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B E H A V I O R   &   S O C I E T Y

How to Think about "Implicit Bias"

Amidst a controversy, it’s important to remember that implicit bias is real—and it matters

By Keith Payne, Laura Niemi, John M. Doris on March 27, 2018

Credit: theprint Getty Images

When is the last time a stereotype popped into your mind? If you are like most

people, the authors included, it happens all the time. That doesn’t make you a racist,

sexist, or whatever-ist. It just means your brain is working properly, noticing

patterns, and making generalizations. But the same thought processes that make

people smart can also make them biased. This tendency for stereotype-confirming

thoughts to pass spontaneously through our minds is what psychologists call implicit

bias. It sets people up to overgeneralize, sometimes leading to discrimination even

when people feel they are being fair.

Studies of implicit bias have recently drawn ire from both right and left. For the right,

talk of implicit bias is just another instance of progressives seeing injustice under

every bush. For the left, implicit bias diverts attention from more damaging instances

of explicitbigotry. Debates have become heated, and leapt from scientific journals to

the popular press. Along the way, some important points have been lost. We highlight

two misunderstandings that anyone who wants to understand implicit bias should

know about.

First, much of the controversy centers on the most famous implicit bias test, the

Implicit Association Test (IAT). A majority of people taking this test show evidence of

implicit bias, suggesting that most people are implicitly biased even if they do not

think of themselves as prejudiced. Like any measure, the test does have limitations.

The stability of the test is low, meaning that if you take the same test a few weeks

apart, you might score very differently. And the correlation between a person’s IAT

scores and discriminatory behavior is often small.

The IAT is a measure, and it doesn’t follow from a particular measure being flawed

that the phenomenon we’re attempting to measure is not real. Drawing that

conclusion is to commit the Divining Rod Fallacy: just because a rod doesn’t find

water doesn’t mean there’s no such thing as water. A smarter move is to ask, “What

does the other evidence show?”

In fact, there is lots of other evidence. There are perceptual illusions, for example, in

which white subjects perceive black faces as angrier than white faces with the same

expression. Race can bias people to see harmless objects as weapons when they are in

the hands of black men, and to dislike abstract images that are paired with black

faces. And there are dozens of variants of laboratory tasks finding that most

participants are faster to identify bad words paired with black faces than white faces.

None of these measures is without limitations, but they show the same pattern of

reliable bias as the IAT. There is a mountain of evidence—independent of any single

test—that implicit bias is real.

The second misunderstanding is about what scientists mean when they say a measure

predicts behavior. It is frequently complained that an individual’s IAT score doesn’t

tell you whether they will discriminate on a particular occasion. This is to commit the

Palm Reading Fallacy: unlike palm readers, research psychologists aren’t usually in

the business of telling you, as an individual, what your life holds in store. Most

measures in psychology, from aptitude tests to personality scales, are useful for

predicting how groups will respond on average, not forecasting how particular

individuals will behave.

The difference is crucial. Knowing that an employee scored high on conscientiousness

won’t tell you much about whether her work will be careful or sloppy if you inspect it

right now. But if a large company hires hundreds of employees who are all

conscientious, this will likely pay off with a small but consistent increase in careful

work on average.

Implicit bias researchers have always warned against using the tests for predicting

individual outcomes, like how a particular manager will behave in job interviews—

they’ve never been in the palm-reading business. What the IAT does, and does well, is

predict average outcomes across larger entities like counties, cities, or states. For

example, metro areas with greater average implicit bias have larger racial disparities

in police shootings. And counties with greater average implicit bias have larger racial

disparities in infant health problems. These correlations are important: the lives of

black citizens and newborn black babies depend on them.

Field experiments demonstrate that real-world discrimination continues, and is

widespread. White applicants get about 50 percent more call-backs than black

applicants with the same resumes; college professors are 26 percent more likely to

respond to a student’s email when it is signed by Brad rather than Lamar; and

physicians recommend less pain medication for black patients than white patients

with the same injury.

Today, managers are unlikely to announce that white job applicants should be chosen

over black applicants, and physicians don’t declare that black people feel less pain

than whites. Yet, the widespread pattern of discrimination and disparities seen in

field studies persists. It bears a much closer resemblance to the widespread

stereotypical thoughts seen on implicit tests than to the survey studies in which most

people present themselves as unbiased.

One reason people on both the right and the left are skeptical of implicit bias might be

pretty simple: it isn’t nice to think we aren’t very nice. It would be comforting to

conclude, when we don’t consciously entertain impure intentions, that all of our

intentions are pure. Unfortunately, we can’t conclude that: many of us are more

biased than we realize. And that is an important cause of injustice—whether you know

it or not.

ABOUT THE  AUTHOR(S)

Keith Payne

Keith Payne is a Professor of Psychology and Neuroscience at UNC Chapel Hill. He studies

implicit bias and the psychological effects of inequality.

Laura Niemi

Laura Niemi is a Postdoctoral Fellow in the Department of Philosophy and the Center for

Cognitive Neuroscience at Duke University and an Affiliate of the Department of Psychology at

Harvard University. She studies moral judgment and the implications of differences in moral

values.

John M. Doris

John M. Doris is Professor in the Philosophy–Neuroscience–Psychology Program and

Philosophy Department, Washington University in St. Louis. He works at the intersection of

cognitive science, moral psychology, and philosophical ethics.