>>Welcome back to the cubes coverage of women in data science, 2022. We've been live all day at Stanford at the Arriaga alumni center. Lisa Martin, John furrier joins me next, trying to, to cure your FOMO that you have. >>I love this events. My favorite events is 2015. We've been coming, growing community over 60 countries, 500 ambassadors and growing so many members. Widths has become a global phenomenon. And it's so exciting to be part of just being part of the ride. Judy and Karen, the team have been amazing partners and it's been fun to watch the progression and international women's day is tomorrow. And just the overall environment's changed a lot since then. It's gotten better. I'm still a lot more work to do, but we're getting the word out, but this year seems different. It seems more like a tipping point is happening and real-time cultural change. A lot of problems. COVID pulled forward. A lot of things, there's a war going on in Europe. It's just really weird time. And it's just seems like it's a tipping point. >>I think that's what we felt today was that it was a tipping point. There was a lot of our guests on the program that are first time with attendees. So in seven, just seven short years, this is the seventh annual width it's gone from this one day technical conference to this global movement, as you talked about. And I think that we definitely felt that women of all ages and men that are here as well understand we're at that tipping point and what needs to be done next to push it over the edge. >>Well, I'm super excited that you are able to do all the amazing interviews. I watched some of them online. I had to come by and, and join the team because I have FOMO. I love doing the interviews, but they're including me. I'm happy to be included, but I got to ask you, I mean, what was different this year? Because it was interesting. It's a hybrid event. It's in part, they didn't have it in person last year, right? So it's hybrid. I showed the streams where everywhere good interviews, what was some of the highlights? >>Just a very inspiring stories of women who really this morning's conversation that I got to hear before I came to set was about mentors and sponsors and how important it is for women of any age and anybody really to build their own personal board of directors with mentors and sponsors. And they were very clear in the difference between a mentor and a sponsor and John something. I didn't understand the difference between the two until a few years ago. I think it was at a VMware event and it really surprised me that I have mentors do ask sponsors. And so that was a discussion that everybody on this onset talked about. >>It was interesting. We're doing also the international women's day tomorrow, big 24 interviews, including the winds of content, as well as global women leaders around the world and to new J Randori, who runs all of AWS, Amy are your maps. And she told me the same thing. She's like, there's too many mentors, not enough sponsors. And she said that out loud. I felt, wow. That was a defining moment because he or she is so impressive. Worked at McKinsey, okay. Was an operator in, in running businesses. Now she heads up AWS saying out loud, we have too many mentors, this get down to business and get sponsors. And I asked her the same thing and she said, sponsors, create opportunities. Mentors, give feedback. And mentors go both ways. And she said, my S my teenage son is a mentor to me for some of the cool new stuff, but ventures can go both ways. Sponsors is specifically about opportunities, and I'm like, I felt like that comment hit home. >>It's so important, but it's also important to teach girls. And especially the there's younger girls here this year, there's high school and middle, I think even middle school girls here, how to have the confidence to, to find those mentors, those sponsors and cultivate those relationships. That's a whole, those are skills that are incredibly important, as important as it is to understand AI data science, machine learning. It's to be able to, to have the confidence in a capability to create that and find those sponsors to help you unlock those opportunities. >>You know, I feel lucky to do the interviews, certainly being included as a male, but you've been doing a lot of the interviews as females and females. I got to ask you what was the biggest, because every story is different. Some people will it's about taking charge of their career. Sometimes it's maybe doing something different. What some of the story themes did you see in your interviews out there? What were some of the, the coverings personal? Yeah. >>Yeah. A lot of, a lot of the guests had stem backgrounds and were interested in the stem studies from when they were quite young and had strong family backgrounds that helps to nurture that. I >>Also heard that role models. Yes, >>Exactly, exactly. A strong family backgrounds. I did talk to a few women who come from different backgrounds, like international business and, but loved data and wanted to be able to apply that and really learn data analytics and understand data science and understand the opportunities that, that it brings. And also some of the challenges there. Everybody had an inspiring story. >>Yeah. It's interesting. One of the senior women I interviewed, she was from Singapore and she fled India during a bombing war and then ended up getting her PhD. Now she's in space and weld and all that stuff. And she said, we're now living in nerd, native environment, me and the younger generation they're nerds. And I, you know, were at Stanford dirt nation. Of course we're Stanford, it's nerd nerd nation here. But her point is, is that everything's digital now. So the younger generation, they're not necessarily looking for programmers, certainly coding. Great. But if you're not into coding, you can still solve society problems. There's plenty of jobs that are open for the first time that weren't around years ago, which means there's problems that are new to that need new minds and new, fresh perspectives. So I thought that aperture of surface area of opportunities to contribute in women in tech is not just coding. No, and that was a huge, >>That was, and we also, this morning, I got to hear, and we've talked about, we talked with several of the women before the event about data science in healthcare, data science, in transportation equity. That was a new thing for me, John, that I didn't know, I didn't, I never thought about transient equity and transportation or lack thereof. And so w what this conference showed, I think this year is that the it's not just coding, but it's every industry. As we know, every company is a data company. Every company is a tech company. If they're not, they're not going to be here for a long. So the opportunities for women is the door is just blown. >>And I said, from my interviews, it's a data problem. That's our line. We always say in the cube, people who know our program programming, we say that, but it actually, when we get the data on the pipeline and the pipeline, it has data points where the ages of drop-off of girls and young women is 12 to 14 and 16 to 18, where the drop-off is significant. So attack the pipelining problem is one that I heard a lot of. And the other one that comes out a lot, it's kind of common sense, and it's talked about it, but it's nuanced, but it became very elevated this year in the breaking, the bias theme, which was role models are huge. So seeing powerful women in leadership positions is really a focus and that's inspires people and they can see themselves. And so I think when people see role models of women and, and folks on in positions, not just coded, but even at the executive suite huge focus. So I think that's going to be a next step function in my mind. That's that's, if I had to predict the trend, it would be you see a lot more role modeling, flexing that big time. >>Good that's definitely needed. You know, we, we often used to say she can't be what she can't see, but one of the interviews that I had said, she can be what she can see. And I loved the pivot on that because it put a positive light, but to your point, there needs to be more female role models that, that girls can look up to. So they can see, I can do this. Like she's doing leading, you know, YouTube, for example, or Sheryl Sandberg of Facebook. We need more of these role models to show the tremendous amount of opportunities that are there, and to help those, not just the younger girls, those even that are maybe more mature find that confidence to build. >>And I think that was another king that came out role models from family members, dad, or a relative, or someone that could see was a big one. The other common thread was, yeah. I tend to break stuff and like to put it together. So at a young age, they kind of realized that they were kind of nerdy and they like to do stuff very engineering, but mind is where math or science. And that was interesting. Sally eaves from in the UK brought this up, she's a professor and does cyber policy. She said, it's a stems gray, but put the arts in there, make it steam. So steam and stem are in two acronyms. Stem is, is obviously the technical, but adding arts because of the creativity needs, we need creativity and problem solving with technical. Yes. So it's not just stem it's theme. We've heard that before, but not as much this year, it's amplified big >>Time. Sally's great. I had the chance to interview her in the last couple of months. And you, you bring up creativity, which is an incredibly important point. You know, there are the, obviously the hard skills, the technical skills that are needed, but there's also creativity. Curiosity being curious to ask a question, there's probably many questions that we haven't even thought to ask yet. So encouraging that curiosity, that natural curiosity is as important as maybe someone say as the actual technical knowledge, >>What was the biggest thing you saw this year? If you zoom out and you look at the forest from the trees, what was the big observation for you this year? >>I think it's the growth of woods. We've decided seven years. It's now in 60 countries, 200 events, 500 ambassadors, probably 500 plus. And the number of people that I had on the program, John, that this is their first woods. So just the fact that it's growing, we, we we've seen it for years, but I think we really saw a lot of the fresh faces and heard from them today had stories of how they got involved and how they met Margo, how she found them. I had a younger Alon who'd just graduated from Harvard back in the spring. So maybe not even a year ago, working at Skydio, doing drone work and had a great perspective on why it's important to have women in the drone industry, the opportunities Jones for good. And it was just nice to hear that fresh perspective. And also to S to hear the women who are new to woods, get it immediately. You walk into the Arriaga alumni center in the morning and you feel the energy and the support and that it was just perpetuated year after year. >>Yeah, it's awesome. I think one of the things I think it was reflecting on this morning was how many women we've interviewed in our cube alumni database now. And we yet are massing quite the database of really amazing people and there's more coming in. So that was kind of on a personal kind of reflection on the cube and what we've been working on together. All of us, the other thing that jumped out at me was the international aspect this year. It just seems like there's a community of tribal vibe where it's not just the tech industry, you know, saying rod, rod, it's a complete call to arms around more stories, tell your story. Yes. More enthusiasm outside of the corporate kind of swim lanes into like more of, Hey, let's get the stories out there. And the catalyst from an interview turned into follow up on LinkedIn, just a lot more like viral network effect so much more this year than ever before. So, you know, we just got to get the stories. >>Absolutely. And I think people given what we've been through the last two years are just really hungry for that. In-person collaboration, the opportunity to see more leadership to get inspired and any level of their career. I think the women here this today have had that opportunity and it's been overwhelmingly positive as you can imagine as it is every year. But I agree. I think it's been more international and definitely much more focused on teaching some of the other skills, the confidence, the creativity, the curiosity. >>Well, Lisa, as of right now, it's March 8th in Japan. So today, officially is kicking off right now. It's kicking off international women's day, March 8th, and the cube has a four region portal that we're going to make open, thanks to the sponsors with widths and Stanford and AWS supporting our mission. We're going to have Latin America, AMIA Asia Pacific and north America content pumping on the cube all day today, tomorrow. >>Exactly. And we've had such great conversations. I really enjoyed talking to the women. I always, I love hearing the stories as you talked about, we need more stories to make it personal, to humanize it, to learn from these people who either had some of them had linear paths, but a lot of emergency zig-zaggy, as you would say. And I always find that so interesting to understand how they got to where they are. Was it zig-zaggy, was it zig-zaggy intentionally? Yes. Some of the women that I talked to had very intentional pivots in their career to get them where they are, but I still thought that story was a very, >>And I like how you're here at Stanford university with winds the day before international Wednesday, technically now in Asia, it's starting, this is going to be a yearly trend. This is season one episode, one of the cube covering international women's day, and then every day for the rest of the year, right? >>What were some of your takeaways from some of the international women's day conversations that you had? >>Number one thing was community. The number one vibe was besides the message of more roles or available role models are important. You don't have to be a coder, but community was inherently the fabric of every conversation. The people were high energy, highly knowledgeable about on being on point around the core issue. It wasn't really politicized was much more of about this is really goodness and real examples of force multipliers of diversity, inclusion and equity, when, what works together as a competitive advantage. And, you know, as a student of business, that is a real change. I think, you know, the people who do it are going to have a competitive advantage. So community competitive advantage and just, and just overall break that bias through the mentoring and the sponsorships. >>And we've had a lot of great conversations about, I loved the theme of international women's day, this year breaking the bias. I asked everybody that I spoke with for international women's day and for width. What does that mean to you? And where are we on that journey? And everyone had a really insightful stories to share about where we are with that in their opinions, in their fields industries. Why, and ultimately, I think the general theme was we have the awareness now that we need, we have the awareness from an equity perspective, that's absolutely needed. We have to start there, shine the light on it so that the bias can be broken and opportunities for everybody can just proliferate >>Global community is going to rise and it's going to tend to rise. The tide is rising. It's going to get better and better. It was a fun year this year. And I think it was relief that COVID kind of going out, people getting back into physical events has been, been really, really great. >>Yep, absolutely. So, John, I, I appreciate all the opportunities that you've given me as a female anchor on the show. International women's day coverage was fantastic. Widths 2022 coming to an end was fantastic. Look forward to next year. >>Well, Margo, Judy and Karen who put this together, had a vision and that vision was right and it was this working and when it gets going, it has escape, velocity unstoppable. >>It's a rocket ship. That's a rocket. I love that. I love to be part of John. Thanks for joining me on the wrap. We want to thank you for watching the cubes coverage of international women's day. The women's showcase as well as women in data science, 2022. We'll see you next time.

>>Welcome everyone to the cubes coverage of women in data science conference width 2022. Live from Stanford at the Arriaga alumni center. I'm Lisa Martin. My next guest is joined me. Sharon Hutchins is here the VP and chief of AI plus data operations at Intuit Sharon. Welcome. Thank you. >>Excited to >>Be here. This is your first woods, very first but into it in words. >>That's right. Intuitively it's goes way back. I'm relatively new to the organization, but Intuit has been a long time sponsor of woods, and we love this organization. We have a great alignment with our goals, which has a passion and commitment to advancing women in technology and data science. And we have the same goal added to it. We are at 30% women in technology with the goal of hitting 37% by 2024. And I know that widths has a great goal of 30 by 30, so that's awesome. >>30 by 30. And here we are around, I think it's still less than 25% of stem positions are filled by women. But obviously you're ahead of that on Intuit congratulate. >>I think we're ahead of that. And I think part of the reason why we're ahead of that is because we've got great programs at Intuit to support women. One of our key programs is tech women at Intuit. And so it's an internal initiative where we focus on attracting, retaining and advancing women. So it's a great way for women across technology to support one another. Sure. You've heard of the term there's power in the pack, and we believe that when we connect women, we can help elevate their voices, which elevates our business and elevates our products. >>It does. In fact, there's some stats I was looking at recently that just showed if there was even 30% females at the executive level, how much more profitable organizations can be in how much higher performance they can have. So the data is there that suggests this is a really smart business decision to be making. >>Absolutely absolutely the data is, is no lie. I see it firsthand in my own business. And in fact, at Intuit, we've got a broader initiative around diversity and inclusion. It's led from the top. We have set goals across the company and we hold ourselves accountable because we know that if there are more women at the table and more diversity at the table, all around, we make better business decisions. And if you look at our product suite, which is a terrible tax, QuickBooks, mint, credit, karma, and MailChimp, we've got a diverse customer base of a hundred thousand, sorry, a hundred million customers. And so it's a lot of diversity in our customer base and we want a lot of diversity in the company. >>Fantastic. That there's such a dedicated effort to it. You just came in here from the career panel. Talk to me about that. What were some of the key things that were discussed? Yeah, >>I have my notebook open here because there were so many great takeaways from actually just from the day in general. I'm just so at, at the types of issues that women are tackling across different industries, they're tackling bias. And we know that bias is corrected when women are at the table, but from a career perspective, some of the things that were mentioned from the panel is the fact that women need to own their own careers and they need to actively manage their careers. And there's only so much your manager can do and should do. You've got to be in the driver's seat, driving your own career. One of the things that we've done at Intuit as we've implemented sort of a self promoting process. So twice a year during our promotion period, either your manager can nominate you for a promotion or you can self promote. So it's all about you creating a portfolio of all of your great work. And of course, you know, managers are very supportive of the process and support, you know, women and, and all technologists in crafting their portfolios for a fair chance at promotion. And so we just believe that if you take bias out of a career progression, you can close that fair and equitable gap that we see sometimes across industries with compensation. >>This is, that would be great if we can ever get there. One of the things that's nice about woods, I think it was last year or the year before they opened it up to high school students. So it was so nice walking in this morning, seeing the young, fresh faces, the mature faces, but you bring up a great point of women need to be their own mini to create their own personal board of directors and really be able to, to be at the helm of their career. Do you, did you find that the audience is receptive to that? Do they have the confidence to be able to do that? >>Yeah, absolutely. And, and that was a point that was raised a couple of times this morning, there were women who talked about having great mentors, but it is more important to have a board of your personal board of directors than one mentor, because you've got to make sure that you sort of tackle all aspects of your career life. And you know, it's not all about the technology, a good portion of how you spend your time and where you spend your time is collaborating and negotiating and communicating across the company. And so that's very important. And so that was a key message that folks shared this morning. >>That's good. That's incredibly important. I wish we had more time. You've got to run to the airport. Sharon, it's been a pleasure to have you on the program. Thank you for sharing what Intuit and woods are doing together, your involvement and some of the great messages, inspiring messages from the career panel. >>Exactly. And for all of the young expiring high school students. Yes. We want them to check out into it. www.intuit.com, careers, >>Intuit.com. Is it slash careers slash careers slash careers perfectly. I'm an Intuit customer. I will say. Awesome. It's been a pleasure talking to you. Thank you, Sharon. Bye-bye for Sharon Hutchins. I'm Lisa Martin. You're watching the cubes coverage of women in data science, 2022.

>>Hey everyone. Welcome back to the cubes. Live coverage of women in data science, worldwide conference widths 2022. I'm Lisa Martin coming to you from Stanford university at the Arriaga alumni center. And I'm pleased to welcome my next guest. Hannah Sperling joins me business process intelligence or BPI, academic and research alliances at SAP HANA. Welcome to the program. >>Hi, thank you so much for having me. >>So you just flew in from Germany. >>I did last week. Yeah. Long way away. I'm very excited to be here. Uh, but before we get started, I would like to say that I feel very fortunate to be able to be here and that my heart and vicious still goes out to people that might be in more difficult situations right now. I agree >>Such a it's one of my favorite things about Wiz is the community that it's grown into. There's going to be about a 100,000 people that will be involved annually in woods, but you walk into the Arriaga alumni center and you feel this energy from all the women here, from what Margo and teams started seven years ago to what it has become. I was happened to be able to meet listening to one of the panels this morning, and they were talking about something that's just so important for everyone to hear, not just women, the importance of mentors and sponsors, and being able to kind of build your own personal board of directors. Talk to me about some of the mentors that you've had in the past and some of the ones that you have at SAP now. >>Yeah. Thank you. Um, that's actually a great starting point. So maybe talk a bit about how I got involved in tech. Yeah. So SAP is a global software company, but I actually studied business and I was hired directly from university, uh, around four years ago. And that was to join SAP's analytics department. And I've always had a weird thing for databases, even when I was in my undergrad. Um, I did enjoy working with data and so working in analytics with those teams and some people mentoring me, I got into database modeling and eventually ventured even further into development was working in analytics development for a couple of years. And yeah, still am with a global software provider now, which brought me to women and data science, because now I'm also involved in research again, because yeah, some reason couldn't couldn't get enough of that. Um, maybe learn about the stuff that I didn't do in my undergrad. >>And post-grad now, um, researching at university and, um, yeah, one big part in at least European data science efforts, um, is the topic of sensitive data and data privacy considerations. And this is, um, also topic very close to my heart because you can only manage what you measure, right. But if everybody is afraid to touch certain pieces of sensitive data, I think we might not get to where we want to be as fast as we possibly could be. And so I've been really getting into a data and anonymization procedures because I think if we could random a workforce data usable, especially when it comes to increasing diversity in stem or in technology jobs, we should really be, um, letting the data speak >>And letting the data speak. I like that. One of the things they were talking about this morning was the bias in data, the challenges that presents. And I've had some interesting conversations on the cube today, about data in health care data in transportation equity. Where do you, what do you think if we think of international women's day, which is tomorrow the breaking the bias is the theme. Where do you think we are from your perspective on breaking the bias that's across all these different data sets, >>Right. So I guess as somebody working with data on a daily basis, I'm sometimes amazed at how many people still seem to think that data can be unbiased. And this has actually touched upon also in the first keynote that I very much enjoyed, uh, talking about human centered data science people that believe that you can take the human factor out of any effort related to analysis, um, are definitely on the wrong path. So I feel like the sooner that we realize that we need to take into account certain bias sees that will definitely be there because data is humanly generated. Um, the closer we're going to get to something that represents reality better and might help us to change reality for the better as well, because we don't want to stick with the status quo. And any time you look at data, it's definitely gonna be a backward looking effort. So I think the first step is to be aware of that and not to strive for complete objectivity, but understanding and coming to terms with the fact just as it was mentioned in the equity panel, that that is logically impossible, right? >>That's an important, you bring up a really important point. It's important to understand that that is not possible, but what can we work with? What is possible? What can we get to, where do you think we are on the journey of being able to get there? >>I think that initiatives like widths of playing an important role in making that better and increasing that awareness there a big trend around explainability interpretability, um, an AI that you see, not just in Europe, but worldwide, because I think the awareness around those topics is increasing. And that will then, um, also show you the blind spots that you may still have, no matter how much you think about, um, uh, the context. Um, one thing that we still need to get a lot better at though, is including everybody in these types of projects, because otherwise you're always going to have a certain selection in terms of prospectus that you're getting it >>Right. That thought diversity there's so much value in thought diversity. That's something that I think I first started talking about thought diversity at a Wood's conference a few years ago, and really understanding the impact there that that can make to every industry. >>Totally. And I love this example of, I think it was a soap dispenser. I'm one of these really early examples of how technology, if you don't watch out for these, um, human centered considerations, how technology can, can go wrong and just, um, perpetuate bias. So a soap dispenser that would only recognize the hand, whether it was a certain, uh, light skin type that w you know, be placed underneath it. So it's simple examples like that, um, that I think beautifully illustrate what we need to watch out for when we design automatic decision aids, for example, because anywhere where you don't have a human checking, what's ultimately decided upon you end up, you might end up with much more grave examples, >>Right? No, it's, it's I agree. I, Cecilia Aragon gave the talk this morning on the human centered guy. I was able to interview her a couple of weeks ago for four winds and a very inspiring woman and another herself, but she brought up a great point about it's the humans and the AI working together. You can't ditch the humans completely to your point. There are things that will go wrong. I think that's a sends a good message that it's not going to be AI taking jobs, but we have to have those two components working better. >>Yeah. And maybe to also refer to the panel discussion we heard, um, on, on equity, um, I very much liked professor Bowles point. Um, I, and how she emphasized that we're never gonna get to this perfectly objective state. And then also during that panel, um, uh, data scientists said that 80% of her work is still cleaning the data most likely because I feel sometimes there is this, um, uh, almost mysticism around the role of a data scientist that sounds really catchy and cool, but, um, there's so many different aspects of work in data science that I feel it's hard to put that all in a nutshell narrowed down to one role. Um, I think in the end, if you enjoy working with data, and maybe you can even combine that with a certain domain that you're particularly interested in, be it sustainability, or, you know, urban planning, whatever that is the perfect match >>It is. And having that passion that goes along with that also can be very impactful. So you love data. You talked about that, you said you had a strange love for databases. Where do you, where do you want to go from where you are now? How much more deeply are you going to dive into the world of data? >>That's a good question because I would, at this point, definitely not consider myself a data scientist, but I feel like, you know, taking baby steps, I'm maybe on a path to becoming one in the future. Um, and so being at university, uh, again gives me, gives me the opportunity to dive back into certain courses and I've done, you know, smaller data science projects. Um, and I was actually amazed at, and this was touched on in a panel as well earlier. Um, how outdated, so many, um, really frequently used data sets are shown the realm of research, you know, AI machine learning, research, all these models that you feed with these super outdated data sets. And that's happened to me like something I can relate to. Um, and then when you go down that path, you come back to the sort of data engineering path that I really enjoy. So I could see myself, you know, keeping on working on that, the whole data, privacy and analytics, both topics that are very close to my heart, and I think can be combined. They're not opposites. That is something I would definitely stay true to >>Data. Privacy is a really interesting topic. We're seeing so many, you know, GDPR was how many years did a few years old that is now, and we've got other countries and states within the United States, for example, there's California has CCPA, which will become CPRA next year. And it's expanding the definition of what private sensitive data is. So we're companies have to be sensitive to that, but it's a huge challenge to do so because there's so much potential that can come from the data yet, we've got that personal aspect, that sensitive aspect that has to be aware of otherwise there's huge fines. Totally. Where do you think we are with that in terms of kind of compliance? >>So, um, I think in the past years we've seen quite a few, uh, rather shocking examples, um, in the United States, for instance, where, um, yeah, personal data was used or all proxies, um, that led to, uh, detrimental outcomes, um, in Europe, thanks to the strong data regulations. I think, um, we haven't had as many problems, but here the question remains, well, where do you draw the line? And, you know, how do you design this trade-off in between increasing efficiency, um, making business applications better, for example, in the case of SAP, um, while protecting the individual, uh, privacy rights of, of people. So, um, I guess in one way, SAP has a, as an easier position because we deal with business data. So anybody who doesn't want to care about the human element maybe would like to, you know, try building models and machine generated data first. >>I mean, at least I would feel much more comfortable because as soon as you look at personally identifiable data, you really need to watch out, um, there is however ways to make that happen. And I was touching upon these anonymization techniques that I think are going to be, um, more and more important in the, in the coming years, there is a proposed on the way by the European commission. And I was actually impressed by the sophisticated newness of legislation in, in that area. And the plan is for the future to tie the rules around the use of data science, to the specific objectives of the project. And I think that's the only way to go because of the data's out there it's going to be used. Right. We've sort of learned that and true anonymization might not even be possible because of the amount of data that's out there. So I think this approach of, um, trying to limit the, the projects in terms of, you know, um, looking at what do they want to achieve, not just for an individual company, but also for us as a society, think that needs to play a much bigger role in any data-related projects where >>You said getting true anonymization isn't really feasible. Where are we though on the anonymization pathway, >>If you will. I mean, it always, it's always the cost benefit trade off, right? Because if the question is not interesting enough, so if you're not going to allocate enough resources in trying to reverse engineer out an old, the tie to an individual, for example, sticking true to this, um, anonymization example, um, nobody's going to do it right. We live in a world where there's data everywhere. So I feel like that that's not going to be our problem. Um, and that is why this approach of trying to look at the objectives of a project come in, because, you know, um, sometimes maybe we're just lucky that it's not valuable enough to figure out certain details about our personal lives so that nobody will try, because I am sure that if people, data scientists tried hard enough, um, I wonder if there's challenges they wouldn't be able to solve. >>And there has been companies that have, you know, put out data sets that were supposedly anonymized. And then, um, it wasn't actually that hard to make interferences and in the, in the panel and equity one lab, one last thought about that. Um, we heard Jessica speak about, uh, construction and you know, how she would, um, she was trying to use, um, synthetic data because it's so hard to get the real data. Um, and the challenge of getting the synthetic data to, um, sort of, uh, um, mimic the true data. And the question came up of sensors in, in the household and so on. That is obviously a huge opportunity, but for me, it's somebody who's, um, very sensitive when it comes to privacy considerations straight away. I'm like, but what, you know, if we generate all this data, then somebody uses it for the wrong reasons, which might not be better urban planning for all different communities, but simple profit maximization. Right? So this is something that's also very dear to my heart, and I'm definitely going to go down that path further. >>Well, Hannah, it's been great having you on the program. Congratulations on being a Wood's ambassador. I'm sure there's going to be a lot of great lessons and experiences that you'll take back to Germany from here. Thank you so much. We appreciate your time for Hannah Sperling. I'm Lisa Martin. You're watching the QS live coverage of women in data science conference, 2020 to stick around. I'll be right back with my next guest.

>>Welcome everyone to the cubes coverage of women in data science, worldwide conference 2022. I'm Lisa Martin, coming to you live from Stanford university at the Arriaga alumni center. It's great to be back at widths in person, and I'm pleased to welcome fresh from the main stage Tiara Bill's assistant professor at UCLA Tierra. Welcome to the program. >>I'm glad to be here. Thank you for having me. Tell >>Me a little bit about your background. You're a civil engineer and I was telling you, so it was my dad. So I'm, I'm partial to civil engineers, but give our audience an overview of your background, what you studied and all that. Good. >>Yeah. So I'm a civil engineer, um, specifically transportation engineer, um, at UCLA. I also have an appointment in the public policy department. And so, um, I'm split between the two, my work focuses on travel demand modeling and how to use these tools to better inform, uh, and learn more about transportation equity and how to advance transportation equity. Um, and what that means is that we are prioritizing the needs of vulnerable communities, um, in terms of the data that we're using, the models that we're using to guide decision-making, um, in terms of the very projects that we evaluate and ultimately the decisions that we make to invest in certain transportation improvements. How >>Did you get interested in transportation equity? >>Yeah, so I think it, it stems from growing up, uh, in Detroit, some or Detroit born and raised native, and it stems from growing up in an environment where it was very clear that space matters that where you live the most, that you have access to, uh, whether you have a car or not. Um, whether you have flexibility in your, in your travel, it all matters. And it all governs the opportunities that you have access to. So it was very clear to me, um, when I would realize that certain certain kids didn't really leave their neighborhood, you know, they didn't travel about the city, let alone outside of the city and abroad. And so, um, and there are also other, you know, examples of, um, there are examples and cases after case where it's clear that communities are, um, being exposed to a high level of emissions, for example, um, that might result from transportation, but they're not positioned to benefit, um, in the same ways that the people who own the infrastructure on the freight or what have you. So, um, these are all very real experiences that have motivated my interest in transportation equity. >>Interesting. It's something I actually had never thought about, but you bring up a great point. How are talk to me about the travel demand models, how they're relevant and, and where some of the biases are in travel data, >>Right? So travel demand models, they are they're computational tools. They're empirically estimated meaning that their estimated from raw data, um, everything about them is driven by the data that you have access to. And how they're used is in largely in regional transportation planning, when it is necessary for regions to assess 10, maybe 15, 20 years into the future. Um, how is transportation going to change as a result of changes in travel patterns, growth in the population, um, changes and how firms are distributed across the landscape. Um, environmental changes, all sorts of changes that, um, that guide and direct our transportation decisions at an individual level. So regions are assessing these things over time and they need these powerful travel demand models in order to perform those assessments. And then they also, once they have an understanding of what the need is, because for example, they expect traffic congestion to improve, or sorry to increase over time. Um, there needs to be a means of assessing alternatives for mitigating those issues. And so they use the same types of models to understand if we expand highway capacity, if we, uh, build a new form of transit, is that going to mitigate, uh, the challenges that we're going to face in the future >>And travel demand, modeling and equity? What's the connection there? I imagine there's a pretty good >>Deep connection, right? So the connection is that. So we're using these tools to decide on the future of transportation investments and because of a history of understanding that we have around how ignoring the conditions for vulnerable communities, ignoring how, um, uh, transportation decisions might differentially impact different, different groups, different segments. Um, if we ignore that, then it can lead to devastating outcomes. And so I'm citing, um, examples of the construction of the Eisenhower interstate system back in the fifties and sixties, where, uh, we know today that there were millions of black and minority communities that were, uh, displace. Um, they weren't fairly compensated all because of lack of consideration for, for outcomes to these communities and the planning process. And so we are aware that these kinds of things can happen. Um, and because of that, we now have federal regulations that require, uh, equity analysis to occur for any project that's going to leverage federal funding. And so it's, it's tied to our understanding of what can happen when we don't focus on equity is also tied to what the current regulations are, but challenge is that we need better guidance on how to do this, how to perform the equity analysis. What types of improvements are actually going to move the needle and advance us toward a state where we can prioritize the needs of the vulnerable travelers and residents? What >>Excites you about the work that you're doing? >>You know, I, I have a vested interest in seeing conditions improve for, um, for the underdog, if you will, for folks who, um, they, they work hard, but they still struggle, um, for folks who experience discrimination in different forms. Um, and so I have a vested interest in seeing conditions improve for them. And so I'm really excited about, uh, the time that we're in, I'm excited that equity is now at the height of many discussions, um, because it's opening up resources, right? To have, uh, more folks paying attention, more folks, researching more folks, developing methods and processes that will actually help to advance equity, >>Advancing equity. We definitely need that. And you're right. There's, there's good V visibility on it right now. And let's take advantage of that for the good things that can come out of it. Talk to me a little bit about what you talked about in your talk earlier today here at widths. >>Right? So today I got a chance to elaborate on how travel demand models can end up, um, uh, with, with issues of bias and under-representation, and it's tied to a number of things, but one of them is the data that reusing, because these are, uh, empirically estimated tools. They take their form, they take their, uh, significance. Everything about them is shaped by the data that we use. Um, and at the same time, we are aware that vulnerable communities are more prone to issues that contribute to data bias. And under-representation so issues, for example, like non-response, um, issues like coverage bias with means that, um, certain groups are for whatever reason, not in the sample link frame. Um, and so, because we know that these types of errors are more prevalent for vulnerable communities, it brings, uh, it raises questions about, um, the quality of the decisions that come out of these models that we estimate based on these data. >>And so I'm interested in weaving these parts together. Um, and part of it has to do with understanding the conditions that, um, that underlie the data. So what do I mean by conditions? I gave an example of, uh, cases where there is discrimination and as evidenced by the data that we have available as evidenced, uh, for example, by examining, um, the quality of service across racial groups, um, using Uber and Lyft, right? So we have information that, that, that presents this to us, but that information is still outside of what we typically use to estimate travel, demand models. That information is not being used to understand the context under which people are making decisions. It's not being used to better understand the constraints that people are facing when they're making, uh, decisions. And so what is the connection that means that we are using data, um, that does not will capture the target group. >>People who are low income, elderly, um, transit dependent, uh, we're not capturing these groups very well because of the prevalence of, of various types of survey bias. Um, and it is shaping our models in unknown ways. And so my group is really trying to make that connection between, okay, how do we collect Bader, better data, first of all, but second, what does that mean? What are the ramifications for prediction, accuracy for VR, for various groups, and then beyond that, what are the policy implications? Right. Um, I think that the risk is that we might be making wrong decisions, right? We might be assuming that, uh, certain types of improvements are actually going to improve quality of service for vulnerable communities when they actually don't. Right. Um, and so that's the worry and that's part of the unknown, and that's why I'm working in this >>Part of the anonymity. Also, I'm sure part of your passion and your interest international women's day is tomorrow. And the theme this year is break the bias of breaking the bias with >>Mercy back >>To travel equity. Where do you think we are on, on being able to start mitigating some of the biases that you've talked about? >>I think that it's all about phasing. I think that there are things that we can do now, right? And so, um, at the point of making decisions, um, we can view the results that we have through this lens, that it might be an incomplete picture. We can view it through a historical lens. We can also view it, um, using emerging data that allows for us to explore some of these constraints that, you know, might be exogenous to the models or X, not in, not included in how we estimate the models. Um, and so that's one thing that we can do in practice is okay. We already know that there are some challenges let's view this from a different lens, as opposed to assuming that it's giving us the complete picture. Right. Um, and that's kind of been my theme, uh, today is that, you know, as decision-makers, as analysts, as data scientists, as researchers, we do have tendency of assuming that the data that we have, the results that we have is giving us the complete picture when we know, but it's not, we know that we act as if it is, but we know that it's not right. >>So, you know, we need to, there's a lot of learning and changing of behaviors, um, that that has to happen. >>Changing behaviors is challenging. >>It is behavior changes is tough, but it's necessary, but it's necessary. It's necessary. And it's urgent. And it's critical, especially if you're going to, uh, improve conditions for vulnerable community. >>What are some of the things that excite you, that looking at where we are now, we've got a nice visibility on equity. There, there's the conscious understanding of the bias and data and the work to help to mitigate that. What are some of the things that excite you about what you're doing and maybe even some of the policies that you think should be enacted as a result of more encompassing datasets? >>It's a good question. Um, one thing I will say is what excites me is it's also tied to the emerging data that we have available. So I'm trying to go back to an example that I gave about measuring constraints. Think that we can now do that in interesting ways, because we're collecting data about everything we're collecting data about, um, not just about where we travel, but how we travel, why we travel. Um, you know, we, we collect information on who we're traveling with, you know, so there's a lot more information that we can make use of, um, in particular to understand constraints. So it's, it's really exciting to me. And when I say that again, um, talking about, um, how would we make a choice to take a certain mode of transportation or to leave our house at a certain time in the morning to, to get to work. >>Um, we're making that under some conditions, right? Right. And those conditions aren't always observed and traditional data sets. I think now we're at a time where emerging data sources can start to capture some of that. And so we can ask questions that we weren't able to, or answer questions that we weren't able to answer before. And the reason why it's important in the modeling is because in the models, you have this sort of choice driven side and you have the alternatives. So you're making a choice amongst some set of alternatives. We model the choices and we spend a lot of time and pay a lot of attention to the decision process. And what factors goes into making the choice, assuming that everyone really has the same set of universal choices. Right. I think that we need to take a little, pay a little more intention, um, to understanding the constraints that people have, um, and how that guides the overall outcomes. Right? So, so that's what I'm excited about. I mean, it's basically leveraging the new data in new ways that we weren't able to before >>Leveraging the data in new ways. Love it. Tierra, thank you for joining me, talking about transportation equity, what you're doing there, the opportunities and kind of where we are on that road. If you will. Thank you so much for having me, my pleasure. I'm Lisa Martin. You're watching the cubes coverage of women in data science conference, 2022. We'll be right back with our next guest.

live from Stanford University it's the cube covering global women and data science conference brought to you by Silicon angle media good morning and welcome to the cube I'm Lisa Martin and we are live at the global fourth annual women in data science conference at the Arriaga Alumni Center at Stanford I'm very pleased to be joined by one of the Wits ambassadors this year Regina cut of our math data science senior manager Accenture at Google and as I mentioned you are an ambassador for wits in Bangla Road the event is Saturday so Janelle welcome to the cube thank you pleasure it is - this is the fourth annual women in data science conference this year over 150 regional events of which you are hosting Bengaluru on Saturday March 9th 50-plus countries they're expecting a hundred thousand people to engage tell us a little bit about how you got to be involved in wins yeah so I care about data science but also what accurate representation of women in gender minority in the space and I think it's global initiative is doing amazing job in creating a significant impact globally and that kind of excited me to get involved with its initiative so you have which I can't believe you're an SME with ten plus years experience and data analytics focusing on marketing and customer analytics you've had senior analytics leadership positions at Accenture Hewlett Packard now Google tell me a little bit about before we get into some of the things that you're doing specifically the data--the on your experience as a female in technology the last ten plus years it's been exciting I started my career as an engineer I wanted to be a doctor fortunately unfortunately it couldn't happen and I ended up being an engineer and it has been an exciting ride since then I felt that had a passion for doing personal management and I posted management and specialization of operational research and project management and I started my career as a data scientist worked my way up through different leadership positions and currently leading a portfolio for Accenture at Google yeah in the read of science domain yeah it's exciting absolutely so one of the things that is happening this year wins 2019 the second annual data thon that's right really looking at predictive analytics challenge for social impact tell us a little bit about why Woods is doing this data thon and what you're doing in not respectively in Bengaluru okay so well you see data science in itself is a highly interdisciplinary domain and it requires people from different disciplines to come together look at the problem from different perspectives to be able to come up with the most amicable and optimal solution at any given point of time and Gareth on is one such avenue that fosters this collaboration and data thon is also an interesting Avenue because it helps young data science enthusiasts whom the require design skill sets and also helps the data science practitioners enhance and sustain their skill sets and that's the reason which Bangalore was keen on supporting what's global data thon initiative so this skill set so I'd like to kind of dig into that a bit because we're very familiar with those required data analytics skill sets from a subject matter expertise perspective but there's other skill sets that we talk about more and more with respect to data science and analytics and that's empathy it's communication negotiation can you talk to us a little bit about how some of those other skills help these data thon participants not just in the actual event but to further their careers absolutely so really into the real world so there are a lot of these challenges wherein you would require a domain expert you require someone who has a coding experience someone who has experience to handle multiple data sites programmatically and also you need someone who has a background of statistics and mathematics so you would need different people to come together I look at the problem and then be able to solve the challenges right so collaboration is extremely pivotal it's extremely important for us to put ourselves in other shoes and see a look at the problem and look at the problem from different perspective and collaboration or the key to be able to be successful in data science domain as such okay so let's get into the specifics about this year's data sets and the teams that were involved in the data thon all right so this year's marathon was focused on using satellite imagery to analyze the scenario of deforestation cost of oil palm plantations so what we did at which Bangalore is we conducted a community workshop because our research indicated that men dominated the Kegel leaderboard not just in Bangla but for India in general despite that region having amazing female leader scientists who are innovators in their space with multiple patents publications and innovations to the credit so we asked few questions to certain female data scientists to understand what could be the potential reason for their lower participation and the Kegel as a platform and their responses led us to these three reasons firstly they may not have the awareness about Kegel as a platform may be a little bit more about that platform so reviewers can understand that right so Kegel is a platform where in a lot of these data sets have been posted if anybody is interested to hold the required a design skill says they can definitely try explore build some codes and submit those schools and the teams that are submitting the codes which are very effective having greater accuracy he would get scored and the jiggle-ator build and you know that which is the most effective solution that can be implemented in the real world so we connected this data Sun workshop and one of the challenges that most of the female leader scientists face is having an environment to network collaborate and come up with a team to be able to attempt a specific data on challenge that is in hand so we connected data from workshop to help participants overcome this challenge and to encourage them to participate into its global hit a fun challenge so what we did as a part of this workshop was we give them on how to navigate Kegel as a platform and we connected an event specifically focused on networking so that participants could network form teams we also conducted a deep in-depth technical session focusing on deep neural nets and specifically on convolutional neural nets the understanding of which was pivotal to be able to solve this year's marathon challenge and the most interesting part of this telethon workshop was a mentorship guidance we were able to line up some amazing mentors and assign these minders to the concern or the interested participating teams and these matters work with respective teams for the next three weeks and for them terms with the required guidance coaching and mentorship held them for the VidCon showed me that's fantastic so over a three-week period how many participants did you have there 110 plus people for the key right yeah for the event and there are multiple teams that have formed and we assigned those mentors we identified seven different mentors and assigned these mentors to the interested participating teams we got a great response in terms of amazing turnout for the event new teams got formed new relationships got initiated new relationships new collaborations all right tell us about those achievements so they were there was one team from engineering branch or engineering division who were really near to the killer's platform they have their engineering exams coming up but despite that they learned a lot of these new concepts they form the team they work together as a team and we were able to submit the code on the Kegel leader board they were not the top scoring team but this entire experience of being able to collaborate look at the problem from different perspective and be able to submit the code despite one of these challenges and also navigate the platforming itself was a decent achievement from my perspective a huge achievement yeah so who you are at Stanford today you're gonna be flying back to go host the event there tell us about from your perspective if we look at the future line of sight for data science let's just take a peek at the momentum this that this Woods movement is generating this is our fourth year covering this fourth annual event fourth year on the cube and we see tremendous tremendous momentum mm-hmm with not just females participating and the woods leaders providing this sustained education throughout the year the podcast for example that they released a few months ago on Google Play on iTunes but also the number of participants worldwide as you look where we are today what in your perspective is the future for data science all right so data science is a domain is evolving at a lightning speed and may possibly hold the solution to almost all the challenges faced by humanity in the near future but to be able to come up with the most amicable and sustainable solution that's more relevant to the domain achieving diversity in this field is most and initiatives like wits help achieve that diversity and foster a real impact absolutely what's original thank you so much for joining me on the cube this morning live from wins 2019 we appreciate that wish you the best of luck kids a local event in Bengaluru over the weekend thank you it was a pleasure likewise thank you we want to thank you you're watching the cube live from Stanford University at the fourth annual woods conference I'm Lisa Martin stick around my next guest will join me in just a moment