11 Jun We all have them! Describe types of bias that you exhibit and would like to reduce in your everyday life and in the workplace. What are some strategies that can
We all have them! Describe types of bias that you exhibit and would like to reduce in your everyday life and in the workplace. What are some strategies that can be used to effectively reduce these types of bias? Your post should include (1) bias you notice in your everyday life (2)bias you notice in the workplace [this can be one you or someone else exhibits] (3)strategies to reduce bias. Can you use the same strategy to reduce different types of bias? Refer to this week's materials for ideas.
Two Powerful Ways Managers Can Curb Implicit Biases by Lori Mackenzie and Shelley Correll
OCTOBER 01, 2018
PATRIK STOLLARZ/GETTY IMAGES
Many managers want to be more inclusive. They recognize the value of inclusion and diversity
and believe it’s the right thing to aspire to. But they don’t know how to get there.
For the most part, managers are not given the right tools to overcome the challenges posed by
implicit biases. The workshops companies invest in typically teach them to constantly check
their thoughts for bias. But this demands a lot of cognitive energy, so over time, managers go
back to their old habits.
Based on our work at the Stanford Women’s Leadership Lab, helping organizations across many
industries become more diverse and inclusive, our research shows there are two, small — but
more powerful — ways managers can block bias: First, by closely examining and broadening their
definitions of success, and second, by asking what each person adds to their teams, what we call
their “additive contribution.”
The problem is that, when hiring, evaluating, or promoting employees, we often measure people
against our implicit assumptions of what talent looks like — our hidden “template of success.”
These templates potentially favor one group over others, even if members of each group were
equally likely to be successful.
Take, for example, the hiring process. While interviewing a candidate, we might ask her where
she went to school or to share her experiences. We genuinely believe we are gathering relevant
information that will help us decide objectively whether the person is a good fit for the job. But,
in fact, we are likely measuring that person against our hidden “template.” Did the person go to
the “right” school? Are her experiences similar to ours? Is her personality a close match with that
of the other employees on the team?
Not surprisingly, most managers end up hiring people who match their implicit template of
success. Now, this approach may seem like a recipe for sound decision-making. Wouldn’t those
people work best with the hiring manager and fit in with the rest of the team? Perhaps.
But this approach can pose a serious problem: Even if we want to be inclusive, the template itself
may inadvertently invite bias by giving preference to more traditional candidates or “the safe
bet.” In finance, for example, that might mean believing — based on no evidence — that only MBA
graduates from an elite university are likely to succeed at their jobs. Even if we apply that criteria
fairly to every candidate, it can lead to an implicit preference for hiring white males. After all, 60
to 70% of graduates of elite MBA programs are male — and very few are people of color.
Take another example: In positions that demand skills for working in an open-source context, our
hidden template of success might lead us to believe, again with no evidence, that only someone
who is already part of the open-source community can do the job well. This narrow definition,
however, will result in the same kind of candidate being picked over and over. Those who
volunteer in the open-source community often do so outside and beyond their paid “day” job
hours, which pretty much excludes people with care-giving roles and other responsibilities
outside of work. As a result, open-source communities are typically 3 to 5% women and mostly
younger men. You can see how replicating the template of success can quickly translate into
sameness. And sameness blocks performance and innovation.
Diversity, on the other hand, spurs innovation. In research spanning decades, Columbia professor
Katherine Phillips has repeatedly found that, when tasked to innovate, teams that include diverse
members and that value the contributions of all their members outperform homogenous teams.
When working across difference, Phillips finds that team members work harder. They have to in
order to communicate and to reach consensus with others who may not share the same
experiences or perspectives. This makes all members of the team think more deeply and arrive at
better decisions. Diversity, as Phillips writes, “makes us smarter.”
The Power of Additive Contribution
To block our implicit biases, we need to challenge the assumptions behind our templates for
success. We need to ask if the criteria used to evaluate candidates will lead us to choose
employees who will add to our team success or simply replicate the status quo. For example, is an
MBA from a top business school really necessary to be successful in this position? It may be, or
maybe we’re privileging some criteria without evidence that they are necessary for success. We
need to ask questions that help us determine how a person adds to the portfolio of experiences
and skills across our entire team.
Focusing on additive contribution, a term we developed in a collaboration with Alix Hughes,
diversity program leader at Amazon, is a powerful way to avoid sameness in a team and to foster
inclusion and innovation. When we consider other’s additive contributions, we open the door to
people who might not traditionally match our implicit template of success, that are not like “us.”
We make our teams more diverse and more successful.
So how can you ask questions that help you determine someone’s additive contribution? Here
are four ways:
Clarify ambiguous criteria for success. First ask, “What are my hidden ‘preferences?’” Then
challenge your hidden preferences by asking what are the mindsets, skills, and diverse
experiences that actually lead your team to success. This may make you more effective at hiring
people who will thrive in your organization. Instead of asking about prior open-source
experience, for example, you might seek someone who can discuss critical points effectively and
respectfully in an environment of open debate.
Focus on a person’s value to your team. Ask, “How does this person’s approach help us get to
better discussions and decisions?” or “Does this person help me see outside my ‘box’?” Professor
Mary Murphy, an expert on growth mindsets in organizations, offered this question: “How can
[or does] this person add to the total value (composition) of our team?” By asking questions like
these, you are more likely to move beyond your hidden template of success and avoid any
implicit bias that might come along with it.
Run a gap analysis. Ask, “What skills and experiences am I missing on my team that this person
has?” Be careful not to focus on one-dimensional characteristics. For example, don’t determine
you need “a woman to round out the team.” Diversity for diversity’s sake often leads others to
make negative assumptions about your people decisions — and about those you hire or promote.
Criteria still matter. Instead, look at how people can add to the total portfolio of mindsets, skills,
and experiences on the team.
Consider their journey. Ask, “What has this person learned from her experiences? Can she take
risks and persevere through difficulties?” We often perceive being quickly promoted as an
indicator of someone’s talent. But using this criteria might lead you to overlook the value of grit
and perseverance. If a person took a risk and it did not pay off, for example, they may have
learned more than a person who took a safer path. The lessons people learn throughout their
careers are often the key to uncovering their additive contribution.
Small Wins, Big Payoff
In 2016 Anton Hanebrink, Intuit’s Chief Corporate Strategy and Development Officer, took over a
high-performing team known for its contributions to the direction of the company. The team’s
historical approach to finding top talent had been simple — target graduates of top universities
and MBA programs with experience at leading management consulting firms or investment
banks. While these filters simplified the screening process, they also led to a relatively
homogenous way of viewing the world.
Seeking to find a better way he pushed his team to broaden how they thought about top
talent. The breakthrough for Anton and his team came during an offsite we facilitated for the
company on implicit biases in criteria, such as only hiring people from elite universities. The
company’s CFO asked a crowd of the company’s most accomplished finance leaders to raise their
hand if they had attended an Ivy League school. Hardly anyone raised their hand.
Seizing on the moment, Anton pushed his team to examine this historical criterion more
closely. His team discovered it was not an effective marker of how well the person would perform
in the organization. It was, indeed, just a hidden preference. In reality, many of the top
performers at Intuit, including the CEO and CFO, did not hold degrees from an Ivy League school.
Energized by Anton’s charge, the team worked together to define the skills, experiences, and
mindsets that actually were necessary to succeed in the team. They identified the abilities to
structure ambiguous problems, influence change at senior levels, and to effectively develop team
members as the key contributions an incoming executive should add to the team. None of these
abilities would be guaranteed by a credential earned sometimes decades ago.
After reconsidering their template of success, the team’s approach to hiring changed significantly.
They especially improved how they interviewed candidates, engaging them more deeply and
thoughtfully on the core skills of the job than they had in the past. They even went so far as
redact the names of schools and prior employers during the interview process.
As a result, the team hired top talent whose diverse backgrounds have added to their total
portfolio of skills. Anton’s team achieved more gender and racial diversity as well. By redefining
success, a greater diversity of people were able to be seen for their leadership. The breadth of
talent has led to a more rigorous debate of ideas and enabled the team to navigate new business
opportunities and identify critical strategic insights they would have missed with their old
approach to recruiting talent.
That’s the power of reexamining our assumptions and considering people’s additive
contributions. They constitute small changes on our part, but the payoff is significant.
Lori Mackenzie is Executive Director of the Clayman Institute at Stanford University and co-founder of Stanford VMware Women’s Leadership Innovation Lab.
Shelley Correll is professor of sociology and organizational behavior at Stanford University, Director of the Stanford VMware Women’s Leadership Innovation Lab, and the Barbara D. Finberg Director of the Clayman Institute for
Related Topics: D E C I S I O N M A K I N G | M A N A G I N G P E O P L E | P E R S O N N E L P O L I C I E S
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9/15/2020 The Problem with Implicit Bias Training :: Reader View
The Problem with Implicit Bias Training
Tiffany L. Green,Nao Hagiwara
It’s well motivated, but there’s little evidence that it leads to meaningful changes in behavior
Credit: Nicola Katie Getty Images
While the nation roils with ongoing protests against police violence and persistent societal racism, many organizations have released statements promising to do better. These promises often include improvements to hiring practices; a priority on retaining and promoting people of color; and pledges to better serve those people as customers and clients.
As these organizations work to make good on their declarations, implicit bias training is often at the top of the list. As the thinking goes, these nonconscious prejudices and stereotypes are spontaneously and automatically activated and may inadvertently affect how white Americans see and treat Black people and other people of color. The hope is that, with proper training, people can learn to recognize and correct this damaging form of bias.
In the health care industry, implicit bias is among the likely culprits in many persistent racial and ethnic disparities, like infant and maternal mortality, chronic diseases such as diabetes, and more recently, COVID-19. Black Americans are about 2.5 times more likely to die from COVID-19 relative to whites, and emerging data indicate that Native Americans are also disproportionately suffering from the pandemic. Implicit biases may impact the ways in which clinicians and other health care professionals diagnose and treat people of color, leading to worse outcomes. In response to these disparities, Michigan and California have mandated implicit bias training for some health professionals.
There’s just one problem. We just don’t have the evidence yet that implicit bias training actually works.
To be sure, finding ways to counter unfair treatment is critical. The evidence is clear that implicit prejudice, an affective component of implicit bias (i.e., feeling or emotion) exists among health care providers with respect to Black and/or Latinx patients, as well as to dark-skinned patients not in those categories. In turn, these biases lower the quality of patient-provider communication and result in lower satisfaction with the healthcare encounter.
But while implicit bias trainings are multiplying, few rigorous evaluations of these programs exist. There are exceptions; some implicit bias interventions have been conducted empirically among health care professionals and college students. These interventions have been proven to lower scores on the Implicit Association Test (IAT), the most commonly used implicit measure of prejudice and stereotyping. But to date, none of these interventions has been shown to result in permanent, long-term reductions of implicit bias scores or, more importantly, sustained and meaningful changes in behavior (i.e., narrowing of racial/ethnic clinical treatment disparities).
9/15/2020 The Problem with Implicit Bias Training :: Reader View
Even worse, there is consistent evidence that bias training done the “wrong way” (think lukewarm diversity training) can actually have the opposite impact, inducing anger and frustration among white employees. What this all means is that, despite the widespread calls for implicit bias training, it will likely be ineffective at best; at worst, it’s a poor use of limited resources that could cause more damage and exacerbate the very issues it is trying to solve.
So, what should we do? The first thing is to realize that racism is not just an individual problem requiring an individual intervention, but a structural and organizational problem that will require a lot of work to change. It’s much easier for organizations to offer an implicit bias training than to take a long, hard look and overhaul the way they operate. The reality is, even if we could reliably reduce individual-level bias, various forms of institutional racism embedded in health care (and other organizations) would likely make these improvements hard to maintain.
Explicit, uncritical racial stereotyping in medicine is one good example. We have known for many years that race is a social construct rather than a proxy for genetic or biological differences. Even so, recent work has identified numerous cases of race-adjusted clinical algorithms in medicine. In nephrology, for example, race adjustments that make it appear as if Black patients have better kidney function than they actually do can potentially lead to worse outcomes such as delays in referral for needed specialist care or kidney transplantation. Other more insidious stereotyping characterizes Native Americans and African Americans as more likely to be “noncompliant” with diet and lifestyle advice. These characterizations of noncompliance as a function of attitudes and practices completely ignore structural factors such as poverty, segregation and marketing—factors that create health inequities in the first place.
Meaningful progress at the structural and institutional levels takes longer than a few days of implicit bias training. But there are encouraging examples of individuals who have fought successfully for structural change within their health care organizations. For example, medical students at the University of Washington successfully lobbied for race to be removed as a criterion for determining kidney function— a process that took many years. Their success may have important implications for closing gaps in disparities among patients with renal disease. And innovative new programs like the Mid-Ohio Farmacy have linked health care providers with community-based organizations, and help providers address food insecurity among their low-income patients—an issue that disproportionately impacts people of color. (Doctors can write a “food prescription” that allows their patients to purchase fresh produce.)
None of this, of course, means that we should give up on trying to understand implicit bias or developing evidence-based training that successfully reduces discriminatory behaviors at the individual level. What it does mean is that we need to lean into the hard work of auditing long-standing practices that unfairly stigmatize people of color and fail to take into account how health inequities evolve. Creating organizations that value equity and ultimately produce better outcomes for people of color will be long, hard work, but it’s necessary and it’s been a long time coming.
Celebrating 175 Years of Discovery
ABOUT THE AUTHOR(S)
Tiffany L. Green
Tiffany L. Green, Ph.D. is an assistant professor in the Department of Population Health Sciences and the Department of Obstetrics and Gynecology at the University of Wisconsin-Madison.
Nao Hagiwara, Ph.D., is an associate professor in the Department of Psychology at Virginia Commonwealth University.