 from Stanford University in Palo Alto, California. It's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. Welcome back to theCUBE. We are live at Stanford University for the third annual Women in Data Science WIDS Conference. I'm Lisa Martin and we've had a great morning so far talking with a lot of the speakers and participants at this event here at Stanford, which of course is going on globally as well. Very excited to be joined by one of the keynotes this morning at WIDS, Latanya Sweeney, the Professor of Government and Technology from Harvard. Latanya, thank you so much for stopping by theCUBE. Well, thank you for having me. Absolutely. So you are a computer scientist by training. Yes. WIDS, as I mentioned, in its third year, they're expecting 100,000 people to engage. There's 177, I think, Margo said, regional WIDS events going on right now in 53 countries. Isn't that amazing? It's so exciting. It is incredible in such a short period of time. What is it about WIDS that was an attraction? Do you say, yes, I want to participate in this event? Well, one of the issues is just simply the idea that data science represents this sort of wave of change of how do I analyze data? How do I make a difference? And the conference itself celebrating the fact that women are taking the step is hugely important. I mean, when I was a graduate student at MIT, I was the first black woman to get a PhD in computer science from MIT. So, and when I, and, you know, sort of no women, you really just didn't see women in this area at all. So when I come to a conference like WIDS, it's huge. It's just huge to see all these walls broken down. I love that. Walls breaking down, barriers kind of evaporating. In your time at MIT, I'd love to understand a little bit more. Were you very conscious? Hey, I'm one of the very few females here. Did it bother you or were you just, you know what? This is my passion. I don't care. I'm going to keep going forward. What was that experience like? Well, at first I was very naive and a belief that, you know, all that really mattered was the work I did. And I never had problems with the students, but I did have lots of problems with the professors with this idea that you had to be like them in ways that was beyond your brain or your work in order to really be exalted by them. And so whether I wanted to admit it or whether I just wanted to ignore it, it just sort of came crashing down. Did you have mentors at that time or did you think, you know what? I'm not funding anybody that I can really follow. I've got to be my own mentor right now. Right. I mean, I don't think my experience is really that uncommon for women in my generation. Very difficult to find mentors who would be complete mentors, complete, see themselves in you and really try to exalt you and navigate you. What women often have found is that they can find a partial person here and a partial person there, one who can help them in this regard or that regard, but not the same kind of idea that you would be the superstar of one of these mentors. That's not to take away from the fact that there have been these angels in my life who made a big difference. And so I don't want to take away from that that somehow I did this all by myself. That's not true. With the conference today, one of the things that Maria Clave said and her welcome remarks was encouraging this generation, don't be worried if there's something that you're not good at. I loved how she was sort of encouraging people to sort of women sort of let go of maybe some of those preconceived notions of, I can't do this, I'm not good at that. I think it's very liberating and still in 2018, with the fact that there is such a diversity gap, it's still so needed. What were maybe some of the three takeaways, if you will, of your keynote this morning that you imparted on the audience? Was that technology design is the new policymaker that they're making policy. The design itself is making policy, but nobody's like monitoring it. But we could in fact use data science to monitor to show the unforeseen consequences. And in the examples that we've done that, we've had big impact on the world. So share some of that with us because that's your focus. You were in, what department in Harvard, you said, government? So I sit in the government department. Unforeseen consequences of technology? Yes. Tell us about that. Well, you know, so in my, in the keynote, I talked about examples where technology is basically challenging every democratic value that we have. And sort of like no one's really aware we kind of think about it here and there. But by doing simple data science experiments, we can quantify that. We can demonstrate it. And by doing that, we shore up sort of those who could help us the most, the advocates, the regulators and journalists. And so I gave examples from my own work and from the work of my students. Tell me a little bit about your students, actually. Are they undergrads? Do you also have graduate students as well? I have both. You've both. I have both. The talk was about, I teach a class called data science to save the world. And we tackle three to four real-world problems within the semester that we solve. And then the students are left to do their own independent projects. And at the end, many of those go on to be published papers. Wow. I feel like you need to have like a cape or some sort of superhero emblem. We can work on that later. But tell me about the diversity within this student body at Harvard in your classes. Are you finding, maybe, the ratio of men to women, for example? Well, many of the universities from my time have really changed. So when I was an undergraduate, the typical classroom of Harvard undergrads would be all white men or mostly all white men. Sounds like a lot of stems, though. But now, if you walk into Harvard, we see a lot more diversity within the university. I'm also a faculty dean at one of their residential houses. And so the diversity is huge. However, when you start getting into computer science, you start seeing, you don't see as much diversity. But in the data sciences of the world course, we get students from all over. They come from different backgrounds. They come in different colors, shapes, and sizes. Each with a skill set and a desire to learn how to have impact. I think that desire is key. How do you help them sort of build their own confidence in terms of, regardless of what color, flavor, my peer group is, I like this. I want to be in this. How do you help ignite that confidence within someone that's quite new into this? So if you're 20-something or almost 20 and you do something that a regulator changes their laws or a newspaper article picks up or you're on the Today Show, that pretty much changes the course of your life. And that's what we found with the students. Some of them have done just some remarkable work that's really been picked up and exalted. And it's stayed with them. It's changed the direction in which they've gone. So what we do in the course is we teach them that there's just so many problems that are low hanging and how to spot a problem, an issue that they can solve and how to solve it in a way that can have impact. And that's really what the course focuses on. That impact is so important to just continue to feel someone's fire. For that person to then be empowered to be able to ignite a fire under somebody else. But I think one of the things that you mentioned sort of speaks to some of the things that we're seeing and these boundaries and lines are blurring. Not just so much even on from a gender perspective, but even career path, A, B, C, D. Now it's data is feeling the world. Every company is becoming a tough company because they have to be, right? To make consumer demands and just grow and be profitable as a business. But I also like the parallel there that these rigid maybe more rigid lines of careers are now opening up because like you're saying you can make impact being a data scientist in every sector you can influence policy. And wow, what a huge opportunity. It's almost like it's infinite, right? Yeah, I mean, if you look at even the range of talks in the conference today, you get a great sense of not only new tools in different areas, but just the sheer spectrum of areas in which data science is playing. Yes. And that these women are already working and already having impact. So speaking of the conference today, one of the things that I think is that we're hearing is it's not just about inspiring, I think Maria Clauves had said on the Cube previous to today that she found that young women in their first semester of university and college courses are probably like the right age and time in their lives to really ignite a spark. But I think there's also sort of a reinvigoration of the women that have been in technology and STEM fields for a while. Are you feeling and hearing kind of some of the same things from your peers and colleagues here? Definitely, we see it at the two levels. We want to, it's really important to try to get them in freshman year before they have a discipline defined for themselves or how they see themselves so that you can sort of ignite that spark and keep that spark alive. But then later women or others who are already in a field and looking for a way to sort of release and redefine themselves, data science is definitely giving them that opportunity. It really is. So what are some of the things that you're looking forward to for your career at Harvard as 2018 moves forward? Well, we, you know, the students, we try to tackle the big problems. Election vulnerabilities has been a big one for us on our agenda. The privacy of publicly available data is another big one that we've been working on. And, Mola, I think that's enough for a while. That's pretty big. Yeah. I think so. We'll get those done. Designing the logo for the T-shirt because you definitely need to have a super power T-shirt. So last question for you. If you could give young Latonya advice when you were just starting out college, not knowing any of this was going to happen in terms of this movement that is WIDDs in 2018, what would some of those key advice points for you, for your younger self be? To believe in yourself. To believe in yourself in that it's going to work, it's going to work out. One of the things that I grew to learn was how to turn lemons into lemonade. And that turns out to be very, very powerful because it's a way to bounce back when you're faced with things that you can't control, that people are trying to put obstacles in your way, you just sort of find another way to keep going. And the world sort of bend it towards me, so that was really cool. And also that failure is not a bad upward, right? Nah, that's absolutely correct. It's part of a natural course. And I think any leader in whatever industry you're in, whatever country, whatever ethnicity, gender, everybody has, I wouldn't even say missteps. It's just part of life, but I think... Yeah, it's just part of the what... At Harvard, like I said, I'm the dean in one of the faculty houses. And one of the main things that we do each throughout the year is invite speakers in who are accomplished in whatever area they're in. But the one thing that they all have in common is they took this really roundabout way to get where they are. And a lot of that was because failures and blocks came in the way. And that's really important, I think, for young adults to really understand. I agree. Well, Tony, thank you so much for carving out some time to stop by and chat with us on theCUBE. We are excited to have your wisdom shared to our audience and we wish you a great rest of the conference. All right, thank you very much. We'll see you next time on theCUBE. Okay. We want to thank you for watching theCUBE. I'm Lisa Martin. We are live from the third annual Women in Data Science Conference at Stanford University. Stick around after the short break. I'll be back with my next guest.