Next, we have Laura Gonzalez who's going to present building a
collaborative network to support women of color and us. Laura. Thank you
so much and um it was so great to hear the previous presentations because
I see so many connections to the things that our team also found as well.
Um So I wanna first shout out my uh cose co pis uh Doctor Joy Robertson,
Doctor Anne Sheri mcnair and Clarissa San Diego, as well as the mentees
that I'll be briefly introduce you to today. Um If we can go to the next
slide, please. So we decided to focus on building a collaborative model to
support women of color through user experience because user experience
design is inherently such an interdisciplinary field of study that it
allows pathways for um women of color from different backgrounds,
backgrounds to make contributions, even if they didn't necessarily come
from a science and technology background themselves. Um So we developed
this pilot study to do um to create a sustainable mechanism for
intervening in the lack of women of color representation in UX research
and in technology design more broadly, using the pathway of UX design as a
way to encourage women who may be interested in tech but who may not have
had the background in the um in technology to um be a part of the tech
industry and contribute um their, their skills. Our research questions uh
were grounded in how can women of color from humanity background
specifically be mentored and encouraged to pursue pur pursue careers in UX
and technology design. What skills both soft and hard can women of color
be equipped with in order to enter the technology industry after
graduation. So we were working with undergraduate and graduate women of
color and what can the technology industry and UX research specifically
learn from the skills backgrounds and experiences of women of color in
order to more effectively diversify the work for the workforce in a
sustainable way because as we all know, just getting the women of color to
diversify, the workspace is not necessarily a sustainable model if we can
go to the next slide, please. Um So this actually started as a one year um
study, but it's extended into a longitudinal ethnographic study because we
built such strong relationships with our mentees. We've continued to trace
and meet regularly and trace their progress. Um We had four international
Multilingual women, women of color with humanity backgrounds who were all
very interested in entering the tech industry through UX, but they didn't
necessarily see the path or have a path for doing so. Uh for the first
year we held biweekly workshops focused on UES principles and practices.
And we had several guest uh speakers come in and give lessons on UX and
also provide feedback to our four mentees. Uh through these conversations
, our mentees developed individual projects um based on their own
interests. And I'm happy to talk more in depth about what those projects
are in the Q and A. But they each developed projects that were related to
their own interests that also applied the skills we were learning as a
group in UX through our uh biweekly meetings. Then at the end of the year
, um we had a mentorship and um networking event with UX professionals and
industry where our mentees got to share their projects and get feedback.
And then from there, we've continued to trace the progress. So many uh two
of our mentees have already graduated and we're continuing to trace how
has their journey extended beyond the scope of our um initial one year
mark. We can go to the next slide, please. Some of the methods that we
used. Um Are we collected written reflections from each mentee throughout
the one year project after each of the meetings that we had, where they
were talking about um how they felt about the new skills that they were
learning about the feedback that they rece received. Um We also engage in
inter um individual interviews with each mentee throughout the project. So
kind of see how they are perceiving their role in the tech industry. Um
throughout the one year program, um we also use some UX methods to as part
of our own studies. So things like journey mapping where um RMT is traced
, you know, where they started and when, where they ended and where they
want to continue going through this collaboration. And something that I
think was unique and very important to a project was that rather than
coding the interview transcripts as um a team of P I, we engaged in
collaborative analysis of all the data. So we read the essays together, we
talked about the interviews together, we read the transcripts together and
developed um implications based on our collective analysis of what the
mentees engaged in rather than just the pis analysis. Um And we think this
was really, this led us to um really interesting findings and relationship
building if we can go to the next slide, please.
So in the end, um the mentees all uh one back please, one slide back.
Thank you. Um So in the end, all of our mentees successfully completed
their UX research projects and have publications coming out of this work.
Um One thing that all our mentees noticed in their um reflections and in
our analysis sessions was that their language skills because they all
identified as Multilingual and spoke multiple lang uh different languages.
They perceived their language skills as something of value to UX research
, especially as interfaces are designed for Multilingual users and in
multiple languages and many of the um UX professionals that they network
with brought up uh mentees language skills as an asset to technology
design. However, when thinking about seeing themselves in the tech
industry, the mentees did have a lot of hesitancies about things like
stigmas associated with their accents and whether or not their um language
skills would be stigmatized in the workplace. Um And this is where I saw
some connections to the previous presentations as well, right? What is
this inclusive uh work environment look like for women of color, even
though their skills might be very valued? Yes, we want to design
Multilingual interfaces. But do we want Multilingual people working in
these environments um outside of the norms of standardized written English
? And that was a hesitation for several of our participants. Um Another
thing that uh we found from the reflections is that once our mentees
started applying for tech industry jobs, a lot of the language used on
these um job applications in the tech industry were perceived as a as a
barrier for our mentees. So especially in relation to visa concerns. So a
lot of the job applications were not clear whether visa sponsorship would
be provided, whether uh applicants needed to be authorized to work in the
US before they applied. And if this was not explicitly stated um in the
job application, then the mentees kind of um assumed that they would not
be eligible for these jobs. And so we see this as a barrier to building
inclusivity in the tech industry. Um Same thing with jobs that do not
necessarily call for skills um related to engineering, often required
engineering backgrounds or engineering degrees as just like template
requirements. And so one of the things we want to do is continue to work
with industry professionals and tech companies to interrogate why these
requirements are in place. Um If there are ways to kind of ex and how the
wording about the engineering background requirement might be placed on
job applications to leave it open for women of color, from humanity,
disciplines who have experience but not necessarily engineering degrees to
still feel like they can apply. Um If we can go to the next slide, please.
Um So some of the things that we're doing for implications are next steps.
Uh One thing through the guidance um of our mentors uh Doctor Scott, um
we're working for um to build policy implications related to this because
we saw that there are some potential implications for how we can
collaborate again with employers to establish some policy for the clarity
that needs to be um the clarity with which job applications need to be
written. So that women of color and women of color from international
backgrounds feel like they are actually being recruited and are welcome to
apply to these jobs. You know, as research shows a lot of times white men
might feel like they are of course qualified to apply. But for women of
color, these barriers are already systematically in place. And so when we
are not clear about what we're actually requiring for these jobs, um there
are the, there are participants at least didn't feel as compelled to apply
even though they are very much eligible for the positions. Um We also want
to continue, like I said, building connections between women of color in
the tech industry, with women of color in the tech industry at different
stages of their careers. So many of the women that we met with um were
women of color, but they were more advanced in the tech industry. And our
mentees mentioned that they'd like to meet people at different stages. So
people who are just starting um people who are kind of mid career and then
people who are more senior in the tech industry so they can get a wide
range of experience. And also we're just looking to build a be a bigger
network of mentees where our previous mentees can now become, you know,
the mentors for more junior women of color interested in this area. Thank
you. I think that was it.
Thank you so much, Maura. Um to hear about nontraditional pathways and the
implications on transforming recruitment strategies is so important to all
of our work. So, thank you for that. Next. Uh We have Rati Kawawa who will
be presenting the Leadership Academy for Women of color in tech rati.
Thank you, Kimberly. So, um let me just explain first why Leadership
Academy. Um I spent 39 years in tech and as an individual, I succeeded, I
had a phd in computer science and the first one in the world to have one,
a woman of color to have a phd. And within a few years and then I was the
last 17 years, I was a vice president at the lab. So I succeeded. But I
had a second role, which was I was a member of the senior leadership team
of my company. I miserably failed in that role as it, as it relates to
diversity and inclusion. I succeeded in other ways, but I could not change
the culture beyond my own organization. So I thought about this problem
that why was it so hard? And my uh conclusion is that diversity and
inclusion in a tech company is a very complex system problem and they are
not treating it as a complex system problem. Tech companies are used to
dealing with complex problems. They put a man on the moon, they do all
kinds of things, but they have not taken this diversity and inclusion
problem to be a system problem. They look at it as an incremental problem.
So what I said is even they look at it as a system problem. They take the
hardest problem that will prevent that spaceship, say from taking off and
work on the hardest problem first with the most resources. So that, that
doesn't prevent the launch of the spacecraft five years later. But for
women of color, which is the hardest problem in diversity in tech, they
haven't started working on it. They worked on the women problem, which was
the white women problem when that gets solved. Asian women problem, then
underrepresented minorities. So they have not treated it as a system
problem to be solved. So my conclusion was I need to work on women of
color in tech. Then I could work on management or I can work on helping
women of color succeed. And the reason I have chosen to work on Leadership
Academy for women of color is I believe I do not have the right solutions
yet to walk into a boardroom and say do these seven things and I guarantee
you you're going to succeed. It is the solutions are not strong enough yet
for the tech mindset of those leaders. So with teaching the women of color
in tech, I'm going to be developing the stories, the women of color
stories and the assets of what makes them succeed. And all the work you
guys are doing the solutions then will be like possible, more possible
that they will work at the executive level. So that's why I'm starting
with this women of color teaching them. But building the assets that then
go direct to the managers in man in changing the culture. So let's go to
the next slide, please. So the next slide becomes, how do we accelerate
the women of color to positions of inf influence, influence in tech firms
? I'll go through it very fast in 10 seconds is basically the first rung
of the ladder as you know, is broken. And you also know that for
minorities and women, if you don't get noticed in the first stage of your
career, forget it. You're gonna plateau early. And that is not true for
white males, they can plateau at 1st, 2nd and 3rd. But it has been shown
that the more different you are from the norm, the earlier, you have to
get notice for the right reasons. And then leadership is the number one
gap between perceptions of employers and perceptions of new graduates and
what is missing when you turn up at the workplace. So therefore, I decided
leadership development is what, what I was gonna focus on. And the
question is institutions, educational institutions are not preparing our
women of color for the non level playing field. They have to prepare them
more in a way so that they face the same degree of challenge when they get
to the workplace, but that's not what happened. So therefore, there's a
lot of work to be done on the women. So my Harvard research first
identified 19 levers for what it takes to thrive versus just survive. And
there's a big difference between planning to thrive and planning to
survive. And the next thing was the grant that I was able to do the
interviews with women of color and extract the stories. And then I'm doing
api did a pilot and this I believe will double the speed of early career
advancement. And then once these women of color get into first level
management positions, they will then amplify the work and nudge the
processes that are not the formal processes that create the culture change.
That's how when I was a white, when we were white and Asian women, we
created the change is through our informal nudging of middle level
processes. So let's go to the next slide, please. Um uh The next slide
just shows you and I won't go through it. Is I've done this research and
then I also checked my results validated them that there are only 19
things that need to be taught in the area of non technical skills,
leadership development, which make the whole difference in early career.
I'm focusing on early career because if you, you don't get beyond that,
you do, you just don't even expect to have a chance to go beyond that. So
we won't go through this, but I'll illustrate some of them in the next
slide. So let's go to the next slide and this is where I'm gonna spend
most of my time. So this was the project of collecting 100 women of color
stories. And so let me just start with Cammy, who has worked for a major
company in Silicon Valley. And, uh, she basically has every time she has a
new manager, in spite of having two degrees in computer science and
engineering keeps getting told occasionally you are not technical enough
and she did a lot of things. She ignored it for a while, the feedback. And
then finally when she took action, she had, she removed her earrings, she
removed her jewelry, she started wearing uh t-shirts and she started
wearing glasses without lenses, all the stuff that she had to do in order
to just perception change the, manage the perception, since the situation
, manage the perception and yet remain authentic to herself. So her story
is very, very interesting how she managed to remain totally authentic. But
adapted. The problem is women of color don't have role models in their own
workplaces. So it took her 15 years to figure things out which she could
have figured out in two years if she had had lots of exposure to those
people. Similarly. So this is a very interesting story of how she then
changed that whole perception. And now she's a master at it. Now, she
writes technical memos even though she shouldn't have to. So it's a matter
of deciding what her strategy is. It's not right, but it's real. And that
is what we teach. These are the kinds of stories and so can he comes into
the classroom at the leadership academy and spends time with the students
and she's a very big leader in Silicon Valley. So, what she does and she
has two daughters and she's, they're both going for masters in computer
science as well. So now we go to Deb, who has a slightly different issue,
which is about a growth mindset around personal change. And she's also a
leader in Silicon Valley and she, I was one of the people I interviewed
and her thing was typical of many Asians. You're very introverted and you
think that's a personality trait that you have, it's not, you can, you can
act like an extrovert. And she says it's no more difficult than learning
Spanish and she proves it. And now she is in Vegas reports to Zuckerberg.
So you know what I'm trying to tell you is these are stories Nora May is a
Hispanic woman who connects with her, her main strength, which is that she
knows how to ask. She went to MIT she was a single mom. She learned how to
ask for everything. Then she used that skill every time and got promoted
five times in six years at going. So her story says, whatever your
strength is, whether it's resilience or asking how can you use that
strength to get going? The new company really fast. And then Mike, I had
him up there because I really want to tell you that sometimes I use like a
Hispanic male. Mike is a Hispanic male from a Fortune 100 company. And he
comes and we ask him who's the best intern you ever had? And then he tells
the story of Jay who is the best intern he ever had. And then he says in
the talk about yourself first meeting with my intern, how that in turn
impressed the hell out of Mike and then shape the assignment that the
attorn got. So you can shape your early assignments if you're smart about
how you reflect your, your strength to your manager. So it's all about
solutions. The mini case methods that we are developing really allow the
women in the class to look at the range where they want to operate and
many of them shift where they operate within a six week time frame. That
is the critical part. So let's go to the next slide. So what we did is we
actually did a summer academy with 54 students and Zoom and we did it
because summer internships were canceled and people were calling us. And
so we said let's do a summer academy. And so we did it on Zoom completely
and look at the results up there, the results and we had a control group
and we had these people whom we, we took half the people that had applied
and they are reporting less stress and anxiety, more confidence in their
negotiation skills, greater. They're gonna do other leadership skills,
enhanced perception. So the this is relative to a control, 100 and nine
people applied, we took half of them and the other are a control group and
this is before and after six weeks. So the the partnership to do this was
formed between University of Massachusetts, Harvard Kennedy School and
myself and the test, uh pilot test was passed. The students came, 54 came
, 53 stayed. So they and they learned and all of this was done in a
virtual setting. And these assets that we have done that we have created
two assets, that framework that I showed you with all the 19 levers. And
the second is the story, we have shown that within four weeks, we were
able to create a six week curriculum and do it and deliver it because of
the pandemic. And that is why I feel that this asset based model is going
to succeed. Let's keep going. Do I have one or two minutes now?
Next steps? So one more minute, rossi one more minute. So I want to engage
with engage educational institutions because there are three things they
are not doing right. They're advising women of color the same way as they
would advise a white man. And I want to tell you what those three, I don't
have time to tell you what they are. Then there are hypotheses. I need
that need to be validated through proper research. But I have gotten that
from my work and I want to convert the pilot material so that everyone is
going to be open source. Anybody that wants it can use it anywhere in the
and then I want to do the other things and then engage with tech form. So
last slide is just a thank you. Slide. Let's go to the last slide and the
grant has given me the credibility. I always had credibility personally
for having succeeded in tech. But not that I know anything about how I
could teach Black Hispanic women about anything. Nobody would trust me to
do that. Black computer was the first place that gave me a platform to
teach some of my ideas. And I'm really glad I got this grant to do this.
So and then the budget gave me the the budget to hire one teaching fellow.
So I really appreciate the support. This is my dream with or without walk.
It's my dream. This is going to happen and we already did that this
summer. We're going to do it again next summer and now we're gonna scale
it nationally. That is what our plan is. Thank you. Thank you so much Roy
and particularly for your generous offer to make your curriculum um open
access. Um Let's move on next. We have Joan C Williams who will be talking
about workplace experiences of women of color and tech. Joan. Um Many
thanks for the consortium for all the wonderful support they've given me.
Um It's just been a really um a dream of mine to be able to do another
study um in this arena. Um So, uh next slide,
um we believe that what we have done with the consortium support is the
largest quantitative study of women of color and tech. We did 12 inter in
depth interviews. Uh We did a quantitative survey with 215 participants,
100 and 89 women of color. And you can see that 65% of the people uh who
responded were individual contributors. 27% were managers and about 8%.
Other next slide.
Um What I have done is taken this huge literature on racial and gender
bias from uh experimental social psychology, industrial organizational
psychology, um and individual uh uh and behavioral economics and
integrated into five basic patterns of workplace bias in professional
workplaces. Um The first pattern which is triggered both by race and by
gender as well as other vectors of status is that some groups have to
prove themselves more than others, which means that some groups literally
have to be more competent than uh others in order to succeed. So that's
the prove it again pattern, technical name, descriptive stereotyping, um
leniency bias, um attribution bias and other uh other names. The second is
what I call the tight rope is that some groups have to be savvier than
others in order to succeed. The easiest way to explain this is that for
white men in professional workplaces, they just need to be authoritative
and ambitious. But every other group needs to be authoritative and
ambitious in a way that's acceptable to the white men in their environment.
And that means that there's often a ding for assertive behavior. Um And
that groups other than white men, sometimes elite white men. Uh um it's
even narrower than white men, but uh usually it's white men. Uh uh So
every other group is pressured to behave in deferential ways and also play
subordinate roles which really influences their access to high quality
assignments. Um The third is that and they uh by the way and that they're
also often um faulted for personality problems. Um The third is um
maternal wall. This is the strongest trigger of gender bias. Um It's that
um basically motherhood triggers very strong negative competence and
commitment assumptions. The fourth pattern I call the tug of war and
that's really the in group favoritism works for some but not for others.
Because often gender bias against women, feels conflicts among women and
racial bias sometimes feels conflicts among um people of color as well to
white, not white enough. And then the fifth pattern is racial stereotypes
that are not picked up by the other four patterns. A lot of racial
stereotypes are picked up by proved again, tight, open tug of war, but
there's some elements that aren't particularly relevant in tech is that a
people of Asian descent are seen as um good for technical jobs but not
leadership and other people of color are seen as not technical enough as
we've heard. And for um black professionals in this as in so many other
professional workplaces, their experience really stands out because of the
startling levels of disrespect that they often encounter now who reports
the highest levels of bias. Um in general, a East Asian women in our
sample reported the highest average levels of bias. This may have been
because we were focused on 2010 when President um Trump was going around
saying things like uh that, that he was angry about the quote, the Chinese
virus. Next slide.
Here's an example of prove it again bias. We're always looked at as why
did you deserve it? Not because I have the brain power to support me being
in the role. It doesn't matter how well I perform. I feel like I have to
go above and beyond. This is a pattern that we have found again and again
, we studied six different industries. Um african-american women always
report the highest levels. Uh Well, women of color always report the
highest levels of prove it again bias. And african-american women among
them typically report the highest levels of prove it again bias because
they're triggering two sets of negative competence assumptions, one based
on gender and one based on race. Next slide.
Um So here for tight rope. Um What you see here is like Lyr scale. So
these uh lyre scale measures the strength of bias from not at all to very
strong. So you can see that, that the uh East Asian women report the
highest levels of pushback for assertive behavior. Um All other women are
in between and what white women report the lowest levels. Although in
study after study, we have reported, um white white women report sharply
very sharply lower levels than white men. But you can see that there is a
um both a gender and a racial hierarchy as is so common in these patterns.
Then the next is with maternal wall. Um uh My ne my confidence and
commitment were questioned after I had Children. Um You can see that uh
Latinx women reported the strongest bias among this. There's a very strong
, they very consistently report the strong, the strongest maternal law
bias. Um And then white women on the other side and all other women in
between. Uh next slide, maternal well bias um includes both um having your
confidence and que and commitment questioned after you had Children. But
also if your confidence and commitment are indisputable, for example, if
you work long hours, then um you're deemed for that too, you trigger
dislike. She's a bad mother. So she's probably a bad person and also
you're held to higher performance standards. So it's kind of damned if you
do. Damned if you don't. Um, anger is another tight rope pattern because
again, white men have a lot more scope in many workplaces to show anger
than any other group. Here's a native, uh, Alaskan native woman. When I
have a very strong opinion about something I take special care includes in
choosing my words. If something has made me angry in a meeting, I take it
out of the meeting, write down my anger, give it a day or two and then I
try to address it later when I'm feeling less emotionally attached to that
issue. So this picks on something up that Rocky said, I mean, uh I wrote a
book in 2014 called What Works For Women at work in which I interviewed
100 and 27 wise women and asked them, how do you navigate successfully
through gender bias? And um so one of the, and what I found basic bottom
line is, you know, women can do anything backwards and heels, but they
have to be twice as good. Um And not only just in terms of their technical
skills, that's to prove it again, they have to be twice as good in terms
of um human relations. And you see all of this uh background level of
politically, political savvy and the extra emotion work that this native
Alaskan woman has to do to be successful in her workplace. Next slide.
Um Women also have far um higher levels of uh the office housework um that
one woman said I've constant, I'm constantly slotted into admin roles even
though I have a master's degree and have been physically looked past so
many times. Um Women of all races get pressure to do more administrative
housework and behind the scenes work. Uh And um women of all races and men
of color get less access to the glamour work. Another very strong tight
rope pattern. We see again and again, we saw it in the status set as well.
Next slide, some evidence is that Latinx women have the highest levels.
Um talk of war bias. Um This is conflict within um uh uh a racial or
gender group. You can see South Asian women were by far the tall, the
highest we aggregated racial and gender bias. This uh we have persistent
reports of um conflict with. So with um uh South Asian men that may be
what's going on here and you see the same pattern and then the racial
pattern of being disrespected or demeaned at work again, as in many other
data sets, black women report the highest levels. And then we see the same
distribution among different groups of women. Next slide.
Um uh uh This is there, there is this sort of concept of isolationism that
happens when you're the only one who looks like you since you're the only
Latina um that you have to represent your whole group. So anything you do
, if it's going badly, everyone in the room is going to think that Latina
engineers uh of engineers and uh Latina engineers in a bad way in the
future. Um That's uh that's a very typical example uh of tug of War next
line.
Um I'll just end with a few examples of the bias interrupters because as
Rotten pointed out um uh to address systemic racism and sexism, you need
to change systems and you need to change it with systems thinking. And so
we have developed biased interrupters and I'll just end with a couple of
examples. We um I wrote a 2.5 document that um integrated racial and
gender bias and showed and described how it typically takes uh uh plays
out in performance evaluations and just giving people that document and
reading them through. It led to sharply higher bonuses and statistically
significantly higher performance evaluations for black women and black men
and also white women. And then finally, um and a bias interrupter, I'm
gonna be proposing in our report um uh on women of color in tech is that
um women of color um really often get caught doing a lot of diversity work
for which they are not paid and which they get no credit for and they get
no administrative support for and they want to continue to do the work.
But what I have vetted with them, the idea that if they are asked to do
that work, they should be provided with adequate um administrative support
so that all they have to do is just find the speakers and then somebody
else in the org does everything else. That's a good example of an
organizational bias interrupter. Many, many thanks. I appreciate the
opportunity.
Thank you, Joan. I love the bias interrupters and um hearing how it can
make the systemic change that we all know needs to occur. Next, we have
Heather Metcalf, Aaron Kelly and Aspen Russell, their presentation stem to
market conscious commercialization over to you. Thank you so much, Kim.
And I wanna just extend the gratitude. Every other presenter has to um all
the folks that are there. I'm Aspen Russell. We'll be presenting on behalf
of Erin and Heather and we can hit the next slide.
All right. So uh the work that uh Kr and A su helped us do is built on a
foundation of work we had done a couple of years prior. Um So we had a
couple, we had an accelerator for stem trained women and we also were
doing what's called intentional investing where we were targeting
investors and doing biased trainings. Um So we could learn from the women
in Stem and the Accelerator Program and translate that knowledge onto the
investors. Uh These are a couple of our findings um for that, it's not a
pipeline problem. Women of color, folks of color and entrepreneurship are
there. It is the active avoidance and you know, canceling out of these
people existing, which is why the funding is not going there. Uh In
general, the second is that boot camps don't work. Surprise, surprise if
you have care responsibilities, you're not going to go to a Y Combinator
House with a bunch of men and just stay there 24 7 for a week. It's not
really conducive to the majority of entrepreneurs who need to get this
work done. However, relevant to the work we're going to talk about today
um is about gatekeepers. Uh So in our interviews with uh venture
capitalists and angels, there's a lot of blame being shifted on to the
entrepreneurs about their relationship and this pipeline issue. However,
through those interventions, we were trying to re re engage the focus on
the people who are the gatekeepers to support organization and the funders
that they can really do the work that's necessary to systemically shift
the culture of the system to allow more folks into. Um you know, the
funding pipelines that we are trying to address in this case. Uh So this
translates directly into the work we're doing. Uh with Kor, you can get
the next slide, please. I um so we wanted to target a different audience.
So we wanted to do tech transfer offices at research institutions because
they're quite different than venture capitalists um or panels that we'd
worked with before. So what you're seeing is a selection of six questions
from our pres survey. Uh where we were trying to understand for the
specific folks at this uh research institution, what their perception was
, a lot of what this study was about was going into tech transfer office
doing a bias uh workshop um about their perceptions and then seeing what
the reality was and then providing policy suggestions or cultural changes
that they could do to um better the representation and support and
retention of, of women and folks of color and entrepreneurship. So if we
take a look at what they said, uh they regularly seek out opportunities to
support women and people of color, they are great when they do find these
folks about working with them to commercialize diverse city is an
essential component of success. Uh They disagree that people of color and
women enter their office. Um They, you know, encounter that women and
people of color have just as much business acumen as other folks and that
they are fine with talking about diversity and other conversations um in
their office sounds like Utopia. We are very curious seeing these results
, how it would actually translate later on so we can go to the next slide
, please.
So our methodology for doing this, right? It was a workshop. So it was a
little before lunch and then a few hours after so a little over half a day.
Um And the the the requested topic was networking, right? So how can we
diversify, be more intentional with the networks? We know that things like
word of mouth are very homogenous ways to recruit people um which doesn't
lead to great turnover and, and a diversity of people entering your office.
So networking was kind of one of the focuses. Uh we recorded the entire
workshop. Um And once we had finished the workshop, we had the recording
transcribed and then Heather Aaron and myself went through the full
transcription, pulling quotes into thematic categories which we could then
understand for recommendations as well as clean other things to translate
to other folks who are doing um very similar work. Uh Next question,
please or next slide. Um So now I wanna talk a bit about the emergent them.
So once we had really come through um all of all of the transcription
from this particular workshop, we had pretty deep conversations about what
it means to do the outreach. So very similar to our work with venture
capitalists and um Angels is whose responsibility is it to make a resource
known, whose responsibility is it to have control over the mechanisms to
get in contact or a hold of someone a bit different about a university is
that um the people who theoretically need this support the entrepreneurs,
potential entrepreneurs, maybe faculty or phd students. And it links very
much into the second emerging theme about entrepreneurship as a pathway is
that you have to know a resource exists to use it. And when a tech
transfer office is saying that, oh, if anyone comes into our office, we
are totally fine to support them, but is more unwilling to take
responsibility about anyone knowing about their resource. Like, that's a
really, really big issue because they're relying on word of mouth. When we
, you know, poked at this question. They are like, yeah, if someone comes
to our, if they're successful, they get the patent to get the license,
then they refer to someone in their department, they come in, they get the
patent to get the license. But the level of outreach and intentionality
about who these people are, what the um networks that they uh have
inherited or the ones that they're using, how similar they are, the
pattern recognition that was happening there just didn't particularly
exist. So a really big point across the investor groups that we have seen
is this whose responsibility is to find the right folks. The second thing
that we are really interested in, right is that entrepreneurs by this
pathway, I'm sure if you're in a university, right. Phd S research phd S
don't particularly uh advocate for you to be an entrepreneur. You know, a
lot of advisors do not have the skills to teach this and they may not know
the resources on campus uh to make this happen for a variety of students.
So that can be the immediate block as well as a strong uh you know,
negative relationship with an advisor. If you are not going on a strictly
academic route and you do want to go into the private sector, for instance.
Um And having a tech transfer office in the middle of those conversations
is something that hasn't been particularly worked out but is a very common
uh negative interaction that happens for this university. The last one
which is absolutely near and dear to my heart is evaluation metrics. So if
we just rewind in our mind a little bit about all those fabulous things
this office had to say about how they solve themselves. Um We were like,
this is great. You know, obviously you got us in the door and you're doing
this workshop, you think you're doing really, really great. It seems like
you care about it. Where's the data? Um It didn't exist. Uh So in a tech
transfer office, some of the focal points we're working on are the
application and then drop off points with it, right? So you'd look at who
starts an application, who submits an application, who comes in for the
first meeting, who files a patent, who, you know those in for licensing?
And then can we cross reference that with identity demographics to see if
there's any correlation between uh certain groups of folks, uh women or
people of color? And are they dropping off? Is the uh you know, the open
response about how they heard about the tech transfer office? Like all of
these are, you know, amazing baselines for continuous improvement, which
is what we are going through throughout the uh the workshop, none of this
was collected and what they had collected was usually a binary of if they
had finished an application or not. And because the system had changed the
consistency of the way they collected that data was also non-existent. Um
So that was one of the really big trends that we had to work through,
through this workshop. Um Next slide please.
And if you can imagine um doing any work with real people has some
challenges. Um And one of those was biased examples. Uh So folks who may
pursue similar work, uh It definitely we would bring up analogies, right?
Like things like intersectionality or bias in general are really hard to
internalize in four or five hours. So we use a lot of examples about jury
selection or the issues with fintech where, you know, men, investors would
not even look at a fem tech entrepreneur because they'd talk to their wife
or it was so beyond their scope of their lived experience that they just
cannot deal with it. Um On the other hand, of jury selections about uh
more equitable justice happening from diverse uh jurors and the importance
of that, right? But we would be stopped and interrupted numerous times
whenever we would give analogies or metaphors. Um that would go into the
semantics of, of the specific problem how it did not overlap to the office
that it, that they were in, that, that's not them. Um So some
defensiveness um which can partially be explained that the leadership of
this institution wanted us to do this workshop four out of the seven
preserve respondents, which is about a third sided. Coming to this
training is because the leadership said that this is, is a training they
should go to you, right. So something that um probably was to be expected
, the second challenge was about mutual trust. Um So similar to the biased
examples, there's a lot of pushback on what the definition of trust was
relative to a network. The idea that their trust was on a spectrum um as
well as the trust between the facilitators myself, Heather and Aaron and
the attendees themselves just because of the, the uh state at which we
entered the actual intervention or, or workshop itself. All right, next
slide, please.
So from all this, I would like to provide uh the action steps that we have
provided for this office and that can generally be applied uh to a lot of
folks who are in the entrepreneurship support um or who are working within
specifically tech transfer offices. Um So they should come as no
particular surprise. But the first one is continuously improving your
network. We had a lot of epiphanies and neurons firing at the it that we
can balance our critical understanding of our networks by looking at
things like do your kids go play soccer together versus did I inherit my
network? Right. Did this come from my advisor? So this is a diversity of
ways to understand how homogeneity can exist but not at the surface level
for your networks between your strong and your weak ties. I think a lot of
people were receptive to that kind of exploration. The second thing is
about educating your ecosystem, um understanding the time share and
coalition that you can have across your campus, your university to be
mutually beneficial, to get the work done that you need to and to
diversify the way that people get in your door. The last one should be
fairly obvious, you need to prove it to yourself, right? So collecting and
analyzing data to either prove what you already think or set a baseline to
improve on the values that you already hold. That was one of the things we
really try to get across is you obviously care about this. This is a value.
Now, how can we create a system that allows you to track and improve all
of these things? Um And then the last slide is just our contact
information again, thanks to everyone, all the other presenters and the
folks at A SCN QR. Thank you so much Aspen, particularly for the reminder
that intent and outcomes are not always synonymous. Last but not least we
have Kate product who will be presenting access to capital funding
vehicles and growth of start ups for founders who are women of color. Take.
Hi. Um You can move on to the next slide uh We wanted to take so just as a
backdrop, uh my name is Kate Burdock, I am CEO of Women 2.0 and founding
partner of the W Fund. All of that stuff really revives uh revolves
strongly around um the start up community very specifically, of course, um
gender and representation in that community. Um We deal with issues and
have dealt for a while on both sides of the coin. So both the founder side
and then also the funding side and of course, how they match. Um We've
heard it a couple times here but uh one of the huge gaps in the industry
is um first of all, uh 2% of VC capital, which is normally the type of
capital that anybody who gets into this space is directed towards sort of
the the golden egg, so to speak, uh goes towards women, uh fully women led
teams and then 0.06%
of that goes towards black women. It's about 0.34% that goes towards uh
Latinas. Um And it's a further breakdown from there, but we wanted to
really narrow in again on uh the women of color founders. And very
specifically, we did look at black women founders for this study. So what
we wanted to figure out is in their early stages of funding um what sorts
of access they have to resources? What were the resources that they found
most helpful and important? What were the resources that they felt least
helpful, important and to some extent uh comparative as well to, as we
opened up the survey to um to uh women founders from any background. Uh
some level of um comparative data between black women founders and non
black women founders. We can switch to the next slide.
Um Very quickly, our uh our data collection was uh a survey that went out
again um highlighting various stages, we focused both on pre launch. Um
And we had uh several questions that asked our founders to rate both their
perception of access, um their actual access and then how important they
were to both pre launch and post lost funding. Um We sent this out over
our own network. We have several partner networks that we worked closely
with as well who again have um founder audiences um and uh a couple of
other sort of smaller but very well focused uh communities that we have
access to. Um And so we got uh this is sort of the general breakdown. We
got just about 60 who identify as black or African American. We then did a
phase two qualitative um section with um 1030 minute interviews. That was
essentially a follow up from a couple of these founders. You can go to the
next section with the next slide, please. Thank you. So, a couple of the
key findings and first, I'll stop. And um part of what we wanted to do
also was, was really uh work on some of the gut feelings that we had had
with working with so many of these founders as well. And so, um we did try
to make sure that that, that any bias is accounted for. But in fact, we
did, uh it did trickle out to have some hard numbers in some of the things
that we've sort of seen anecdotally across our network. So a couple of the
key findings um and some of these were a little more interesting than
others. This was probably one of the more interesting ones um in the pre
launch phase. So this is before uh ideas are getting off the ground, we
didn't include incorporation on that, but essentially the no one knows
about this up until public launch, we're ready to get users the pre launch
phase. Um And it, the number one slot for all founders was savings. It was
the most important pre launch uh resource available. However, there was a
very large disparity in how much starting capital founders had. Um for
Black founders, the most common amount of start up funding was between the
range of 5099.
So about 5000 or so, the second was $0
comparatively with our non Black founders, the most common amount of money
that they started out with was $50,000 plus. So that alone is a really big
um area to look at for the pre launch. A couple of the qualitative data
points that we found in that were um uh definitely just the straight up
ability for pre um to have savings at all. It was also supplemented by the
value of um like friends and family around rounds and that sort of thing.
Um And one of the common threads is that which we sort of already knew.
But one of the common threads is that um black women uh in particular,
their, their friends and family, they don't have access to as many people
who can fill out larger friends and family rounds than are non black
respondents. Um So this was a big area. I think that discrepancy was
pretty large. Um Next slide, please.
So credit um This should go as no surprise. Um Credit was significantly
harder to access for black founders, but was considered to be of much
higher importance once achieved uh to grow uh to start and grow the
companies. Um So just as a um uh uh just a quick sort of the rankings of
that from a difficulty standpoint, black founders listed credit as being
the eighth and we had 19. Um Just so everybody was, we had 19 resource
options. Black founders listed it as eight with eight most difficult to
access versus non black founders found it to be 15th most difficult to
access. But for black founders who did, who were able to access credit, it
was the fifth most important resource versus the 11th most important
resource.
Um I will say that for both across the board credit, once a company was
launched as and in and in growth phase uh really dropped in importance for
everybody across the board. Um in terms of the growth stage. Um Next slide
, please.
Um huge difference in the importance of loans um for post launch growth of
a company. Um So for non black founders, loans were listed as the seventh
uh most important resource and for black founders, loans were listed as
the dead last in the 19th resource. Um for Black founders, um some of that
had to do with uh difficulty, some of that had to do frankly with that the
black founders um found themselves able to get lower uh lower amounts of
loan and therefore, it simply really became a lower part of their
resourcing for the companies. Um This is an area that is interesting um
and a possible area for digging into in a little bit. Uh We have some
recommendations around that in general. Um Next slide, please.
And for most of the founders, but more so for the black founders, the
stage from uh minimal viable product, which is essentially like I have
something that I can go out and test things on with users. Um to go to
market seems to be one of the more difficult hurdles to overcome due to
lack of resources. And it is where um especially black founders found the
most prohibitive hurdles. Um And the biggest, the biggest um thing that
they could not achieve when they did, we're not able to ac access monetary
sources of resource. So we, we sort of bucketed our resource into monetary
that things like literally money. So, you know, whether it's VC loans
everything and then non monetary things like um trade or um interns or
access to a community group or something like that. So of the capital
sources, it was stated that um slow growth, um limited ability to grow
teams, anything in that realm was cited at a at a slightly higher uh
capacity with our black founders in that particular stage. Um Next slide,
please.
Um This was
cool in that we think this is a good opportunity and I'll get to that. Um
Crowd funding and equity, crowd funding had a very low usage rate, but
they were also considered by almost everybody, but especially um black
founders to be very difficult to access. They were perceived as being very
difficult to access. Um A couple of the qualitative pieces of feedback
lead us to believe that this may be actually due to a lack of education on
those vehicles. Um We had one respondent who said I hadn't heard of that
term. Um And the next thing I'm doing when I'm finished with this survey
is to go straight and Google that. So, um we'll get to that in the
opportunity section, but we actually think there's something um pretty
cool here. Um Next, excuse me, next slide, please.
Um
Angel investing and venture capital are both perceived to be difficult to
access and were in fact reported to be access uh reported as being
difficult to access. This should come to no surprise. The additional item
on this is that in some of the qualitative responses, our black founders
were, were hyper aware of the the real staff behind the numbers. And um so
as an example, um this woman, this woman uh stated it's very challenging
um and almost impossible to get investment from this group as an African
American women, I've discovered few angels and B CS are open to engaging.
Additionally, we had several people actually state the the known the
exactly the stats that I mentioned at the beginning, they actually stated
the known um disparities um of how few dollars in this asset class go
towards black women founders. Um So, while similar, it, it did seem to be
reported qualitatively that there was a hyper awareness of the disparities.
So not quite sure if there's something there, but that was from a
sentimental standpoint. That was very interesting to us as we were reading
through some of the responses um next slide and I know we're sort of over
time. So um oops my dots got messed up. Sorry about that. Couple of the
key areas for opportunity that we saw kind of the top ones that we saw.
There are a couple of, you know, smaller opportunities, but um the first
one is increased exposure to debt based financing that specifically
support this group of founders. There are a whole class of um of debt
based financing vehicles and platforms that are really upping. One very
close partner of ours is enrich her. Um And they have a various set types
of capital and they're specifically focused on women, business owners, but
very, very strong focus on black women business owners. It's not quite
exclusive. And so really understanding how those channels can be opened
and have that access, that capital be um much widely accessed because it
seems to be something that is um valuable once the once it can actually
again be obtained. The second thing which um which we're super excited
about because it validated again, uh something that we've been leaning
into quite a lot, um It didn't necessarily have the numbers around was an
increased education around alternative financing and especially equity,
crowd funding and crowd funding. Um We,
we know again anecdotally that when um founders actually get educated on
how these platforms function and work and how they can go in and run a
successful campaign, um It does actually end up being quite valuable. And
so that that is something that we as an organization are absolutely gonna
continue to lean in on, possibly, um possibly with more gusto so to speak.
Um One qualitative thing that we really heard in a variety of different
ways and how we sort of translated it. We had a lot of people where the
knowledge of how to access stuff and how to get started in the start up
space, which then is very closely tied with how the various ways to
resource your company that specifically was reported qualitatively several
times in, in a variety of different ways. And so trying to both educate
people and this is this is part of what we do as an organization. Um And,
but maybe even having a more formal channel to have it happen, um
educating people who don't, who aren't coming from, you know, the major
tech hubs or anything like that, especially educating them. Um And then
also pointing them towards the right educating them on the resources and
pointing them in the right direction um could be very valuable. Um And
then lastly just on sort of the MVP and go to market uh identify
additional ways to lower the hurdle in the beginning stages of product
development, that is a very busy space. Um However, it does continue to be
reported from a resourcing standpoint, so that probably um could be
flushed out additionally, but still a still a pain point um in general. So
those were our big findings and thank you. Awesome. Thank you so much Kate
, really appreciate those findings and it was exciting to see. Um Although
I guess I shouldn't say exciting validating to see um some similarities in
what we're seeing more broadly in the VC space. And also understanding
that VC and entrepreneurship is a really right place for intervention for
women of color. So I appreciate both of your presentations. Um So I know
we are over time and I just wanted to, to give a huge thank you to all of
our presenters and our grantees for the amazing work that you all have
done. Um As you all can see, there's some really useful data that I think
can be applied immediately across um all different levels of the pipeline
to think about. How do we better support um And encourage women of color
um to persist in computing and entrepreneurship. Um And thank you for
joining us and listening in on the findings. And I encourage you to reach
out to any of the individuals that you um found some interesting or
compelling findings to get more information and just wanted to give
everyone a quick reminder that we will share out the slides and also
recorded a recording of this session. Um And one final thank you to
pivotal for funding this work and to our advisory board members for
helping to guide all of the uh work of the collaborative. So, thank you so
much and I hope everyone has a wonderful rest of your week.