 Hey everyone, welcome to theCUBE's coverage of Women in Data Science 2022. I'm Lisa Martin, and I'm here with one of the key featured keynotes for this year's WID event, Cecilia Aragon, the professor, the Department of Human-Centered Design and Engineering at the University of Washington. Cecilia, it's a pleasure to have you on theCUBE. Thank you so much, Lisa. It's a pleasure to be here as well. You have an amazing background that I want to share with the audience. You are a professor, you are a data scientist, an aerobatic pilot, and an author with expertise in human-centered data science, visual analytics, aviation safety, and analysis of extremely large and complex datasets. That's quite the background. Well, thank you so much. It's all very interesting and fun. So let's talk about that journey. I'd love to understand if you were always interested in STEM, if it was something that you got into later. I know that you are the co-founder of Latinas in Computing, a passionate advocate for girls and women in STEM. Were you always interested in STEM or was it something that you got into in a kind of a nonlinear path? I was always interested in it. When I was a young girl, I grew up in a small Midwestern town, and my parents are both immigrants. And I was one of the few Latinas in a mostly white community. I loved math, but I also wanted to be an astronaut. I remember when we were asked, I think it was in second grade, what would you like to be when you grow up? I said, oh, I want to be an astronaut. And my teacher said, oh, you can't do that. You're a girl. Pick something else. So I picked math and she was like, okay. I loved it because one of the great advantages of math is that it's kind of like a magic trick for young people, especially if you're a girl or if you are from an underrepresented group. Because if you get the answers right on a math test, no one can mark you wrong. It doesn't matter what the color of your skin is or what your gender is. Math is powerful that way. And I will say there is nothing like standing in front of a room of people who think little of you and you silence them with your love of numbers. I love that. I never thought about math as power before, but it clearly is. But also, you know, and I wish we had more time because I would love to get to how you overcame that feeling. You write books about that, but being told you can't be an astronaut, you're a girl, and maybe laughing at you because you liked math. How did you overcome that? And so never mind. I'm doing it anyway. Yeah. Well, that's a it's a okay. The short answer is I had incredible imposter syndrome. I didn't believe that I was smart enough to get a PhD in math and computer science. But what enabled me to do that was becoming a pilot. And I learned how to fly small airplanes. I learned how to fly them upside down and pointing straight at the ground. And I know this might sound kind of extreme. So this is not what I recommend to everybody. But if you are brought up in a way where everybody thinks little of you, one of the best things you can possibly do is take on a challenge that's scary. I was afraid of everything. But by learning to fly and especially learning to fly loops and rolls, it gave me confidence to do everything else because I thought I appointed the airplane at the ground at 250 miles an hour and waited. Why am I afraid to get a PhD in computer science? Wow, how empowering is that? Yes, it really was. And we're going to be talking about some of the books that you've written. But I want to get into data science and AI and get your thoughts on this. Why is it necessary to think about human issues in data science and AI? What are your thoughts there? As a developer of algorithms that work over very large data sets, I've always found it really important to consider the humans on the other end of the algorithm. And that's why I believe that all data science is truly human centered or should be human centered. Should be human centered and also involves both technical issues as well as social issues. Absolutely correct. So one example is that many of us who started working in data science, including I have to admit me when I started out, assume that data is unbiased. It's scrubbed of human influence. It is pure in some ways. However, that's really not true. As I started working with data sets, and this is generally known in the field, that data sets are touched by humans everywhere. As a matter of fact, in the recent book that we're coming out with human centered data science, we talk about five important points where humans touch data, no matter how scrubbed of human influence it's supposed to be. What's led to the separation between data science and humans? Well, I think a lot of it simply has to do with incorrect mental models. So many of us grew up thinking that, oh, humans have biases, but computers don't. And so if we just take decision making out of people's hands and put it into the hands of an algorithm, we will be having less biased results. However, recent work in the field of data science and artificial intelligence has shown that that's simply not true. That algorithms reinforce human biases. They amplify them. So algorithmic biases can be much worse than human biases and can have greater impacts. So the book human centered data science, you can see it there over Cecilia's right shoulder. When does this come out and how can folks get a copy of it? So it came out March 1st and it's available in bookstores everywhere. It was published by MIT Press and you can go online or you can go to your local independent bookstore or you can order it from your university bookstore as well. Excellent. Last question I mentioned, you are keynoting at this year's WIDS conference. Talk to me about like the top three takeaways that the audience is going to get from your keynote. So I'm very excited to have been invited to WIDS this year, which of course is a wonderful conference to support women in data science and I've been a big fan of the conference since it was first developed here at Stanford. The three top takeaways I would say is to really consider the data science can be rigorous and mathematical and human centered and ethical. It's not a trade-off. It's both at the same time and that's really the number one that I'm hoping the keynote will bring to the entire audience. And secondly, I hope that it will encourage women or people who've been told that maybe you're not a science person or this isn't for you or you're not good at math. I hope it will encourage them to disbelieve those views. I love that the scaffolding. I think the one of the high-level takeaways that we're all going to get from your keynote is inspiration. Thank you so much for sharing your path to STEM, how you got here, why humans data science and AI are have to be foundationally human centered. Looking forward to the keynote and again Cecilia Aragon, thank you so much for spending time with me today. Thank you so much, Lisa. It's been a pleasure. Likewise, for Cecilia Aragon, I'm Lisa Martin. You're watching the CUBE's coverage of Women in Data Science 2022.