. Normal is almost a destructive word because what is normal? Who is normal? Unless we actually learn to care about everybody? If you put people in pockets. Like years ago I was on a project where we did a project on, we called small data. People always say, well, this is statistically insignificant information generally referring to people.

Are you looking at an overall cluster? Are you looking at. Yeah, we can get really let's, we can go deep into this. Like, are you looking at the data?

Yeah. Are you looking at, you know, in one instance, one example was on a particular campus, smoking. Normally smoking is not allowed. This is like don't smoke. But there was a indigenous tribe that that was part of their religious routine to be able to smoke. And so understanding that, but there were statistically insignificant. And so it was just like we're not going to deal with this. Even though the data showed that this was really important for this group of people, there weren't enough of them for there to be effort to address it. When very simply they could have given them a space and said here's where you can go and do this right. But because it was small, it was just dismissed. And so that to me is a different way of thinking. We could change that way of thinking. When we see everyone as significant, then we will rethink our processes around that. But that's the bigger issue. I think sometimes we try to have a math problem solve what really is a moral problem, which is I don't see certain people or if that's the thing or I don't see those issues as important. If we could dial that up a bit, the technology, the innovation on top of that would just explode and we would see problems that we're still grappling with, I believe solved in our lifetime.

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Wow, you have some big goals, big charges. I mean I'm on the same page as you thought. So a week collectively, right. You sit on multiple boards which is fantastic and helps you learn other people's perspectives, share your perspective, and advocate in different ways. Anita B reaches over 400,000 people, so. And you also, of course, have a podcast, Be the Way Forward, where you explore a lot of the issues we're tackling right now. So I'd love to hear a little bit about a conversation you've had on Be the Way Forward that you think really changed how you think about technology, justice, or how you helped change minds of others through that conversation. And also, second, separate, I have a stack of questions. I'm sorry, me too. For the members of Anita B. How have they been able to mobilize around responsible AI?

Okay, so I get to talk to so many. Amazing. That's why I love to do a podcast. And one woman who just always stands out in my mind is Dr. Brittany Barretto. And she is a geneticist by train. And she actually created an app that dealt with genetics in terms of relationships and that she stopped doing that. But having done that, when you're in a startup community, you start to understand more about, okay, what are startups do? What are some of the challenges that people have? And because she's medical, she started to pay more attention to those people who were trying to solve medical problems. And what it ended up getting her into was the fact that there are so many common medical issues that women have that are just not addressed at all. Like there is a. I think it's called endometriosis. Do you know that there's absolute, there's no diagnosis for it, there's no treatment for it. So people will suffer 10, 15 years before someone finally says, I think that's endometriosis. And she, she helped me understand how the government did. I don't know if you knew this. The government gets involved in diagnoses. Like if you come up with a diagnosis, you have to go to Congress to get them to actually do something around actually making that thing something that's named so that it can go into the medical cannon. So ultimately it's money. It's about money. The insurance agency can get reimbursed for it. Will no one bother to do that within demetriosis. And so now what she does is she helps women who are trying to solve women medical issues get funded. And just amazing. Did you knows that? I didn't know that. Oh my God. It's.

Yeah, no, I didn't know that. And I mean, that brings up so many other issues about breast cancer research.

Yes, yes, yes.

There's a vaccination for certain types of breast Cancer. But again, it doesn't make money for the pharma, you know, for the big pharma. So it doesn't get. And the biases we think about, like, healthcare system was built on certain segments of the population.

Yes. And those things are still.

We could go for the whole thing.

We could go the whole thing. One other thing I want to share that I thought it just. Just blew my mind. So I don't know if people knew this, but the health drug Viagra was actually being tested as a heart drug.

Oh.

So they were going through all these tests around, you know, how does it work for heart? And the other thing people may not be aware is most drugs are not tested on women. They're only tested on a very narrow segment of the male population. But there was this other thing that happened when these men were taking this drug, and all of a sudden they, like, scrapped the whole heart medicine thing and it became Viagra. The interesting thing was there were just a. There were a few women somehow that snuck into the study. And what they found is when they took this drug, they had absolutely no cramps whatsoever. Shut them down cold.

Oh, no. I'm like, I need that cold.

But here's the interesting thing. They've tried 12 times to get the money to have it studied for that purpose. They're like, you have Midol, you have whatever, you're fine. Take it. Take an aspirin. Right. So she brings all these types of things to light and helps to support women who are doing amazing, amazing, amazing things. She was at our conference recently and spoke with the panel of three other women who were doing the same sort of marvelous stuff. And so, I mean, that's just another reason why here people who are like, well, I'll solve that problem. You don't want to solve the problem. I will. And that's why we have to have. We have to have women at the table. Yeah.

I'm gonna have to go listen to that episode now. I'm just. I'm just speechless.

Yeah. And I tell you the other thing. She shared this book that everybody should read called Invisible Women, and it shows where we're not in the data. The author is Carolyn Criado Perez, and marvelous book. Although when you get to the medical part, you will hold your head, but because it's.

I mean, and women are still invisible in so many parts, and different populations of women are very invisible.

That's right. Yeah.

Again, we could. That's. This is a whole other conversation. So we can go. I think we have about 10 podcasts in this one episode.

Well, we'll just have to come back and forth between each other's podcast.

Yes, please. I do want to ask about, for Anita B. Who is an ideal member, how can people support if they are not women in tech? And what are some of the biggest things that you see the members doing right now to advocate for change and to help be part of the change?

You know, that's an interesting question because when I first started and I've been around for about eight and a half years now, there was always this question of who is a tech woman, right? Who really is a tech woman? I think AI is starting to introduce more of the population. And if AI is the thing that we're going to use and ultimately going to be mostly users, then that really blows it up a bunch. But the ideal person is someone who is either in tech or wants to get into tech. Brenda, that's a big population. It is. It was one of my goals to broaden the tent to people who would seek to come in. I've even shared with companies that shopping around in their own population of other folks who are not technical to find technical people. They've been with your company, they're loyal, they know your business, they can learn the tech. There was this time when, when I, you know, I could say in my day, you know, when, you know, computer science was really on the upswing, a lot of people didn't have the degrees and the men could learn it. They thought, you have the bent for it, you can learn it, right? All of a sudden, when other populations started to enter, then it was more like, well, you got to have all this. And I'm not, I mean, I'm totally for education. I'm not, you know, but, but can you learn? And you have to, because it's changing all the time, right? So our population are people who, you know, they might be people who are self taught all the way up to PhDs, multiple PhDs. One of the things that we do, and I want to share is we put front and center women who are doing or have done amazing things. Because generally there was a study, there was a study done three or four years ago. A survey went out and they asked various questions about people in tech. And one of the questions, of course they asked were, who? Name three men, powerful men in tech, or computer scientists or something like that. Of course, they immediately named the usual suspects. They could name them right off the bat. Then they asked, name three women, tech women, computer science women. Do you know what they said? First of all, only some small percent of the population, like 10%, thought that they could answer, and then only 4% did answer. And here were their answers. Sheryl Sandberg. She's not a tech woman. She worked at a tech company. She's not a tech woman. Siri and Alexa. And generally, that's the problem. The contributions are not attributed to women. Haley Lamar. We can give her the fact that we have Bluetooth, right? And that could go on and on and on. So one of the things is we put on our stage women who have done amazing things, and that's not just so that they can get the accolades, which they do. They deserve them. But it's for other women to say, oh, I could do that too. You know, I may have been talked out of it. I've been told I can't. You can't do that, honey. I can't tell you how many times I've heard that. But when you see. And that's part of what we do at the Grace Hopper Celebration and in our membership is we bring women together to have conversations around the contributions that we are making that many times are not considered to be contributions. Or like, for instance, many times we are not given the same titles or promotions as others. And so we think that our work is insignificant and we don't realize just how integral the work is that we're doing. And I'm hoping, and this has been our record, is that women get inspired to not only come in, but stay. There's a stat that's a really sad stat that's longstanding. Most women in the US leave tech at around 35, so they get just. That's 15, 12, 15 years. They're just about ready to really launch into management. But they hit that ceiling and they go, I could go do something else. I did it. And so think of the brain drain. Think of the amazing leadership opportunities that we miss out on because they do that. And so our membership, our all of our ecosystem is around bringing people together to understand their power and how necessary they are to our way of life. Because tech is necessary to our way of life. Yeah, Mic drop right there.

Brinda, this has been such a pleasure. We have many more conversations in store. I am assured of that now that we finally met. And I'm so grateful that you wanted to give your time to us today. Of course, we'll have all the links in the show notes, and I just want to ask you if you have a favorite quote, mantra, verse, poem, family motto, words that continue to inspire you to do the work that you're doing for everybody else.

Oh, there's so much but one I always think about, especially as you're trying to address bias and fix systems by James Baldwin. Not everything that is faced can be changed, but nothing can be changed until it is faced.

Amazing. Thank you so much.

Thank you. And thank you for what you do. It is amazing. I love your podcast. Well, thank you, thank you.

I appreciate that so much. It's always nice to hear from women who inspire well, from women and people who inspire me to keep doing this.

So absolutely.

Please thank you to everybody who's listening to this episode, watching it on your favorite platform. Check out the links in the show notes. You'll find some goodies. And this is work we all need to do so you know, figure out what your small role can be that one step that you can take.

Absolutely.

Thanks for listening or watching to this episode of youf Brand Amplified. Don't forget to leave us a rating or review on your favorite podcast listening platform. And if you want to learn more, check us out@yourbrandamplified.com.