Chat with us, powered by LiveChat 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 | EssayAbode

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.

DIVERSITY

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

Gender Research.

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

This article is about DIVERSITY

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9/15/2020 The Problem with Implicit Bias Training :: Reader View

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www.scientificamerican.com /article/the-problem-with-implicit-bias-training/

The Problem with Implicit Bias Training

Tiffany L. Green,Nao Hagiwara

7-8 minutes

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

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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

Nao Hagiwara, Ph.D., is an associate professor in the Department of Psychology at Virginia Commonwealth University.

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