[MUSIC]
I think especially here, in the heart of Silicon Valley,
where organizations pride themselves on their innovation and their creativity.
If any place were to be able to take advantage of a diverse workforce and
the creativity that's associated with that diversity, it should be here.
And yet time and time again, we see evidence that Silicon Valley firms
are in fact very homogeneous, mostly male and mostly white.
We don't have a lot of leaders who are female or people of color.
But let's focus on a minute on females,
because if we're ever going to be successful in diversity.
That's should be the dimension on which we're most successful,
because we have the most of them, right?
51% of the population, so, what happens?
Well, turns out that we actually have a bias.
If we think leader, too many of us think male.
And if we think male, we think leader.
But in fact, there's some very interesting research that's coming out
in the last couple of years, that suggests that that perspective may be changing.
[MUSIC]
One of my most favorite studies, is a study that was done, is actually
a meta-analysis, so they got a whole bunch of other studies, 99 different studies.
And they looked at leaders who evaluated themselves and
whose subordinates evaluated them.
And they came up with a very interesting set of results.
What they found was, that while male leaders evaluated
themselves as more effective than did their female counterparts.
The subordinates of this leaders, evaluate the female leaders
as more effective than they did their male counterparts.
And this was true, except in two industries government and the military.
Now, what's unique about those two industries?
Those two industries are extremely hierarchical.
So, women were perceived by their subordinates as better leaders
in organizations or
in industries where it was a more collaborative form of leadership.
Other researchers have found that one of the ways to actually
get a more effective group is to increase the number of women.
And it turns out there's actually a very high correlation between the number of
women in a group and the ability of that group to be innovative and creative.
[MUSIC]
The first contender may be that there simply aren't enough qualified women.
A second contender maybe none conscious or explicit bias.
A third contender maybe that the criteria by which we
hire changes depending on who we're looking at.
And the fourth criteria maybe in fact what happens is we get women
into the organization, we don't move them up because we change
the criteria by which we judge their performance.
[MUSIC]
This is an interesting perspective, because it suggests that were there enough
qualified women, of course, we would hire them.
But what's interesting to observe, given that statement, is that since
1988 more women have graduated from US universities than have men.
And in 2010, for example, the majority of people receiving
undergraduate degrees, masters degrees, and PhD's were women.
And so it turned out that a lot of people who are getting these advanced degrees
happen to be female.
So maybe it is not that there are not enough qualified women.
Maybe what is happening is we are not seeing these women as qualified.
Now, maybe that is because of implicit bias, right?
We have biases about what we think would be the correct person having this job.
And so my favorite example of how powerful
this implicit bias is is some work by Golden and Rouse.
And what they did was,
they went to symphony orchestras who were conducting auditions.
And they said, we'd like you to conduct your auditions behind a curtain.
Now this didn't seem a big deal, because think about a symphony orchestra.
What does a symphony orchestra care about?
The quality of the music.
Who cares if the violin that is so awesome is being played by a male or a female?
This is irrelevant.
And in fact, certainly their financial
model does not require that they have all males or all females.
It's irrelevant, it's the quality of the music that the patrons will pay for.
And so what happened?
So they did their auditions behind a curtain
which hid the race and gender of the musician.
And when they did that, women were 50% more likely to move up in the audition and
250% more likely to gain a seat in the orchestra.
So while there seems to be no economic incentive as to why we have this bias.
It maybe that there is a physiological impediment that stops
us from seeing from hearing the music of women as being equal or
sometimes better than the music of men.
[MUSIC]
Wouldn't that be awesome if we could actually just judge people on their
performance rather than filtering that performance through their notion of
gender and what is appropriate or not appropriate.
Well, actually, an organization here in the Valley did that.
What they did is, they had an online application process and
the application process required that people engage in activities
that were representative of what they would be doing in the organization.
And before they judged who they would actually bring in for
the interview they stripped all of the information about race or
gender from the applicants, they just have their performance.
And when the curtain was pulled back over 60% of
the top performers were people of color or women.
Their's an organization here called,
Speak with the Geek which is a tech recruiting organization.
And they did an interesting experiment.
They sent out resumes to their clients, 5,000 resumes, and
the clients review those resumes and then sent back requests to interview folks.
And when they did this with full information about the race and
gender of the applicants.
Fewer than 5% of the women were called back for interviews.
They took those same 5,000 resumes and sent them out again,
but they stripped off identifying information about race and gender.
And in that case, 54% of the resumes that were asked to be brought in for
interviews were resumes of women.
The difference is huge.
[MUSIC]
Oftentimes we look at the candidate and decide what it is we want, and
adjust the criteria to who we are looking at.
There was a wonderful study that was done by researchers who posed
to people who were doing recruiting for a traditional male job, a police chief.
And in advance of their looking at candidates,
they ask the recruiters what do you care more about?
And it turns out there were two dimensions that really came through.
One was experience, and one was education.
And so in fact, these recruiters privileged education over experience.
So they sent out two vitas, the vitas were exactly the same,
except that vita number one, from Bob, had more education than experience.
And from Barbara, she had more experience than education and
true to what they said, they selected Bob over Barbara.
Then what the researchers did is, they switched the names.
So now Barbara has more education and Bob has more experience, and
guess what the recruiters preferred now?
You guessed it, experience.
The solution here of course is,
to establish clear criteria before you begin the interview process.
But that's just half the job.
You've gotta make sure that you actually have the correct criteria.
So consider for example, the challenge that Carnegie Mellon University had
when they attempted to improve the gender balance of their computer science major.
They were having a very small percentage of women applying for and
being admitted to their computer science program.
Now as they reassessed the criteria by which they judged whether you could
be admitted or not, one of the criteria was a lot of experience in computers.
Which at first blush, seems quite reasonable, computer major?
You sure you understand computers.
But lets take that apart just a little bit.
So if I were majoring in medicine, well how much experience
would I have to have in a medical setting before I could be a medical major?
Or how much engineering experience do I need before I became an engineer?
Or how much teaching experience would I need before I became an educator?
You see, what happens is that we actually help people figure out how to be a doctor,
an engineer, a teacher.
That's what graduate and undergraduate education's all about.
So the percentage of women went from 7% to 42% with just that one change.
[MUSIC]
Research was done looking at organizations, in fact,
there were three tech organizations and one consulting firm.
And the researchers got access to all of the performance reviews from those four
companies.
And they did an analysis of how did those reviews differ by gender, and if so, how.
What they found was, is that the majority of the reviews
of males as compared to females actually
associated the review with a tangible business related outcome.
So the men were told how well or
poorly they did on something that mattered to the organization.
A real business outcome.
What was interesting is, for the females, they were mostly given vague feedback.
You're doing okay.
You're not doing well.
Maybe you need to have this, but there was no precision in that feedback.
And of the women who were given more precise feedback,
what was also true was it was mostly about their communication style.
And this is the fact the wayI think is the most telling.
And looking at all these performance reviews 75%
of the individuals in these reviews who were judged to be aggressive.
And that aggression came out in their performance review were women.
Seriously?
Of all the people in these organizations,
the problem they were having with aggression was because of the females?
Or maybe it's because we have a different standard for
what we adjudicate as aggression in women versus aggression in men.
There was a study in a medical school that looked at the letters of recommendation
for their students who were going out into residency positions.
And they found very interesting results.
Number one, the letters of the male students were much longer
then those of the female.
And the letters of the male students talked about their accomplishments and
their potential.
While the letters of the female students talked about their personality.
[MUSIC]
Think about an organization whose culture is strongly
committed to meritocracy and data-driven decisions.
You’d think in that type of organization a lot of these problems would go away,
but it turns out just the opposite is true.
When an organization has such a strong adherence to meritocracy and
data-driven decisions.
And their managers and leaders, because of their association with the organization,
or by explicit statement, agree with that.
It turns out those folks are actually more likely to discriminate against women, and
people of color in their compensation decisions, why?
Based on paradoxical, but
it turns out that this is called the moral licensing effect.
So have you ever been a situation?
Well, happens to me all the time.
I go to work out and then because I've worked out really hard I now
give myself permission to have that piece of chocolate cake.
That's called moral licensing.
I've already proved that I'm a good person.
I worked out and now, [SOUND] I can eat my cake.
If I'm a manager in an organization that has such a strong notion of meritocracy.
Then by definition of being in that organization, I lower my guard
about whether or not my individual decision are consistent with that,
because this is part of this organization.
It's our DNA, so of course we focus on merit and data.
This is the problem, organizations and individuals need to be eternally vigilant.
We cannot just assume that that problem has passed, and we have solved the problem
because we care about data or that we care about meritocracy.
We have to assess, are the decisions that I'm making consistent with the data?
Not my perceptions, not my biases, but the data.
And more importantly, do I every day make sure that I actually
broaden the potential of who I might consider for jobs, rather than narrow?
Am I taking responsibility?
When I see good performance, do I let other people know?
Or do I just let that good performance speak for
itself, assuming of course everyone else will see it.
So what is my contribution to the lack of diversity in my organization?
And that's something that everyone of us needs to think about because
it is up to us to change.
[MUSIC]