 Yeah, so that talk sort of stemmed out of the TED talk that I gave on owning your body's data and it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions and so ways that you can use like your temperature data or your heart rate data or what does this data say over time, what does it say about your body's health and really challenging the audience to get excited about looking at that data. We have so many devices that collect data automatically for us and often we don't pause long enough to actually look at that historical data and so that was what the talk was about today like here's what you can find when you actually sit down and look at that data. What's the most important data you think people should be collecting about themselves? Well definitely not your weight because I don't want to know what that is everyday. It depends you know I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body is fertile when you're ovulating it can so that information could give women during that time period really critical information but in general I think it's just a matter of being aware of how your body is changing so for some people maybe it's your blood pressure or your blood sugar if you have high blood pressure or high blood sugar those things become really critical to keep an eye on and I really encourage people whatever data they take to be active in the understanding of and interpretation of the data so it's not like if you take this data you'll be healthy you'll live to a hundred it's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that foundation is a really strong one. There's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than math or computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection. So I think one of the key things about the ethics panel here at WIDS this morning was that first of all it started the day which is a good sign it shouldn't be a separate topic of discussion we need this conversation about values about what we're trying to build for who we're trying to protect how we're trying to recognize individual human agency that has to be built in throughout data science so it's a good start to have a panel about it at the beginning of the conference but I'm hopeful that the rest of the conversation will not leave it behind. We talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control data scientists are building data sets and algorithmic forms of analyses that are both of those two things are just encoded sets of values and if you try to have a conversation about that at just the math level you're going to miss the social level you're going to miss the fact that that's humanity you're talking about so it needs to really be integrated throughout the process talking about the values of what you're manipulating and the values of the world that you're releasing these tools into. Yeah so into it we are a champion of gender diversity and also all sorts of diversity and when we first learned about WIDS we said we need to be a champion of the women in data science conference because for me personally often times when I'm in a room going over technical details I'm often the only woman and not just that I'm often the only woman executive and so part of the sponsorship is to create this community of women very technical women in this field to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science. So first of all at LinkedIn we truly believe in the vision that we are working towards which is really creating economic opportunity for every member of the global workforce and if you're kind of starting from that and thinking about that is our sort of the axiom that we're working towards and then thinking about how you can do that and then obviously the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us right so how can we do that we have some early effort in using and developing differential privacy as a technique for us to do a lot better with regarding preserving that privacy as we're leveraging the data but also at the same time it doesn't ends there right because you're thinking about creating opportunity it's not just about it's preserve the privacy but also when we are leveraging the data how can we leverage the data in a way that is able to create opportunity in a fair way so so there is also a lot of effort that we're having with regarding how can we do that and what does fairness mean what are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop a product the way that we're thinking about responsible design and the way that we build our algorithms the way that we measure in every single dimension