WEBVTT 00:00:28.458 --> 00:00:30.458 Next, we have Laura Gonzalez who's going to present building a 00:00:33.329 --> 00:00:39.756 collaborative network to support women of color and us. Laura. Thank you 00:00:39.789 --> 00:00:43.467 so much and um it was so great to hear the previous presentations because 00:00:43.500 --> 00:00:48.237 I see so many connections to the things that our team also found as well. 00:00:48.270 --> 00:00:54.445 Um So I wanna first shout out my uh cose co pis uh Doctor Joy Robertson, 00:00:54.478 --> 00:00:58.217 Doctor Anne Sheri mcnair and Clarissa San Diego, as well as the mentees 00:00:58.250 --> 00:01:02.820 that I'll be briefly introduce you to today. Um If we can go to the next 00:01:02.853 --> 00:01:08.099 slide, please. So we decided to focus on building a collaborative model to 00:01:08.132 --> 00:01:11.869 support women of color through user experience because user experience 00:01:11.902 --> 00:01:16.209 design is inherently such an interdisciplinary field of study that it 00:01:16.242 --> 00:01:20.272 allows pathways for um women of color from different backgrounds, 00:01:20.305 --> 00:01:23.792 backgrounds to make contributions, even if they didn't necessarily come 00:01:23.825 --> 00:01:27.402 from a science and technology background themselves. Um So we developed 00:01:27.435 --> 00:01:32.113 this pilot study to do um to create a sustainable mechanism for 00:01:32.146 --> 00:01:35.813 intervening in the lack of women of color representation in UX research 00:01:35.846 --> 00:01:41.426 and in technology design more broadly, using the pathway of UX design as a 00:01:41.459 --> 00:01:45.486 way to encourage women who may be interested in tech but who may not have 00:01:45.519 --> 00:01:50.837 had the background in the um in technology to um be a part of the tech 00:01:50.870 --> 00:01:55.686 industry and contribute um their, their skills. Our research questions uh 00:01:55.719 --> 00:01:58.876 were grounded in how can women of color from humanity background 00:01:58.909 --> 00:02:03.536 specifically be mentored and encouraged to pursue pur pursue careers in UX 00:02:03.569 --> 00:02:07.536 and technology design. What skills both soft and hard can women of color 00:02:07.569 --> 00:02:10.186 be equipped with in order to enter the technology industry after 00:02:10.219 --> 00:02:14.096 graduation. So we were working with undergraduate and graduate women of 00:02:14.129 --> 00:02:19.186 color and what can the technology industry and UX research specifically 00:02:19.219 --> 00:02:23.136 learn from the skills backgrounds and experiences of women of color in 00:02:23.169 --> 00:02:26.707 order to more effectively diversify the work for the workforce in a 00:02:26.740 --> 00:02:30.416 sustainable way because as we all know, just getting the women of color to 00:02:30.449 --> 00:02:34.406 diversify, the workspace is not necessarily a sustainable model if we can 00:02:34.439 --> 00:02:40.305 go to the next slide, please. Um So this actually started as a one year um 00:02:40.338 --> 00:02:44.286 study, but it's extended into a longitudinal ethnographic study because we 00:02:44.319 --> 00:02:48.196 built such strong relationships with our mentees. We've continued to trace 00:02:48.229 --> 00:02:53.017 and meet regularly and trace their progress. Um We had four international 00:02:53.050 --> 00:02:56.505 Multilingual women, women of color with humanity backgrounds who were all 00:02:56.538 --> 00:03:00.707 very interested in entering the tech industry through UX, but they didn't 00:03:00.740 --> 00:03:05.106 necessarily see the path or have a path for doing so. Uh for the first 00:03:05.139 --> 00:03:10.136 year we held biweekly workshops focused on UES principles and practices. 00:03:10.169 --> 00:03:14.936 And we had several guest uh speakers come in and give lessons on UX and 00:03:14.969 --> 00:03:18.646 also provide feedback to our four mentees. Uh through these conversations 00:03:18.679 --> 00:03:24.096 , our mentees developed individual projects um based on their own 00:03:24.129 --> 00:03:26.606 interests. And I'm happy to talk more in depth about what those projects 00:03:26.639 --> 00:03:29.987 are in the Q and A. But they each developed projects that were related to 00:03:30.020 --> 00:03:33.850 their own interests that also applied the skills we were learning as a 00:03:33.883 --> 00:03:38.481 group in UX through our uh biweekly meetings. Then at the end of the year 00:03:38.514 --> 00:03:44.751 , um we had a mentorship and um networking event with UX professionals and 00:03:44.784 --> 00:03:48.862 industry where our mentees got to share their projects and get feedback. 00:03:48.895 --> 00:03:52.741 And then from there, we've continued to trace the progress. So many uh two 00:03:52.774 --> 00:03:56.052 of our mentees have already graduated and we're continuing to trace how 00:03:56.085 --> 00:04:00.311 has their journey extended beyond the scope of our um initial one year 00:04:00.344 --> 00:04:05.406 mark. We can go to the next slide, please. Some of the methods that we 00:04:05.439 --> 00:04:09.566 used. Um Are we collected written reflections from each mentee throughout 00:04:09.599 --> 00:04:13.196 the one year project after each of the meetings that we had, where they 00:04:13.229 --> 00:04:16.546 were talking about um how they felt about the new skills that they were 00:04:16.579 --> 00:04:20.887 learning about the feedback that they rece received. Um We also engage in 00:04:20.920 --> 00:04:24.575 inter um individual interviews with each mentee throughout the project. So 00:04:24.608 --> 00:04:29.836 kind of see how they are perceiving their role in the tech industry. Um 00:04:29.869 --> 00:04:35.895 throughout the one year program, um we also use some UX methods to as part 00:04:35.928 --> 00:04:40.555 of our own studies. So things like journey mapping where um RMT is traced 00:04:40.588 --> 00:04:43.645 , you know, where they started and when, where they ended and where they 00:04:43.678 --> 00:04:47.515 want to continue going through this collaboration. And something that I 00:04:47.548 --> 00:04:50.717 think was unique and very important to a project was that rather than 00:04:50.750 --> 00:04:56.426 coding the interview transcripts as um a team of P I, we engaged in 00:04:56.459 --> 00:05:00.717 collaborative analysis of all the data. So we read the essays together, we 00:05:00.750 --> 00:05:04.507 talked about the interviews together, we read the transcripts together and 00:05:04.540 --> 00:05:09.265 developed um implications based on our collective analysis of what the 00:05:09.298 --> 00:05:13.717 mentees engaged in rather than just the pis analysis. Um And we think this 00:05:13.750 --> 00:05:17.377 was really, this led us to um really interesting findings and relationship 00:05:17.410 --> 00:05:22.226 building if we can go to the next slide, please. 00:05:22.259 --> 00:05:28.926 So in the end, um the mentees all uh one back please, one slide back. 00:05:28.959 --> 00:05:33.217 Thank you. Um So in the end, all of our mentees successfully completed 00:05:33.250 --> 00:05:37.916 their UX research projects and have publications coming out of this work. 00:05:37.949 --> 00:05:43.726 Um One thing that all our mentees noticed in their um reflections and in 00:05:43.759 --> 00:05:47.296 our analysis sessions was that their language skills because they all 00:05:47.329 --> 00:05:51.055 identified as Multilingual and spoke multiple lang uh different languages. 00:05:51.088 --> 00:05:54.336 They perceived their language skills as something of value to UX research 00:05:54.369 --> 00:05:58.666 , especially as interfaces are designed for Multilingual users and in 00:05:58.699 --> 00:06:03.257 multiple languages and many of the um UX professionals that they network 00:06:03.290 --> 00:06:07.895 with brought up uh mentees language skills as an asset to technology 00:06:07.928 --> 00:06:12.325 design. However, when thinking about seeing themselves in the tech 00:06:12.358 --> 00:06:16.127 industry, the mentees did have a lot of hesitancies about things like 00:06:16.160 --> 00:06:20.805 stigmas associated with their accents and whether or not their um language 00:06:20.838 --> 00:06:24.955 skills would be stigmatized in the workplace. Um And this is where I saw 00:06:24.988 --> 00:06:28.137 some connections to the previous presentations as well, right? What is 00:06:28.170 --> 00:06:32.377 this inclusive uh work environment look like for women of color, even 00:06:32.410 --> 00:06:35.495 though their skills might be very valued? Yes, we want to design 00:06:35.528 --> 00:06:39.637 Multilingual interfaces. But do we want Multilingual people working in 00:06:39.670 --> 00:06:43.026 these environments um outside of the norms of standardized written English 00:06:43.059 --> 00:06:49.616 ? And that was a hesitation for several of our participants. Um Another 00:06:49.649 --> 00:06:54.437 thing that uh we found from the reflections is that once our mentees 00:06:54.470 --> 00:06:59.906 started applying for tech industry jobs, a lot of the language used on 00:06:59.939 --> 00:07:04.015 these um job applications in the tech industry were perceived as a as a 00:07:04.048 --> 00:07:10.205 barrier for our mentees. So especially in relation to visa concerns. So a 00:07:10.238 --> 00:07:14.145 lot of the job applications were not clear whether visa sponsorship would 00:07:14.178 --> 00:07:18.387 be provided, whether uh applicants needed to be authorized to work in the 00:07:18.420 --> 00:07:23.717 US before they applied. And if this was not explicitly stated um in the 00:07:23.750 --> 00:07:29.666 job application, then the mentees kind of um assumed that they would not 00:07:29.699 --> 00:07:33.387 be eligible for these jobs. And so we see this as a barrier to building 00:07:33.420 --> 00:07:38.846 inclusivity in the tech industry. Um Same thing with jobs that do not 00:07:38.879 --> 00:07:44.765 necessarily call for skills um related to engineering, often required 00:07:44.798 --> 00:07:47.887 engineering backgrounds or engineering degrees as just like template 00:07:47.920 --> 00:07:52.616 requirements. And so one of the things we want to do is continue to work 00:07:52.649 --> 00:07:56.286 with industry professionals and tech companies to interrogate why these 00:07:56.319 --> 00:08:00.921 requirements are in place. Um If there are ways to kind of ex and how the 00:08:00.954 --> 00:08:04.520 wording about the engineering background requirement might be placed on 00:08:04.553 --> 00:08:08.491 job applications to leave it open for women of color, from humanity, 00:08:08.524 --> 00:08:12.370 disciplines who have experience but not necessarily engineering degrees to 00:08:12.403 --> 00:08:18.963 still feel like they can apply. Um If we can go to the next slide, please. 00:08:20.129 --> 00:08:22.129 Um So some of the things that we're doing for implications are next steps. 00:08:25.329 --> 00:08:30.455 Uh One thing through the guidance um of our mentors uh Doctor Scott, um 00:08:30.488 --> 00:08:36.066 we're working for um to build policy implications related to this because 00:08:36.099 --> 00:08:39.625 we saw that there are some potential implications for how we can 00:08:39.658 --> 00:08:45.346 collaborate again with employers to establish some policy for the clarity 00:08:45.379 --> 00:08:50.226 that needs to be um the clarity with which job applications need to be 00:08:50.259 --> 00:08:53.967 written. So that women of color and women of color from international 00:08:54.000 --> 00:08:57.635 backgrounds feel like they are actually being recruited and are welcome to 00:08:57.668 --> 00:09:01.547 apply to these jobs. You know, as research shows a lot of times white men 00:09:01.580 --> 00:09:04.436 might feel like they are of course qualified to apply. But for women of 00:09:04.469 --> 00:09:07.885 color, these barriers are already systematically in place. And so when we 00:09:07.918 --> 00:09:12.456 are not clear about what we're actually requiring for these jobs, um there 00:09:12.489 --> 00:09:16.976 are the, there are participants at least didn't feel as compelled to apply 00:09:17.009 --> 00:09:21.807 even though they are very much eligible for the positions. Um We also want 00:09:21.840 --> 00:09:25.616 to continue, like I said, building connections between women of color in 00:09:25.649 --> 00:09:28.797 the tech industry, with women of color in the tech industry at different 00:09:28.830 --> 00:09:33.635 stages of their careers. So many of the women that we met with um were 00:09:33.668 --> 00:09:36.856 women of color, but they were more advanced in the tech industry. And our 00:09:36.889 --> 00:09:39.571 mentees mentioned that they'd like to meet people at different stages. So 00:09:39.604 --> 00:09:43.191 people who are just starting um people who are kind of mid career and then 00:09:43.224 --> 00:09:46.052 people who are more senior in the tech industry so they can get a wide 00:09:46.085 --> 00:09:50.811 range of experience. And also we're just looking to build a be a bigger 00:09:50.844 --> 00:09:54.412 network of mentees where our previous mentees can now become, you know, 00:09:54.445 --> 00:09:59.375 the mentors for more junior women of color interested in this area. Thank 00:09:59.408 --> 00:10:03.566 you. I think that was it. 00:10:03.599 --> 00:10:09.297 Thank you so much, Maura. Um to hear about nontraditional pathways and the 00:10:09.330 --> 00:10:15.346 implications on transforming recruitment strategies is so important to all 00:10:15.379 --> 00:10:22.246 of our work. So, thank you for that. Next. Uh We have Rati Kawawa who will 00:10:22.279 --> 00:10:28.907 be presenting the Leadership Academy for Women of color in tech rati. 00:10:28.940 --> 00:10:35.667 Thank you, Kimberly. So, um let me just explain first why Leadership 00:10:35.700 --> 00:10:42.135 Academy. Um I spent 39 years in tech and as an individual, I succeeded, I 00:10:42.168 --> 00:10:47.206 had a phd in computer science and the first one in the world to have one, 00:10:47.239 --> 00:10:51.606 a woman of color to have a phd. And within a few years and then I was the 00:10:51.639 --> 00:10:57.476 last 17 years, I was a vice president at the lab. So I succeeded. But I 00:10:57.509 --> 00:11:01.407 had a second role, which was I was a member of the senior leadership team 00:11:01.440 --> 00:11:07.616 of my company. I miserably failed in that role as it, as it relates to 00:11:07.649 --> 00:11:12.645 diversity and inclusion. I succeeded in other ways, but I could not change 00:11:12.678 --> 00:11:19.696 the culture beyond my own organization. So I thought about this problem 00:11:19.729 --> 00:11:26.667 that why was it so hard? And my uh conclusion is that diversity and 00:11:26.700 --> 00:11:33.057 inclusion in a tech company is a very complex system problem and they are 00:11:33.090 --> 00:11:38.566 not treating it as a complex system problem. Tech companies are used to 00:11:38.599 --> 00:11:42.287 dealing with complex problems. They put a man on the moon, they do all 00:11:42.320 --> 00:11:46.746 kinds of things, but they have not taken this diversity and inclusion 00:11:46.779 --> 00:11:52.145 problem to be a system problem. They look at it as an incremental problem. 00:11:52.178 --> 00:11:56.696 So what I said is even they look at it as a system problem. They take the 00:11:56.729 --> 00:12:01.316 hardest problem that will prevent that spaceship, say from taking off and 00:12:01.349 --> 00:12:06.255 work on the hardest problem first with the most resources. So that, that 00:12:06.288 --> 00:12:11.037 doesn't prevent the launch of the spacecraft five years later. But for 00:12:11.070 --> 00:12:16.576 women of color, which is the hardest problem in diversity in tech, they 00:12:16.609 --> 00:12:19.895 haven't started working on it. They worked on the women problem, which was 00:12:19.928 --> 00:12:24.736 the white women problem when that gets solved. Asian women problem, then 00:12:24.769 --> 00:12:29.057 underrepresented minorities. So they have not treated it as a system 00:12:29.090 --> 00:12:33.875 problem to be solved. So my conclusion was I need to work on women of 00:12:33.908 --> 00:12:40.316 color in tech. Then I could work on management or I can work on helping 00:12:40.349 --> 00:12:44.967 women of color succeed. And the reason I have chosen to work on Leadership 00:12:45.000 --> 00:12:49.667 Academy for women of color is I believe I do not have the right solutions 00:12:49.700 --> 00:12:54.066 yet to walk into a boardroom and say do these seven things and I guarantee 00:12:54.099 --> 00:12:58.467 you you're going to succeed. It is the solutions are not strong enough yet 00:12:58.500 --> 00:13:03.246 for the tech mindset of those leaders. So with teaching the women of color 00:13:03.279 --> 00:13:06.875 in tech, I'm going to be developing the stories, the women of color 00:13:06.908 --> 00:13:10.635 stories and the assets of what makes them succeed. And all the work you 00:13:10.668 --> 00:13:15.407 guys are doing the solutions then will be like possible, more possible 00:13:15.440 --> 00:13:19.326 that they will work at the executive level. So that's why I'm starting 00:13:19.359 --> 00:13:23.427 with this women of color teaching them. But building the assets that then 00:13:23.460 --> 00:13:27.927 go direct to the managers in man in changing the culture. So let's go to 00:13:27.960 --> 00:13:34.375 the next slide, please. So the next slide becomes, how do we accelerate 00:13:34.408 --> 00:13:38.287 the women of color to positions of inf influence, influence in tech firms 00:13:38.320 --> 00:13:43.667 ? I'll go through it very fast in 10 seconds is basically the first rung 00:13:43.700 --> 00:13:47.486 of the ladder as you know, is broken. And you also know that for 00:13:47.519 --> 00:13:52.297 minorities and women, if you don't get noticed in the first stage of your 00:13:52.330 --> 00:13:57.866 career, forget it. You're gonna plateau early. And that is not true for 00:13:57.899 --> 00:14:02.375 white males, they can plateau at 1st, 2nd and 3rd. But it has been shown 00:14:02.408 --> 00:14:05.576 that the more different you are from the norm, the earlier, you have to 00:14:05.609 --> 00:14:11.226 get notice for the right reasons. And then leadership is the number one 00:14:11.259 --> 00:14:16.255 gap between perceptions of employers and perceptions of new graduates and 00:14:16.288 --> 00:14:21.217 what is missing when you turn up at the workplace. So therefore, I decided 00:14:21.250 --> 00:14:25.657 leadership development is what, what I was gonna focus on. And the 00:14:25.690 --> 00:14:31.037 question is institutions, educational institutions are not preparing our 00:14:31.070 --> 00:14:36.145 women of color for the non level playing field. They have to prepare them 00:14:36.178 --> 00:14:40.895 more in a way so that they face the same degree of challenge when they get 00:14:40.928 --> 00:14:44.996 to the workplace, but that's not what happened. So therefore, there's a 00:14:45.029 --> 00:14:48.797 lot of work to be done on the women. So my Harvard research first 00:14:48.830 --> 00:14:53.525 identified 19 levers for what it takes to thrive versus just survive. And 00:14:53.558 --> 00:14:56.596 there's a big difference between planning to thrive and planning to 00:14:56.629 --> 00:15:01.047 survive. And the next thing was the grant that I was able to do the 00:15:01.080 --> 00:15:05.356 interviews with women of color and extract the stories. And then I'm doing 00:15:05.389 --> 00:15:10.505 api did a pilot and this I believe will double the speed of early career 00:15:10.538 --> 00:15:14.946 advancement. And then once these women of color get into first level 00:15:14.979 --> 00:15:20.005 management positions, they will then amplify the work and nudge the 00:15:20.038 --> 00:15:24.407 processes that are not the formal processes that create the culture change. 00:15:24.440 --> 00:15:27.635 That's how when I was a white, when we were white and Asian women, we 00:15:27.668 --> 00:15:32.586 created the change is through our informal nudging of middle level 00:15:32.619 --> 00:15:37.976 processes. So let's go to the next slide, please. Um uh The next slide 00:15:38.009 --> 00:15:42.366 just shows you and I won't go through it. Is I've done this research and 00:15:42.399 --> 00:15:47.436 then I also checked my results validated them that there are only 19 00:15:47.469 --> 00:15:51.496 things that need to be taught in the area of non technical skills, 00:15:51.529 --> 00:15:56.101 leadership development, which make the whole difference in early career. 00:15:56.134 --> 00:15:59.741 I'm focusing on early career because if you, you don't get beyond that, 00:15:59.774 --> 00:16:03.422 you do, you just don't even expect to have a chance to go beyond that. So 00:16:03.455 --> 00:16:06.321 we won't go through this, but I'll illustrate some of them in the next 00:16:06.354 --> 00:16:10.846 slide. So let's go to the next slide and this is where I'm gonna spend 00:16:10.879 --> 00:16:15.476 most of my time. So this was the project of collecting 100 women of color 00:16:15.509 --> 00:16:20.316 stories. And so let me just start with Cammy, who has worked for a major 00:16:20.349 --> 00:16:26.307 company in Silicon Valley. And, uh, she basically has every time she has a 00:16:26.340 --> 00:16:30.537 new manager, in spite of having two degrees in computer science and 00:16:30.570 --> 00:16:35.005 engineering keeps getting told occasionally you are not technical enough 00:16:35.038 --> 00:16:41.356 and she did a lot of things. She ignored it for a while, the feedback. And 00:16:41.389 --> 00:16:44.667 then finally when she took action, she had, she removed her earrings, she 00:16:44.700 --> 00:16:49.686 removed her jewelry, she started wearing uh t-shirts and she started 00:16:49.719 --> 00:16:54.525 wearing glasses without lenses, all the stuff that she had to do in order 00:16:54.558 --> 00:16:58.196 to just perception change the, manage the perception, since the situation 00:16:58.229 --> 00:17:04.467 , manage the perception and yet remain authentic to herself. So her story 00:17:04.500 --> 00:17:09.996 is very, very interesting how she managed to remain totally authentic. But 00:17:10.029 --> 00:17:14.555 adapted. The problem is women of color don't have role models in their own 00:17:14.588 --> 00:17:17.847 workplaces. So it took her 15 years to figure things out which she could 00:17:17.880 --> 00:17:21.887 have figured out in two years if she had had lots of exposure to those 00:17:21.920 --> 00:17:26.915 people. Similarly. So this is a very interesting story of how she then 00:17:26.948 --> 00:17:30.595 changed that whole perception. And now she's a master at it. Now, she 00:17:30.628 --> 00:17:34.274 writes technical memos even though she shouldn't have to. So it's a matter 00:17:34.307 --> 00:17:39.597 of deciding what her strategy is. It's not right, but it's real. And that 00:17:39.630 --> 00:17:44.085 is what we teach. These are the kinds of stories and so can he comes into 00:17:44.118 --> 00:17:48.217 the classroom at the leadership academy and spends time with the students 00:17:48.250 --> 00:17:51.555 and she's a very big leader in Silicon Valley. So, what she does and she 00:17:51.588 --> 00:17:55.535 has two daughters and she's, they're both going for masters in computer 00:17:55.568 --> 00:17:59.776 science as well. So now we go to Deb, who has a slightly different issue, 00:17:59.809 --> 00:18:04.107 which is about a growth mindset around personal change. And she's also a 00:18:04.140 --> 00:18:08.117 leader in Silicon Valley and she, I was one of the people I interviewed 00:18:08.150 --> 00:18:13.266 and her thing was typical of many Asians. You're very introverted and you 00:18:13.299 --> 00:18:18.226 think that's a personality trait that you have, it's not, you can, you can 00:18:18.259 --> 00:18:22.785 act like an extrovert. And she says it's no more difficult than learning 00:18:22.818 --> 00:18:28.717 Spanish and she proves it. And now she is in Vegas reports to Zuckerberg. 00:18:28.750 --> 00:18:33.166 So you know what I'm trying to tell you is these are stories Nora May is a 00:18:33.199 --> 00:18:39.436 Hispanic woman who connects with her, her main strength, which is that she 00:18:39.469 --> 00:18:44.226 knows how to ask. She went to MIT she was a single mom. She learned how to 00:18:44.259 --> 00:18:49.166 ask for everything. Then she used that skill every time and got promoted 00:18:49.199 --> 00:18:53.416 five times in six years at going. So her story says, whatever your 00:18:53.449 --> 00:18:57.107 strength is, whether it's resilience or asking how can you use that 00:18:57.140 --> 00:19:01.512 strength to get going? The new company really fast. And then Mike, I had 00:19:01.545 --> 00:19:06.891 him up there because I really want to tell you that sometimes I use like a 00:19:06.924 --> 00:19:11.831 Hispanic male. Mike is a Hispanic male from a Fortune 100 company. And he 00:19:11.864 --> 00:19:17.357 comes and we ask him who's the best intern you ever had? And then he tells 00:19:17.390 --> 00:19:21.766 the story of Jay who is the best intern he ever had. And then he says in 00:19:21.799 --> 00:19:27.426 the talk about yourself first meeting with my intern, how that in turn 00:19:27.459 --> 00:19:33.016 impressed the hell out of Mike and then shape the assignment that the 00:19:33.049 --> 00:19:37.266 attorn got. So you can shape your early assignments if you're smart about 00:19:37.299 --> 00:19:41.585 how you reflect your, your strength to your manager. So it's all about 00:19:41.618 --> 00:19:47.857 solutions. The mini case methods that we are developing really allow the 00:19:47.890 --> 00:19:53.996 women in the class to look at the range where they want to operate and 00:19:54.029 --> 00:19:59.565 many of them shift where they operate within a six week time frame. That 00:19:59.598 --> 00:20:05.065 is the critical part. So let's go to the next slide. So what we did is we 00:20:05.098 --> 00:20:11.325 actually did a summer academy with 54 students and Zoom and we did it 00:20:11.358 --> 00:20:14.877 because summer internships were canceled and people were calling us. And 00:20:14.910 --> 00:20:19.897 so we said let's do a summer academy. And so we did it on Zoom completely 00:20:19.930 --> 00:20:25.085 and look at the results up there, the results and we had a control group 00:20:25.118 --> 00:20:30.006 and we had these people whom we, we took half the people that had applied 00:20:30.039 --> 00:20:34.335 and they are reporting less stress and anxiety, more confidence in their 00:20:34.368 --> 00:20:38.857 negotiation skills, greater. They're gonna do other leadership skills, 00:20:38.890 --> 00:20:42.476 enhanced perception. So the this is relative to a control, 100 and nine 00:20:42.509 --> 00:20:46.055 people applied, we took half of them and the other are a control group and 00:20:46.088 --> 00:20:51.117 this is before and after six weeks. So the the partnership to do this was 00:20:51.150 --> 00:20:55.506 formed between University of Massachusetts, Harvard Kennedy School and 00:20:55.539 --> 00:21:02.597 myself and the test, uh pilot test was passed. The students came, 54 came 00:21:02.630 --> 00:21:08.526 , 53 stayed. So they and they learned and all of this was done in a 00:21:08.559 --> 00:21:13.916 virtual setting. And these assets that we have done that we have created 00:21:13.949 --> 00:21:18.976 two assets, that framework that I showed you with all the 19 levers. And 00:21:19.009 --> 00:21:22.936 the second is the story, we have shown that within four weeks, we were 00:21:22.969 --> 00:21:26.976 able to create a six week curriculum and do it and deliver it because of 00:21:27.009 --> 00:21:31.217 the pandemic. And that is why I feel that this asset based model is going 00:21:31.250 --> 00:21:36.647 to succeed. Let's keep going. Do I have one or two minutes now? 00:21:36.680 --> 00:21:42.627 Next steps? So one more minute, rossi one more minute. So I want to engage 00:21:42.660 --> 00:21:46.176 with engage educational institutions because there are three things they 00:21:46.209 --> 00:21:50.196 are not doing right. They're advising women of color the same way as they 00:21:50.229 --> 00:21:54.016 would advise a white man. And I want to tell you what those three, I don't 00:21:54.049 --> 00:21:58.426 have time to tell you what they are. Then there are hypotheses. I need 00:21:58.459 --> 00:22:03.055 that need to be validated through proper research. But I have gotten that 00:22:03.088 --> 00:22:08.426 from my work and I want to convert the pilot material so that everyone is 00:22:08.459 --> 00:22:13.006 going to be open source. Anybody that wants it can use it anywhere in the 00:22:13.039 --> 00:22:17.387 and then I want to do the other things and then engage with tech form. So 00:22:17.420 --> 00:22:21.805 last slide is just a thank you. Slide. Let's go to the last slide and the 00:22:21.838 --> 00:22:25.555 grant has given me the credibility. I always had credibility personally 00:22:25.588 --> 00:22:29.825 for having succeeded in tech. But not that I know anything about how I 00:22:29.858 --> 00:22:34.906 could teach Black Hispanic women about anything. Nobody would trust me to 00:22:34.939 --> 00:22:39.285 do that. Black computer was the first place that gave me a platform to 00:22:39.318 --> 00:22:43.805 teach some of my ideas. And I'm really glad I got this grant to do this. 00:22:43.838 --> 00:22:47.526 So and then the budget gave me the the budget to hire one teaching fellow. 00:22:47.559 --> 00:22:52.315 So I really appreciate the support. This is my dream with or without walk. 00:22:52.348 --> 00:22:55.956 It's my dream. This is going to happen and we already did that this 00:22:55.989 --> 00:22:58.526 summer. We're going to do it again next summer and now we're gonna scale 00:22:58.559 --> 00:23:04.196 it nationally. That is what our plan is. Thank you. Thank you so much Roy 00:23:04.229 --> 00:23:08.746 and particularly for your generous offer to make your curriculum um open 00:23:08.779 --> 00:23:16.055 access. Um Let's move on next. We have Joan C Williams who will be talking 00:23:16.088 --> 00:23:23.717 about workplace experiences of women of color and tech. Joan. Um Many 00:23:23.750 --> 00:23:27.545 thanks for the consortium for all the wonderful support they've given me. 00:23:27.578 --> 00:23:32.315 Um It's just been a really um a dream of mine to be able to do another 00:23:32.348 --> 00:23:40.348 study um in this arena. Um So, uh next slide, 00:23:40.809 --> 00:23:45.815 um we believe that what we have done with the consortium support is the 00:23:45.848 --> 00:23:51.186 largest quantitative study of women of color and tech. We did 12 inter in 00:23:51.219 --> 00:23:56.717 depth interviews. Uh We did a quantitative survey with 215 participants, 00:23:56.750 --> 00:24:04.325 100 and 89 women of color. And you can see that 65% of the people uh who 00:24:04.358 --> 00:24:11.446 responded were individual contributors. 27% were managers and about 8%. 00:24:11.479 --> 00:24:15.835 Other next slide. 00:24:15.868 --> 00:24:21.847 Um What I have done is taken this huge literature on racial and gender 00:24:21.880 --> 00:24:26.906 bias from uh experimental social psychology, industrial organizational 00:24:26.939 --> 00:24:32.627 psychology, um and individual uh uh and behavioral economics and 00:24:32.660 --> 00:24:38.170 integrated into five basic patterns of workplace bias in professional 00:24:38.203 --> 00:24:43.920 workplaces. Um The first pattern which is triggered both by race and by 00:24:43.953 --> 00:24:48.831 gender as well as other vectors of status is that some groups have to 00:24:48.864 --> 00:24:53.361 prove themselves more than others, which means that some groups literally 00:24:53.394 --> 00:24:59.206 have to be more competent than uh others in order to succeed. So that's 00:24:59.239 --> 00:25:03.217 the prove it again pattern, technical name, descriptive stereotyping, um 00:25:03.250 --> 00:25:09.647 leniency bias, um attribution bias and other uh other names. The second is 00:25:09.680 --> 00:25:13.906 what I call the tight rope is that some groups have to be savvier than 00:25:13.939 --> 00:25:19.506 others in order to succeed. The easiest way to explain this is that for 00:25:19.539 --> 00:25:23.325 white men in professional workplaces, they just need to be authoritative 00:25:23.358 --> 00:25:28.496 and ambitious. But every other group needs to be authoritative and 00:25:28.529 --> 00:25:33.607 ambitious in a way that's acceptable to the white men in their environment. 00:25:33.640 --> 00:25:39.325 And that means that there's often a ding for assertive behavior. Um And 00:25:39.358 --> 00:25:45.006 that groups other than white men, sometimes elite white men. Uh um it's 00:25:45.039 --> 00:25:49.686 even narrower than white men, but uh usually it's white men. Uh uh So 00:25:49.719 --> 00:25:56.236 every other group is pressured to behave in deferential ways and also play 00:25:56.269 --> 00:26:01.666 subordinate roles which really influences their access to high quality 00:26:01.699 --> 00:26:07.357 assignments. Um The third is that and they uh by the way and that they're 00:26:07.390 --> 00:26:12.597 also often um faulted for personality problems. Um The third is um 00:26:12.630 --> 00:26:18.756 maternal wall. This is the strongest trigger of gender bias. Um It's that 00:26:18.789 --> 00:26:23.496 um basically motherhood triggers very strong negative competence and 00:26:23.529 --> 00:26:29.456 commitment assumptions. The fourth pattern I call the tug of war and 00:26:29.489 --> 00:26:33.617 that's really the in group favoritism works for some but not for others. 00:26:33.650 --> 00:26:40.476 Because often gender bias against women, feels conflicts among women and 00:26:40.509 --> 00:26:46.805 racial bias sometimes feels conflicts among um people of color as well to 00:26:46.838 --> 00:26:52.147 white, not white enough. And then the fifth pattern is racial stereotypes 00:26:52.180 --> 00:26:56.097 that are not picked up by the other four patterns. A lot of racial 00:26:56.130 --> 00:27:00.107 stereotypes are picked up by proved again, tight, open tug of war, but 00:27:00.140 --> 00:27:04.416 there's some elements that aren't particularly relevant in tech is that a 00:27:04.449 --> 00:27:09.367 people of Asian descent are seen as um good for technical jobs but not 00:27:09.400 --> 00:27:13.756 leadership and other people of color are seen as not technical enough as 00:27:13.789 --> 00:27:18.926 we've heard. And for um black professionals in this as in so many other 00:27:18.959 --> 00:27:23.075 professional workplaces, their experience really stands out because of the 00:27:23.108 --> 00:27:28.516 startling levels of disrespect that they often encounter now who reports 00:27:28.549 --> 00:27:35.156 the highest levels of bias. Um in general, a East Asian women in our 00:27:35.189 --> 00:27:40.246 sample reported the highest average levels of bias. This may have been 00:27:40.279 --> 00:27:45.607 because we were focused on 2010 when President um Trump was going around 00:27:45.640 --> 00:27:49.776 saying things like uh that, that he was angry about the quote, the Chinese 00:27:49.809 --> 00:27:55.075 virus. Next slide. 00:27:55.108 --> 00:28:00.986 Here's an example of prove it again bias. We're always looked at as why 00:28:01.019 --> 00:28:05.756 did you deserve it? Not because I have the brain power to support me being 00:28:05.789 --> 00:28:10.467 in the role. It doesn't matter how well I perform. I feel like I have to 00:28:10.500 --> 00:28:16.377 go above and beyond. This is a pattern that we have found again and again 00:28:16.410 --> 00:28:21.815 , we studied six different industries. Um african-american women always 00:28:21.848 --> 00:28:26.236 report the highest levels. Uh Well, women of color always report the 00:28:26.269 --> 00:28:31.285 highest levels of prove it again bias. And african-american women among 00:28:31.318 --> 00:28:35.906 them typically report the highest levels of prove it again bias because 00:28:35.939 --> 00:28:39.936 they're triggering two sets of negative competence assumptions, one based 00:28:39.969 --> 00:28:45.276 on gender and one based on race. Next slide. 00:28:45.309 --> 00:28:51.597 Um So here for tight rope. Um What you see here is like Lyr scale. So 00:28:51.630 --> 00:28:56.085 these uh lyre scale measures the strength of bias from not at all to very 00:28:56.118 --> 00:29:01.127 strong. So you can see that, that the uh East Asian women report the 00:29:01.160 --> 00:29:06.647 highest levels of pushback for assertive behavior. Um All other women are 00:29:06.680 --> 00:29:10.728 in between and what white women report the lowest levels. Although in 00:29:10.761 --> 00:29:15.518 study after study, we have reported, um white white women report sharply 00:29:15.551 --> 00:29:19.949 very sharply lower levels than white men. But you can see that there is a 00:29:19.982 --> 00:29:25.619 um both a gender and a racial hierarchy as is so common in these patterns. 00:29:25.652 --> 00:29:32.562 Then the next is with maternal wall. Um uh My ne my confidence and 00:29:32.595 --> 00:29:37.342 commitment were questioned after I had Children. Um You can see that uh 00:29:37.375 --> 00:29:43.062 Latinx women reported the strongest bias among this. There's a very strong 00:29:43.095 --> 00:29:46.993 , they very consistently report the strong, the strongest maternal law 00:29:47.026 --> 00:29:52.753 bias. Um And then white women on the other side and all other women in 00:29:52.786 --> 00:29:59.956 between. Uh next slide, maternal well bias um includes both um having your 00:29:59.989 --> 00:30:04.016 confidence and que and commitment questioned after you had Children. But 00:30:04.049 --> 00:30:08.315 also if your confidence and commitment are indisputable, for example, if 00:30:08.348 --> 00:30:13.397 you work long hours, then um you're deemed for that too, you trigger 00:30:13.430 --> 00:30:17.516 dislike. She's a bad mother. So she's probably a bad person and also 00:30:17.549 --> 00:30:20.367 you're held to higher performance standards. So it's kind of damned if you 00:30:20.400 --> 00:30:26.085 do. Damned if you don't. Um, anger is another tight rope pattern because 00:30:26.118 --> 00:30:31.397 again, white men have a lot more scope in many workplaces to show anger 00:30:31.430 --> 00:30:36.857 than any other group. Here's a native, uh, Alaskan native woman. When I 00:30:36.890 --> 00:30:41.666 have a very strong opinion about something I take special care includes in 00:30:41.699 --> 00:30:46.347 choosing my words. If something has made me angry in a meeting, I take it 00:30:46.380 --> 00:30:50.627 out of the meeting, write down my anger, give it a day or two and then I 00:30:50.660 --> 00:30:55.006 try to address it later when I'm feeling less emotionally attached to that 00:30:55.039 --> 00:31:00.127 issue. So this picks on something up that Rocky said, I mean, uh I wrote a 00:31:00.160 --> 00:31:04.906 book in 2014 called What Works For Women at work in which I interviewed 00:31:04.939 --> 00:31:09.785 100 and 27 wise women and asked them, how do you navigate successfully 00:31:09.818 --> 00:31:15.476 through gender bias? And um so one of the, and what I found basic bottom 00:31:15.509 --> 00:31:18.897 line is, you know, women can do anything backwards and heels, but they 00:31:18.930 --> 00:31:22.986 have to be twice as good. Um And not only just in terms of their technical 00:31:23.019 --> 00:31:27.285 skills, that's to prove it again, they have to be twice as good in terms 00:31:27.318 --> 00:31:31.976 of um human relations. And you see all of this uh background level of 00:31:32.009 --> 00:31:36.325 politically, political savvy and the extra emotion work that this native 00:31:36.358 --> 00:31:42.897 Alaskan woman has to do to be successful in her workplace. Next slide. 00:31:42.930 --> 00:31:49.176 Um Women also have far um higher levels of uh the office housework um that 00:31:49.209 --> 00:31:53.357 one woman said I've constant, I'm constantly slotted into admin roles even 00:31:53.390 --> 00:31:56.795 though I have a master's degree and have been physically looked past so 00:31:56.828 --> 00:32:02.496 many times. Um Women of all races get pressure to do more administrative 00:32:02.529 --> 00:32:07.825 housework and behind the scenes work. Uh And um women of all races and men 00:32:07.858 --> 00:32:11.926 of color get less access to the glamour work. Another very strong tight 00:32:11.959 --> 00:32:15.565 rope pattern. We see again and again, we saw it in the status set as well. 00:32:15.598 --> 00:32:20.516 Next slide, some evidence is that Latinx women have the highest levels. 00:32:20.549 --> 00:32:26.696 Um talk of war bias. Um This is conflict within um uh uh a racial or 00:32:26.729 --> 00:32:31.867 gender group. You can see South Asian women were by far the tall, the 00:32:31.900 --> 00:32:36.516 highest we aggregated racial and gender bias. This uh we have persistent 00:32:36.549 --> 00:32:42.305 reports of um conflict with. So with um uh South Asian men that may be 00:32:42.338 --> 00:32:46.357 what's going on here and you see the same pattern and then the racial 00:32:46.390 --> 00:32:51.347 pattern of being disrespected or demeaned at work again, as in many other 00:32:51.380 --> 00:32:56.236 data sets, black women report the highest levels. And then we see the same 00:32:56.269 --> 00:33:02.075 distribution among different groups of women. Next slide. 00:33:02.108 --> 00:33:08.075 Um uh uh This is there, there is this sort of concept of isolationism that 00:33:08.108 --> 00:33:11.186 happens when you're the only one who looks like you since you're the only 00:33:11.219 --> 00:33:15.357 Latina um that you have to represent your whole group. So anything you do 00:33:15.390 --> 00:33:18.956 , if it's going badly, everyone in the room is going to think that Latina 00:33:18.989 --> 00:33:22.726 engineers uh of engineers and uh Latina engineers in a bad way in the 00:33:22.759 --> 00:33:27.976 future. Um That's uh that's a very typical example uh of tug of War next 00:33:28.009 --> 00:33:30.397 line. 00:33:30.430 --> 00:33:35.867 Um I'll just end with a few examples of the bias interrupters because as 00:33:35.900 --> 00:33:41.746 Rotten pointed out um uh to address systemic racism and sexism, you need 00:33:41.779 --> 00:33:45.887 to change systems and you need to change it with systems thinking. And so 00:33:45.920 --> 00:33:50.467 we have developed biased interrupters and I'll just end with a couple of 00:33:50.500 --> 00:33:56.719 examples. We um I wrote a 2.5 document that um integrated racial and 00:33:56.752 --> 00:34:01.590 gender bias and showed and described how it typically takes uh uh plays 00:34:01.623 --> 00:34:05.788 out in performance evaluations and just giving people that document and 00:34:05.821 --> 00:34:11.320 reading them through. It led to sharply higher bonuses and statistically 00:34:11.353 --> 00:34:16.119 significantly higher performance evaluations for black women and black men 00:34:16.152 --> 00:34:20.892 and also white women. And then finally, um and a bias interrupter, I'm 00:34:20.925 --> 00:34:28.173 gonna be proposing in our report um uh on women of color in tech is that 00:34:28.206 --> 00:34:34.552 um women of color um really often get caught doing a lot of diversity work 00:34:34.585 --> 00:34:37.872 for which they are not paid and which they get no credit for and they get 00:34:37.905 --> 00:34:42.736 no administrative support for and they want to continue to do the work. 00:34:42.769 --> 00:34:46.836 But what I have vetted with them, the idea that if they are asked to do 00:34:46.869 --> 00:34:51.615 that work, they should be provided with adequate um administrative support 00:34:51.648 --> 00:34:55.615 so that all they have to do is just find the speakers and then somebody 00:34:55.648 --> 00:34:59.135 else in the org does everything else. That's a good example of an 00:34:59.168 --> 00:35:03.256 organizational bias interrupter. Many, many thanks. I appreciate the 00:35:03.289 --> 00:35:05.756 opportunity. 00:35:05.789 --> 00:35:12.106 Thank you, Joan. I love the bias interrupters and um hearing how it can 00:35:12.139 --> 00:35:18.086 make the systemic change that we all know needs to occur. Next, we have 00:35:18.119 --> 00:35:23.646 Heather Metcalf, Aaron Kelly and Aspen Russell, their presentation stem to 00:35:23.679 --> 00:35:28.936 market conscious commercialization over to you. Thank you so much, Kim. 00:35:28.969 --> 00:35:32.675 And I wanna just extend the gratitude. Every other presenter has to um all 00:35:32.708 --> 00:35:35.376 the folks that are there. I'm Aspen Russell. We'll be presenting on behalf 00:35:35.409 --> 00:35:39.967 of Erin and Heather and we can hit the next slide. 00:35:40.000 --> 00:35:44.727 All right. So uh the work that uh Kr and A su helped us do is built on a 00:35:44.760 --> 00:35:48.706 foundation of work we had done a couple of years prior. Um So we had a 00:35:48.739 --> 00:35:51.807 couple, we had an accelerator for stem trained women and we also were 00:35:51.840 --> 00:35:54.577 doing what's called intentional investing where we were targeting 00:35:54.610 --> 00:35:57.967 investors and doing biased trainings. Um So we could learn from the women 00:35:58.000 --> 00:36:01.307 in Stem and the Accelerator Program and translate that knowledge onto the 00:36:01.340 --> 00:36:05.276 investors. Uh These are a couple of our findings um for that, it's not a 00:36:05.309 --> 00:36:08.517 pipeline problem. Women of color, folks of color and entrepreneurship are 00:36:08.550 --> 00:36:12.546 there. It is the active avoidance and you know, canceling out of these 00:36:12.579 --> 00:36:15.736 people existing, which is why the funding is not going there. Uh In 00:36:15.769 --> 00:36:18.615 general, the second is that boot camps don't work. Surprise, surprise if 00:36:18.648 --> 00:36:22.026 you have care responsibilities, you're not going to go to a Y Combinator 00:36:22.059 --> 00:36:25.666 House with a bunch of men and just stay there 24 7 for a week. It's not 00:36:25.699 --> 00:36:28.537 really conducive to the majority of entrepreneurs who need to get this 00:36:28.570 --> 00:36:31.945 work done. However, relevant to the work we're going to talk about today 00:36:31.978 --> 00:36:35.586 um is about gatekeepers. Uh So in our interviews with uh venture 00:36:35.619 --> 00:36:39.546 capitalists and angels, there's a lot of blame being shifted on to the 00:36:39.579 --> 00:36:44.026 entrepreneurs about their relationship and this pipeline issue. However, 00:36:44.059 --> 00:36:47.566 through those interventions, we were trying to re re engage the focus on 00:36:47.599 --> 00:36:51.675 the people who are the gatekeepers to support organization and the funders 00:36:51.708 --> 00:36:55.706 that they can really do the work that's necessary to systemically shift 00:36:55.739 --> 00:37:00.106 the culture of the system to allow more folks into. Um you know, the 00:37:00.139 --> 00:37:04.106 funding pipelines that we are trying to address in this case. Uh So this 00:37:04.139 --> 00:37:07.135 translates directly into the work we're doing. Uh with Kor, you can get 00:37:07.168 --> 00:37:12.666 the next slide, please. I um so we wanted to target a different audience. 00:37:12.699 --> 00:37:16.276 So we wanted to do tech transfer offices at research institutions because 00:37:16.309 --> 00:37:21.115 they're quite different than venture capitalists um or panels that we'd 00:37:21.148 --> 00:37:24.686 worked with before. So what you're seeing is a selection of six questions 00:37:24.719 --> 00:37:27.595 from our pres survey. Uh where we were trying to understand for the 00:37:27.628 --> 00:37:31.675 specific folks at this uh research institution, what their perception was 00:37:31.708 --> 00:37:35.115 , a lot of what this study was about was going into tech transfer office 00:37:35.148 --> 00:37:39.269 doing a bias uh workshop um about their perceptions and then seeing what 00:37:39.302 --> 00:37:42.599 the reality was and then providing policy suggestions or cultural changes 00:37:42.632 --> 00:37:46.218 that they could do to um better the representation and support and 00:37:46.251 --> 00:37:50.738 retention of, of women and folks of color and entrepreneurship. So if we 00:37:50.771 --> 00:37:54.499 take a look at what they said, uh they regularly seek out opportunities to 00:37:54.532 --> 00:37:57.800 support women and people of color, they are great when they do find these 00:37:57.833 --> 00:38:00.883 folks about working with them to commercialize diverse city is an 00:38:00.916 --> 00:38:05.541 essential component of success. Uh They disagree that people of color and 00:38:05.574 --> 00:38:09.932 women enter their office. Um They, you know, encounter that women and 00:38:09.965 --> 00:38:12.983 people of color have just as much business acumen as other folks and that 00:38:13.016 --> 00:38:17.173 they are fine with talking about diversity and other conversations um in 00:38:17.206 --> 00:38:20.383 their office sounds like Utopia. We are very curious seeing these results 00:38:20.416 --> 00:38:23.943 , how it would actually translate later on so we can go to the next slide 00:38:23.976 --> 00:38:26.517 , please. 00:38:26.550 --> 00:38:30.566 So our methodology for doing this, right? It was a workshop. So it was a 00:38:30.599 --> 00:38:34.727 little before lunch and then a few hours after so a little over half a day. 00:38:34.760 --> 00:38:38.557 Um And the the the requested topic was networking, right? So how can we 00:38:38.590 --> 00:38:41.945 diversify, be more intentional with the networks? We know that things like 00:38:41.978 --> 00:38:45.686 word of mouth are very homogenous ways to recruit people um which doesn't 00:38:45.719 --> 00:38:48.896 lead to great turnover and, and a diversity of people entering your office. 00:38:48.929 --> 00:38:53.017 So networking was kind of one of the focuses. Uh we recorded the entire 00:38:53.050 --> 00:38:57.296 workshop. Um And once we had finished the workshop, we had the recording 00:38:57.329 --> 00:39:00.467 transcribed and then Heather Aaron and myself went through the full 00:39:00.500 --> 00:39:04.925 transcription, pulling quotes into thematic categories which we could then 00:39:04.958 --> 00:39:09.385 understand for recommendations as well as clean other things to translate 00:39:09.418 --> 00:39:13.336 to other folks who are doing um very similar work. Uh Next question, 00:39:13.369 --> 00:39:19.356 please or next slide. Um So now I wanna talk a bit about the emergent them. 00:39:19.389 --> 00:39:23.517 So once we had really come through um all of all of the transcription 00:39:23.550 --> 00:39:27.876 from this particular workshop, we had pretty deep conversations about what 00:39:27.909 --> 00:39:32.236 it means to do the outreach. So very similar to our work with venture 00:39:32.269 --> 00:39:37.095 capitalists and um Angels is whose responsibility is it to make a resource 00:39:37.128 --> 00:39:41.135 known, whose responsibility is it to have control over the mechanisms to 00:39:41.168 --> 00:39:45.925 get in contact or a hold of someone a bit different about a university is 00:39:45.958 --> 00:39:49.445 that um the people who theoretically need this support the entrepreneurs, 00:39:49.478 --> 00:39:54.276 potential entrepreneurs, maybe faculty or phd students. And it links very 00:39:54.309 --> 00:39:58.626 much into the second emerging theme about entrepreneurship as a pathway is 00:39:58.659 --> 00:40:02.166 that you have to know a resource exists to use it. And when a tech 00:40:02.199 --> 00:40:05.526 transfer office is saying that, oh, if anyone comes into our office, we 00:40:05.559 --> 00:40:08.876 are totally fine to support them, but is more unwilling to take 00:40:08.909 --> 00:40:11.635 responsibility about anyone knowing about their resource. Like, that's a 00:40:11.668 --> 00:40:15.026 really, really big issue because they're relying on word of mouth. When we 00:40:15.059 --> 00:40:18.166 , you know, poked at this question. They are like, yeah, if someone comes 00:40:18.199 --> 00:40:20.646 to our, if they're successful, they get the patent to get the license, 00:40:20.679 --> 00:40:23.385 then they refer to someone in their department, they come in, they get the 00:40:23.418 --> 00:40:27.086 patent to get the license. But the level of outreach and intentionality 00:40:27.119 --> 00:40:32.017 about who these people are, what the um networks that they uh have 00:40:32.050 --> 00:40:34.956 inherited or the ones that they're using, how similar they are, the 00:40:34.989 --> 00:40:37.807 pattern recognition that was happening there just didn't particularly 00:40:37.840 --> 00:40:41.977 exist. So a really big point across the investor groups that we have seen 00:40:42.010 --> 00:40:46.445 is this whose responsibility is to find the right folks. The second thing 00:40:46.478 --> 00:40:49.006 that we are really interested in, right is that entrepreneurs by this 00:40:49.039 --> 00:40:52.546 pathway, I'm sure if you're in a university, right. Phd S research phd S 00:40:52.579 --> 00:40:56.256 don't particularly uh advocate for you to be an entrepreneur. You know, a 00:40:56.289 --> 00:40:59.037 lot of advisors do not have the skills to teach this and they may not know 00:40:59.070 --> 00:41:03.477 the resources on campus uh to make this happen for a variety of students. 00:41:03.510 --> 00:41:08.142 So that can be the immediate block as well as a strong uh you know, 00:41:08.175 --> 00:41:11.662 negative relationship with an advisor. If you are not going on a strictly 00:41:11.695 --> 00:41:15.331 academic route and you do want to go into the private sector, for instance. 00:41:15.364 --> 00:41:19.041 Um And having a tech transfer office in the middle of those conversations 00:41:19.074 --> 00:41:22.211 is something that hasn't been particularly worked out but is a very common 00:41:22.244 --> 00:41:27.227 uh negative interaction that happens for this university. The last one 00:41:27.260 --> 00:41:31.155 which is absolutely near and dear to my heart is evaluation metrics. So if 00:41:31.188 --> 00:41:34.626 we just rewind in our mind a little bit about all those fabulous things 00:41:34.659 --> 00:41:38.456 this office had to say about how they solve themselves. Um We were like, 00:41:38.489 --> 00:41:42.166 this is great. You know, obviously you got us in the door and you're doing 00:41:42.199 --> 00:41:45.385 this workshop, you think you're doing really, really great. It seems like 00:41:45.418 --> 00:41:51.787 you care about it. Where's the data? Um It didn't exist. Uh So in a tech 00:41:51.820 --> 00:41:55.477 transfer office, some of the focal points we're working on are the 00:41:55.510 --> 00:41:59.086 application and then drop off points with it, right? So you'd look at who 00:41:59.119 --> 00:42:02.186 starts an application, who submits an application, who comes in for the 00:42:02.219 --> 00:42:06.481 first meeting, who files a patent, who, you know those in for licensing? 00:42:06.514 --> 00:42:10.220 And then can we cross reference that with identity demographics to see if 00:42:10.253 --> 00:42:14.162 there's any correlation between uh certain groups of folks, uh women or 00:42:14.195 --> 00:42:18.081 people of color? And are they dropping off? Is the uh you know, the open 00:42:18.114 --> 00:42:20.740 response about how they heard about the tech transfer office? Like all of 00:42:20.773 --> 00:42:24.510 these are, you know, amazing baselines for continuous improvement, which 00:42:24.543 --> 00:42:29.477 is what we are going through throughout the uh the workshop, none of this 00:42:29.510 --> 00:42:32.986 was collected and what they had collected was usually a binary of if they 00:42:33.019 --> 00:42:37.356 had finished an application or not. And because the system had changed the 00:42:37.389 --> 00:42:41.247 consistency of the way they collected that data was also non-existent. Um 00:42:41.280 --> 00:42:44.486 So that was one of the really big trends that we had to work through, 00:42:44.519 --> 00:42:49.425 through this workshop. Um Next slide please. 00:42:49.458 --> 00:42:53.695 And if you can imagine um doing any work with real people has some 00:42:53.728 --> 00:42:59.095 challenges. Um And one of those was biased examples. Uh So folks who may 00:42:59.128 --> 00:43:02.796 pursue similar work, uh It definitely we would bring up analogies, right? 00:43:02.829 --> 00:43:06.577 Like things like intersectionality or bias in general are really hard to 00:43:06.610 --> 00:43:10.577 internalize in four or five hours. So we use a lot of examples about jury 00:43:10.610 --> 00:43:14.385 selection or the issues with fintech where, you know, men, investors would 00:43:14.418 --> 00:43:18.800 not even look at a fem tech entrepreneur because they'd talk to their wife 00:43:18.833 --> 00:43:22.709 or it was so beyond their scope of their lived experience that they just 00:43:22.742 --> 00:43:26.678 cannot deal with it. Um On the other hand, of jury selections about uh 00:43:26.711 --> 00:43:31.889 more equitable justice happening from diverse uh jurors and the importance 00:43:31.922 --> 00:43:35.468 of that, right? But we would be stopped and interrupted numerous times 00:43:35.501 --> 00:43:39.660 whenever we would give analogies or metaphors. Um that would go into the 00:43:39.693 --> 00:43:43.412 semantics of, of the specific problem how it did not overlap to the office 00:43:43.445 --> 00:43:47.541 that it, that they were in, that, that's not them. Um So some 00:43:47.574 --> 00:43:51.782 defensiveness um which can partially be explained that the leadership of 00:43:51.815 --> 00:43:56.213 this institution wanted us to do this workshop four out of the seven 00:43:56.246 --> 00:43:59.342 preserve respondents, which is about a third sided. Coming to this 00:43:59.375 --> 00:44:03.213 training is because the leadership said that this is, is a training they 00:44:03.246 --> 00:44:07.046 should go to you, right. So something that um probably was to be expected 00:44:07.079 --> 00:44:11.296 , the second challenge was about mutual trust. Um So similar to the biased 00:44:11.329 --> 00:44:14.327 examples, there's a lot of pushback on what the definition of trust was 00:44:14.360 --> 00:44:18.477 relative to a network. The idea that their trust was on a spectrum um as 00:44:18.510 --> 00:44:22.606 well as the trust between the facilitators myself, Heather and Aaron and 00:44:22.639 --> 00:44:27.077 the attendees themselves just because of the, the uh state at which we 00:44:27.110 --> 00:44:31.776 entered the actual intervention or, or workshop itself. All right, next 00:44:31.809 --> 00:44:34.057 slide, please. 00:44:34.090 --> 00:44:37.967 So from all this, I would like to provide uh the action steps that we have 00:44:38.000 --> 00:44:42.456 provided for this office and that can generally be applied uh to a lot of 00:44:42.489 --> 00:44:46.327 folks who are in the entrepreneurship support um or who are working within 00:44:46.360 --> 00:44:50.566 specifically tech transfer offices. Um So they should come as no 00:44:50.599 --> 00:44:53.706 particular surprise. But the first one is continuously improving your 00:44:53.739 --> 00:44:58.557 network. We had a lot of epiphanies and neurons firing at the it that we 00:44:58.590 --> 00:45:01.276 can balance our critical understanding of our networks by looking at 00:45:01.309 --> 00:45:06.227 things like do your kids go play soccer together versus did I inherit my 00:45:06.260 --> 00:45:09.526 network? Right. Did this come from my advisor? So this is a diversity of 00:45:09.559 --> 00:45:14.945 ways to understand how homogeneity can exist but not at the surface level 00:45:14.978 --> 00:45:18.046 for your networks between your strong and your weak ties. I think a lot of 00:45:18.079 --> 00:45:22.396 people were receptive to that kind of exploration. The second thing is 00:45:22.429 --> 00:45:26.077 about educating your ecosystem, um understanding the time share and 00:45:26.110 --> 00:45:30.066 coalition that you can have across your campus, your university to be 00:45:30.099 --> 00:45:32.885 mutually beneficial, to get the work done that you need to and to 00:45:32.918 --> 00:45:36.916 diversify the way that people get in your door. The last one should be 00:45:36.949 --> 00:45:39.756 fairly obvious, you need to prove it to yourself, right? So collecting and 00:45:39.789 --> 00:45:43.827 analyzing data to either prove what you already think or set a baseline to 00:45:43.860 --> 00:45:46.635 improve on the values that you already hold. That was one of the things we 00:45:46.668 --> 00:45:49.776 really try to get across is you obviously care about this. This is a value. 00:45:49.809 --> 00:45:53.856 Now, how can we create a system that allows you to track and improve all 00:45:53.889 --> 00:45:57.217 of these things? Um And then the last slide is just our contact 00:45:57.250 --> 00:46:00.436 information again, thanks to everyone, all the other presenters and the 00:46:00.469 --> 00:46:06.506 folks at A SCN QR. Thank you so much Aspen, particularly for the reminder 00:46:06.539 --> 00:46:13.276 that intent and outcomes are not always synonymous. Last but not least we 00:46:13.309 --> 00:46:18.416 have Kate product who will be presenting access to capital funding 00:46:18.449 --> 00:46:25.969 vehicles and growth of start ups for founders who are women of color. Take. 00:46:27.949 --> 00:46:29.949 Hi. Um You can move on to the next slide uh We wanted to take so just as a 00:46:35.329 --> 00:46:41.057 backdrop, uh my name is Kate Burdock, I am CEO of Women 2.0 and founding 00:46:41.090 --> 00:46:45.945 partner of the W Fund. All of that stuff really revives uh revolves 00:46:45.978 --> 00:46:51.506 strongly around um the start up community very specifically, of course, um 00:46:51.539 --> 00:46:57.787 gender and representation in that community. Um We deal with issues and 00:46:57.820 --> 00:47:01.675 have dealt for a while on both sides of the coin. So both the founder side 00:47:01.708 --> 00:47:06.537 and then also the funding side and of course, how they match. Um We've 00:47:06.570 --> 00:47:12.925 heard it a couple times here but uh one of the huge gaps in the industry 00:47:12.958 --> 00:47:20.958 is um first of all, uh 2% of VC capital, which is normally the type of 00:47:21.360 --> 00:47:26.445 capital that anybody who gets into this space is directed towards sort of 00:47:26.478 --> 00:47:32.497 the the golden egg, so to speak, uh goes towards women, uh fully women led 00:47:32.530 --> 00:47:36.106 teams and then 0.06% 00:47:36.139 --> 00:47:41.546 of that goes towards black women. It's about 0.34% that goes towards uh 00:47:41.579 --> 00:47:46.267 Latinas. Um And it's a further breakdown from there, but we wanted to 00:47:46.300 --> 00:47:50.945 really narrow in again on uh the women of color founders. And very 00:47:50.978 --> 00:47:55.195 specifically, we did look at black women founders for this study. So what 00:47:55.228 --> 00:48:00.206 we wanted to figure out is in their early stages of funding um what sorts 00:48:00.239 --> 00:48:05.517 of access they have to resources? What were the resources that they found 00:48:05.550 --> 00:48:09.327 most helpful and important? What were the resources that they felt least 00:48:09.360 --> 00:48:14.967 helpful, important and to some extent uh comparative as well to, as we 00:48:15.000 --> 00:48:21.186 opened up the survey to um to uh women founders from any background. Uh 00:48:21.219 --> 00:48:26.925 some level of um comparative data between black women founders and non 00:48:26.958 --> 00:48:32.856 black women founders. We can switch to the next slide. 00:48:32.889 --> 00:48:40.365 Um Very quickly, our uh our data collection was uh a survey that went out 00:48:40.398 --> 00:48:46.787 again um highlighting various stages, we focused both on pre launch. Um 00:48:46.820 --> 00:48:53.467 And we had uh several questions that asked our founders to rate both their 00:48:53.500 --> 00:49:00.026 perception of access, um their actual access and then how important they 00:49:00.059 --> 00:49:05.816 were to both pre launch and post lost funding. Um We sent this out over 00:49:05.849 --> 00:49:09.566 our own network. We have several partner networks that we worked closely 00:49:09.599 --> 00:49:16.586 with as well who again have um founder audiences um and uh a couple of 00:49:16.619 --> 00:49:21.865 other sort of smaller but very well focused uh communities that we have 00:49:21.898 --> 00:49:28.247 access to. Um And so we got uh this is sort of the general breakdown. We 00:49:28.280 --> 00:49:32.706 got just about 60 who identify as black or African American. We then did a 00:49:32.739 --> 00:49:40.577 phase two qualitative um section with um 1030 minute interviews. That was 00:49:40.610 --> 00:49:45.376 essentially a follow up from a couple of these founders. You can go to the 00:49:45.409 --> 00:49:51.416 next section with the next slide, please. Thank you. So, a couple of the 00:49:51.449 --> 00:49:59.106 key findings and first, I'll stop. And um part of what we wanted to do 00:49:59.139 --> 00:50:06.467 also was, was really uh work on some of the gut feelings that we had had 00:50:06.500 --> 00:50:11.385 with working with so many of these founders as well. And so, um we did try 00:50:11.418 --> 00:50:15.595 to make sure that that, that any bias is accounted for. But in fact, we 00:50:15.628 --> 00:50:20.086 did, uh it did trickle out to have some hard numbers in some of the things 00:50:20.119 --> 00:50:25.006 that we've sort of seen anecdotally across our network. So a couple of the 00:50:25.039 --> 00:50:29.486 key findings um and some of these were a little more interesting than 00:50:29.519 --> 00:50:35.276 others. This was probably one of the more interesting ones um in the pre 00:50:35.309 --> 00:50:40.405 launch phase. So this is before uh ideas are getting off the ground, we 00:50:40.438 --> 00:50:46.046 didn't include incorporation on that, but essentially the no one knows 00:50:46.079 --> 00:50:50.296 about this up until public launch, we're ready to get users the pre launch 00:50:50.329 --> 00:50:58.329 phase. Um And it, the number one slot for all founders was savings. It was 00:50:58.719 --> 00:51:04.807 the most important pre launch uh resource available. However, there was a 00:51:04.840 --> 00:51:11.997 very large disparity in how much starting capital founders had. Um for 00:51:12.030 --> 00:51:17.497 Black founders, the most common amount of start up funding was between the 00:51:17.530 --> 00:51:20.267 range of 5099. 00:51:20.300 --> 00:51:25.385 So about 5000 or so, the second was $0 00:51:25.418 --> 00:51:30.365 comparatively with our non Black founders, the most common amount of money 00:51:30.398 --> 00:51:37.885 that they started out with was $50,000 plus. So that alone is a really big 00:51:37.918 --> 00:51:43.767 um area to look at for the pre launch. A couple of the qualitative data 00:51:43.800 --> 00:51:50.695 points that we found in that were um uh definitely just the straight up 00:51:50.728 --> 00:51:58.256 ability for pre um to have savings at all. It was also supplemented by the 00:51:58.289 --> 00:52:03.736 value of um like friends and family around rounds and that sort of thing. 00:52:03.769 --> 00:52:11.769 Um And one of the common threads is that which we sort of already knew. 00:52:13.418 --> 00:52:19.365 But one of the common threads is that um black women uh in particular, 00:52:19.398 --> 00:52:24.006 their, their friends and family, they don't have access to as many people 00:52:24.039 --> 00:52:27.905 who can fill out larger friends and family rounds than are non black 00:52:27.938 --> 00:52:33.256 respondents. Um So this was a big area. I think that discrepancy was 00:52:33.289 --> 00:52:40.925 pretty large. Um Next slide, please. 00:52:40.958 --> 00:52:46.865 So credit um This should go as no surprise. Um Credit was significantly 00:52:46.898 --> 00:52:52.037 harder to access for black founders, but was considered to be of much 00:52:52.070 --> 00:52:58.345 higher importance once achieved uh to grow uh to start and grow the 00:52:58.378 --> 00:53:06.111 companies. Um So just as a um uh uh just a quick sort of the rankings of 00:53:06.144 --> 00:53:11.990 that from a difficulty standpoint, black founders listed credit as being 00:53:12.023 --> 00:53:18.171 the eighth and we had 19. Um Just so everybody was, we had 19 resource 00:53:18.204 --> 00:53:24.271 options. Black founders listed it as eight with eight most difficult to 00:53:24.304 --> 00:53:28.932 access versus non black founders found it to be 15th most difficult to 00:53:28.965 --> 00:53:33.967 access. But for black founders who did, who were able to access credit, it 00:53:34.000 --> 00:53:38.456 was the fifth most important resource versus the 11th most important 00:53:38.489 --> 00:53:40.537 resource. 00:53:40.570 --> 00:53:47.066 Um I will say that for both across the board credit, once a company was 00:53:47.099 --> 00:53:52.845 launched as and in and in growth phase uh really dropped in importance for 00:53:52.878 --> 00:53:58.456 everybody across the board. Um in terms of the growth stage. Um Next slide 00:53:58.489 --> 00:54:03.296 , please. 00:54:03.329 --> 00:54:09.425 Um huge difference in the importance of loans um for post launch growth of 00:54:09.458 --> 00:54:17.458 a company. Um So for non black founders, loans were listed as the seventh 00:54:18.090 --> 00:54:23.517 uh most important resource and for black founders, loans were listed as 00:54:23.550 --> 00:54:31.550 the dead last in the 19th resource. Um for Black founders, um some of that 00:54:31.628 --> 00:54:36.836 had to do with uh difficulty, some of that had to do frankly with that the 00:54:36.869 --> 00:54:42.376 black founders um found themselves able to get lower uh lower amounts of 00:54:42.409 --> 00:54:47.925 loan and therefore, it simply really became a lower part of their 00:54:47.958 --> 00:54:55.037 resourcing for the companies. Um This is an area that is interesting um 00:54:55.070 --> 00:54:59.936 and a possible area for digging into in a little bit. Uh We have some 00:54:59.969 --> 00:55:07.969 recommendations around that in general. Um Next slide, please. 00:55:08.958 --> 00:55:16.958 And for most of the founders, but more so for the black founders, the 00:55:17.898 --> 00:55:23.227 stage from uh minimal viable product, which is essentially like I have 00:55:23.260 --> 00:55:29.186 something that I can go out and test things on with users. Um to go to 00:55:29.219 --> 00:55:33.436 market seems to be one of the more difficult hurdles to overcome due to 00:55:33.469 --> 00:55:39.526 lack of resources. And it is where um especially black founders found the 00:55:39.559 --> 00:55:47.195 most prohibitive hurdles. Um And the biggest, the biggest um thing that 00:55:47.228 --> 00:55:52.477 they could not achieve when they did, we're not able to ac access monetary 00:55:52.510 --> 00:55:57.175 sources of resource. So we, we sort of bucketed our resource into monetary 00:55:57.208 --> 00:56:01.456 that things like literally money. So, you know, whether it's VC loans 00:56:01.489 --> 00:56:07.296 everything and then non monetary things like um trade or um interns or 00:56:07.329 --> 00:56:11.756 access to a community group or something like that. So of the capital 00:56:11.789 --> 00:56:19.789 sources, it was stated that um slow growth, um limited ability to grow 00:56:20.610 --> 00:56:26.876 teams, anything in that realm was cited at a at a slightly higher uh 00:56:26.909 --> 00:56:34.577 capacity with our black founders in that particular stage. Um Next slide, 00:56:34.610 --> 00:56:38.186 please. 00:56:38.219 --> 00:56:42.155 Um This was 00:56:42.188 --> 00:56:47.747 cool in that we think this is a good opportunity and I'll get to that. Um 00:56:47.780 --> 00:56:53.227 Crowd funding and equity, crowd funding had a very low usage rate, but 00:56:53.260 --> 00:56:58.506 they were also considered by almost everybody, but especially um black 00:56:58.539 --> 00:57:03.666 founders to be very difficult to access. They were perceived as being very 00:57:03.699 --> 00:57:10.166 difficult to access. Um A couple of the qualitative pieces of feedback 00:57:10.199 --> 00:57:16.986 lead us to believe that this may be actually due to a lack of education on 00:57:17.019 --> 00:57:23.586 those vehicles. Um We had one respondent who said I hadn't heard of that 00:57:23.619 --> 00:57:27.706 term. Um And the next thing I'm doing when I'm finished with this survey 00:57:27.739 --> 00:57:31.345 is to go straight and Google that. So, um we'll get to that in the 00:57:31.378 --> 00:57:34.666 opportunity section, but we actually think there's something um pretty 00:57:34.699 --> 00:57:41.675 cool here. Um Next, excuse me, next slide, please. 00:57:41.708 --> 00:57:43.876 Um 00:57:43.909 --> 00:57:49.365 Angel investing and venture capital are both perceived to be difficult to 00:57:49.398 --> 00:57:54.066 access and were in fact reported to be access uh reported as being 00:57:54.099 --> 00:57:59.666 difficult to access. This should come to no surprise. The additional item 00:57:59.699 --> 00:58:05.316 on this is that in some of the qualitative responses, our black founders 00:58:05.349 --> 00:58:12.796 were, were hyper aware of the the real staff behind the numbers. And um so 00:58:12.829 --> 00:58:20.829 as an example, um this woman, this woman uh stated it's very challenging 00:58:21.530 --> 00:58:26.747 um and almost impossible to get investment from this group as an African 00:58:26.780 --> 00:58:32.106 American women, I've discovered few angels and B CS are open to engaging. 00:58:32.139 --> 00:58:37.827 Additionally, we had several people actually state the the known the 00:58:37.860 --> 00:58:41.066 exactly the stats that I mentioned at the beginning, they actually stated 00:58:41.099 --> 00:58:49.099 the known um disparities um of how few dollars in this asset class go 00:58:49.840 --> 00:58:57.840 towards black women founders. Um So, while similar, it, it did seem to be 00:58:59.019 --> 00:59:04.486 reported qualitatively that there was a hyper awareness of the disparities. 00:59:04.519 --> 00:59:08.635 So not quite sure if there's something there, but that was from a 00:59:08.668 --> 00:59:12.385 sentimental standpoint. That was very interesting to us as we were reading 00:59:12.418 --> 00:59:18.376 through some of the responses um next slide and I know we're sort of over 00:59:18.409 --> 00:59:25.727 time. So um oops my dots got messed up. Sorry about that. Couple of the 00:59:25.760 --> 00:59:30.885 key areas for opportunity that we saw kind of the top ones that we saw. 00:59:30.918 --> 00:59:37.026 There are a couple of, you know, smaller opportunities, but um the first 00:59:37.059 --> 00:59:41.807 one is increased exposure to debt based financing that specifically 00:59:41.840 --> 00:59:47.405 support this group of founders. There are a whole class of um of debt 00:59:47.438 --> 00:59:52.885 based financing vehicles and platforms that are really upping. One very 00:59:52.918 --> 00:59:58.486 close partner of ours is enrich her. Um And they have a various set types 00:59:58.519 --> 01:00:03.115 of capital and they're specifically focused on women, business owners, but 01:00:03.148 --> 01:00:06.997 very, very strong focus on black women business owners. It's not quite 01:00:07.030 --> 01:00:12.276 exclusive. And so really understanding how those channels can be opened 01:00:12.309 --> 01:00:17.376 and have that access, that capital be um much widely accessed because it 01:00:17.409 --> 01:00:23.046 seems to be something that is um valuable once the once it can actually 01:00:23.079 --> 01:00:28.776 again be obtained. The second thing which um which we're super excited 01:00:28.809 --> 01:00:34.635 about because it validated again, uh something that we've been leaning 01:00:34.668 --> 01:00:40.586 into quite a lot, um It didn't necessarily have the numbers around was an 01:00:40.619 --> 01:00:44.336 increased education around alternative financing and especially equity, 01:00:44.369 --> 01:00:50.816 crowd funding and crowd funding. Um We, 01:00:50.849 --> 01:00:57.807 we know again anecdotally that when um founders actually get educated on 01:00:57.840 --> 01:01:02.787 how these platforms function and work and how they can go in and run a 01:01:02.820 --> 01:01:09.006 successful campaign, um It does actually end up being quite valuable. And 01:01:09.039 --> 01:01:12.807 so that that is something that we as an organization are absolutely gonna 01:01:12.840 --> 01:01:18.606 continue to lean in on, possibly, um possibly with more gusto so to speak. 01:01:18.639 --> 01:01:25.776 Um One qualitative thing that we really heard in a variety of different 01:01:25.809 --> 01:01:31.037 ways and how we sort of translated it. We had a lot of people where the 01:01:31.070 --> 01:01:36.236 knowledge of how to access stuff and how to get started in the start up 01:01:36.269 --> 01:01:42.236 space, which then is very closely tied with how the various ways to 01:01:42.269 --> 01:01:47.796 resource your company that specifically was reported qualitatively several 01:01:47.829 --> 01:01:53.956 times in, in a variety of different ways. And so trying to both educate 01:01:53.989 --> 01:01:59.876 people and this is this is part of what we do as an organization. Um And, 01:01:59.909 --> 01:02:05.526 but maybe even having a more formal channel to have it happen, um 01:02:05.559 --> 01:02:09.256 educating people who don't, who aren't coming from, you know, the major 01:02:09.289 --> 01:02:14.706 tech hubs or anything like that, especially educating them. Um And then 01:02:14.739 --> 01:02:18.506 also pointing them towards the right educating them on the resources and 01:02:18.539 --> 01:02:22.925 pointing them in the right direction um could be very valuable. Um And 01:02:22.958 --> 01:02:27.486 then lastly just on sort of the MVP and go to market uh identify 01:02:27.519 --> 01:02:30.695 additional ways to lower the hurdle in the beginning stages of product 01:02:30.728 --> 01:02:37.217 development, that is a very busy space. Um However, it does continue to be 01:02:37.250 --> 01:02:42.606 reported from a resourcing standpoint, so that probably um could be 01:02:42.639 --> 01:02:48.486 flushed out additionally, but still a still a pain point um in general. So 01:02:48.519 --> 01:02:54.695 those were our big findings and thank you. Awesome. Thank you so much Kate 01:02:54.728 --> 01:02:59.376 , really appreciate those findings and it was exciting to see. Um Although 01:02:59.409 --> 01:03:02.986 I guess I shouldn't say exciting validating to see um some similarities in 01:03:03.019 --> 01:03:06.166 what we're seeing more broadly in the VC space. And also understanding 01:03:06.199 --> 01:03:09.256 that VC and entrepreneurship is a really right place for intervention for 01:03:09.289 --> 01:03:14.135 women of color. So I appreciate both of your presentations. Um So I know 01:03:14.168 --> 01:03:18.675 we are over time and I just wanted to, to give a huge thank you to all of 01:03:18.708 --> 01:03:22.276 our presenters and our grantees for the amazing work that you all have 01:03:22.309 --> 01:03:26.356 done. Um As you all can see, there's some really useful data that I think 01:03:26.389 --> 01:03:30.816 can be applied immediately across um all different levels of the pipeline 01:03:30.849 --> 01:03:34.967 to think about. How do we better support um And encourage women of color 01:03:35.000 --> 01:03:39.727 um to persist in computing and entrepreneurship. Um And thank you for 01:03:39.760 --> 01:03:43.467 joining us and listening in on the findings. And I encourage you to reach 01:03:43.500 --> 01:03:48.467 out to any of the individuals that you um found some interesting or 01:03:48.500 --> 01:03:51.736 compelling findings to get more information and just wanted to give 01:03:51.769 --> 01:03:55.287 everyone a quick reminder that we will share out the slides and also 01:03:55.320 --> 01:03:59.316 recorded a recording of this session. Um And one final thank you to 01:03:59.349 --> 01:04:02.526 pivotal for funding this work and to our advisory board members for 01:04:02.559 --> 01:04:06.986 helping to guide all of the uh work of the collaborative. So, thank you so 01:04:07.019 --> 01:04:12.000 much and I hope everyone has a wonderful rest of your week.