>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the Cubes. Continuing coverage of the fourth annual Women and Data Science Conference with Hashtag with twenty nineteen to join the conversation. Lisa Martin joined by one of the speakers on the career panel today at Stanford. Natalie Evans Harris, the cofounder and head of strategic initiatives at right hive. Natalie. It's a pleasure to have you on the program so excited to be here. Thank you. So you have, which I can't believe twenty years experience advancing the public sectors. Strategic use of data. Nearly twenty. I got more. Is your career at the National Security Agency in eighteen months with the Obama administration? You clearly were a child prodigy, of course. Of course, I was born in nineteen ninety two s. So tell me a little bit about how you got involved with was. This is such an interesting movement because that's exactly what it is in such a short time period. They of a mask. You know, they're expecting about twenty thousand people watching the live stream today here from Stanford. But there's also fifty plus countries participating with one hundred fifty plus a regional events. You're here on the career panel. Tell me a little bit about what attracted you to wits and some of the advice and learnings that you're going to deliver this afternoon. Sure, >> absolutely So Wits and the Women and Data Science Program and Conference on what it's evolved to are the exact type of community collective impact initiatives we want to say. When we think about where we want data science to grow, we need to have diversity in the space. There's already been studies that have come out to talk about the majority of innovations and products that come out are built by white men and built by white men. And from that lens you often lose out on the African American experience or divers racial or demographic experiences. So you want communities like women and data science to come together and show we are a part of this community. We do have a voice and a seat at the table, and we can be a part of the conversation and innovation, and that's what we want, right? So to come together and see thousands of people talking and walking into a room of diverse age and diverse experience, it feels good, and it makes me hopeful about the future because people is what the greatest challenge to data science is going to be in the future. >> Let's talk about that because a lot of the topics around data science relate to data privacy and ethics. Cyber security. But if we look at the amount of data that's generated every day, two point five quintillion pieces of data, tremendous amount of impact for the good. You think of cancer research and machine learning in cancer research. But we also think, Wow, we're at this data revolution. I read this block that you co authored it about a year ago called It's time to Talk About Data Ethics, and I found it so interesting because how how do we get control around this when we all know that? Yes, there is so many great applications for data that were that we benefit from every day. But there's also been a lack of transparency on a growing scale. In your perspective, how do what's the human capital element and how does that become influenced to really manage data in a responsible way? I think that >> we're recognizing that data can solve all of these really hard problems and where we're collecting these quintillion bytes of data on a daily basis. So there's acknowledgment that there's things that humans just can't d'oh so a I and machine learning our great ways to increase access to that data so we can use it to start to solve problems. But we also need to recognize is that no matter how good A I gets, there's still humans that need to be a part of that context because the the algorithms air on Lee as strong as the people that have developed them. So we need data scientist. We need women with diverse experiences. We need people with diverse thoughts because they're the ones we're going to create, those algorithms that make the machine learning and the and the algorithms in the technology more powerful, more diverse and more equal. So we need to see more growth and experiences and people and learning the things that I talk about. When I when others asked me and what I'll mention on the career panel is when you think about data science. It's not just about teaching the technical skills. There's this empathy that needs to be a part of it. There's this skill of being able to ask questions in really interesting ways of the data. When I worked at National Security Agency and helped build the data science program there, every data scientist that came into the building, we, of course taught them about working in our vitamins. But we also made every single one of them take a class on asking questions. The same class that we had our intelligence analyst take so the same ways of the history and the foreign language experts needed to learn how to ask questions of data we needed, Our data scientist told. Learn that as well. That's how you start to look beyond just the ones and zeros and start to really think about not just data but the people that are impacted by the use of the data. >> Well, it's really one of the things I find interesting about data. Science is how diverse on I use that word, specifically because we talked about thought diversity. But it's not just the technical skills as you mentioned. It's empathy. It's communication. It's collaboration on DH those air. So it's such a like I said, Diverse opportunity. One of the things I think I read about in your blawg. If we look at okay, we need to not just train the people on how to analyze the data but howto be confident enough to raise their hand and ask questions. How do you also train the people? >> Two. >> Handle data responsibly. You kind of mentioned there's this notion of sort of like a Hippocratic oath that medical doctors take for data scientist. And I thought that was really intriguing. Tell me a little bit more about that. And how do you think that data scientists in training and those that are working now can be trained? Yeah, influenced to actually take something like that in terms of really individualizing that responsibility for ethical treatment of data. So, towards the >> end of my time at the White House, we it was myself deejay Patil and a number of experts and thought leaders in the space of of news and ethics and data science came together and had this conversation about the future of data ethics. And what does it look like? Especially with the rise of fake news and misinformation and all of these things? And born out of that conversation was just this. This realization that if you believe that, inherently people want to do the good thing, want to do the right thing? How do they do that? What does that look like? So I worked with Data for Democracy and Bloomberg to Teo issue a study and just say, Look, data scientist, what keeps you up at night? What are the things that as you as you build these algorithms and you're doing this? Data sharing keeps you up at night. And the things that came out of those conversations and the working groups and the community of practice. Now we're just what you're talking about. How do we communicate responsibly around this? How do we What does it look like to know that we've done enough to protect the data, to secure the data, to, to use the data in the most appropriate ways? And when we >> see a problem, what do >> we do to communicate that problem and address it >> out of >> that community of practice? And those principles really came the starts of what an ethics. Oh, the Hippocratic oath could look like it's a set of principles. It's not the answer, but it's a framework to help guide you down. Your own definition of what ethical behaviour looks like when you use data. Also, it became a starting point for many companies to create their own manifestos and their own goals to say as a company, these are the values that we're going to hold true to as we use data. And then they can create the environments that allow for data scientists to be able to communicate how they feel about what is happening around them and effect change. It's a form of empowerment. Amazing. I love >> that in the last thirty seconds, I just want to get your perspective on. Here we are spring of twenty nineteen. Where are we as a society? Mon data equaling trust? >> Oh, I love that we're having the conversation. And so we're at that point of just recognizing that data's more than ones and zeroes. And it's become such an integral part of who people are. And so we need some rules to this game. We need to recognize that privacy is more than just virus protection, that there is a trust that needs to be built between the individuals, the communities and the companies that are using this data. What the answers are is what we're still figuring out. I argue that a large part of it is just human capital. It's just making sure that you have a diverse set of voices, almost a brain trust as a part of the conversation. So you're not just going to the same three people and saying, What should we d'Oh But you're growing and each one teach one and building this community around collectively solving these problems. Well, >> Natalie's been such a pleasure talking with you today. Thank you so much for spending some time and joining us on the Cuban. Have a great time in the career panel this afternoon. Atwood's. >> Thank you so much. This is a lot of fun. >> Good. My pleasure. We want to thank you. You're watching the Cube from the fourth annual Women and Data Science Conference alive from Stanford University. I'm Lisa Martin. I'll be back with my next guest after a short break