shown with varying methodologies
and in varying geographies, to a degree
that demands attention.
Challenges Faced by Diverse Teams
Unfortunately, it is not easy to make
diverse teams effective.a There are a
number of forces that work against the
desired effect: having the entire team
productive. There can be potential negative effects of any of the following:
˲ unconscious bias,
˲ stereotype threat,
˲ exclusion from critical social networks,
˲ lack of role models, and
˲ unaware managers.
The following sections address
primarily unconscious bias and stero-type threat.
Unconscious bias. One of the factors
that both limits the diversity present
in a team or organization, and that inhibits the potential success of diverse
teams, is the unconscious bias, that
is, stereotypes, that we hold toward
people based on the social groups to
which they belong. Stereotypes are,
simply, a constellation of traits, characteristics, skills, and values that we
ascribe to members of social groups,
such as gender, race, age, religion,
nationality, and others. These are
learned through cultural messages
and stories, comments from family
and friends, portrayals in the media,
and so forth. These stereotypes, despite our best intentions, can bias our
impressions of, and affect our actions
toward, others in our environment.
The stereotypes especially relevant in
work situations include those characteristics that are visible, such as sex,
race, weight, and age; but also those
not visible but relatively easy to discern, such as educational background
Project Implicit at Harvard hosts
an online test of implicit associa-
a Many studies referenced here refer to wom-
en, but these results should largely be con-
sidered to apply across all axes of diversity;
for example, gender, cultural and national
origin, sexual orientation, age, educational
background, religion, and other life experi-
ences. It is more difficult to study differences
that are not externally visible, such as differ-
ences in economic class, than visible differ-
ences like gender or race, but these less-visi-
ble differences are also important to consider
in conversations about diversity.
tions: the user’s implicit association
between two concepts is measured via
user response time. There are reports
for many associations, and the results
˲ Almost everyone has measurable
biases (for example, 70%–80% have
biases against women in technology,
or preferring white to African Ameri-
can, or preferring young people).
˲ Almost no one reports such biases
(for example, 15% report a preference
for white people).
˲ Even the people who are the sub-
ject of a bias may have that bias. For
example, I tested as moderately bi-
ased against women in science and
technology, and this is totally against
The book Blindspot5 explains the
development of the implicit asso-
ciation test and its results, exploring
the reasons for differences between
unconscious perception and our as-
sumptions. For example, it has been
found that age is one of the strongest
biases tested; this is true even among
the elderly, and in societies in Asia that
traditionally have valued the wisdom
of age and experience.
5 Blindspot also
considers the evidence for any predic-
tive link between measured biases and
outcomes or behaviors; results are not
yet conclusive in this area.
In individual cases, bias can be
very difficult to verify. In aggregate,
however, bias and other forms of un-
conscious decision-making are read-
˲ About 58% of CEOs of Fortune 500
companies are taller than 6ft. (about
183cm), and almost a third are taller
than 6ft. 2in. (about 188cm). In the
population in general, about 14.5%
are taller than 6ft., and 3.9% taller
than 6ft. 2in.
17 Most people would not
believe height predicts competence,
and yet, these choices are frequently
made. In fact, height is strongly cor-
related with career success.
˲ In a laboratory situation, applicants were seen in the waiting room
either alone, or sitting next to another applicant. Applicants who were
seen sitting next to an overweight
applicant were less likely to be hired
than an applicant sitting alone or
next to an average weight applicant,
Figure 3. Collective Intelligence vs. Team Composition.
0 50 100
The horizontal axis indicates team composition. The blue circles represent
averages for each percentage level; the red bars indicate standard deviation.
Courtesy of Woolley and Malone.