Courtland Milloy, the Metro editor of the Washington Post, admits that he’s racially biased.
So now it can be told:
“Your data suggest a strong automatic preference for Black relative to White,” the summary of my test results said.
For some readers, no doubt, this is confirmation — if any was needed — that I am a “reverse racist.” But the last thing I wanted was to end up in that group of African Americans who showed a pro-white, or anti-black, bias. I’m talking about 48 percent of black test takers who have internalized the same biases as a majority of white people: Black is bad; white is good.
The test (which can be found here) to which Milloy refers was developed by a Harvard psychologist to detect hidden, deep-seated, “implicit” bias. As described in an article in the Washington Post magazine last Sunday,
By linking together words and images, the race bias test measures what associations come most easily to mind. People who take the Web version are asked to classify a series of faces into two categories, black American and white American. Then they are asked to mentally associate the white and black faces with words such as ‘joy’ and ‘failure.’ Under time pressure, many Americans find it easier to group words such as ‘failure’ with black faces, and words such as ‘joy’ with white faces.
Actually, Milloy passes quickly over his own biases, as well he should since the virtue of this test (if virtue it is) is that it finds bias in just about everyone. As the blurb under the headline of the Sunday article puts it,
Many Americans believe they are not prejudiced. Now a new test provides powerful evidence that a majority of us really are.
That’s probably an understatement. As the article reports, based on those taking the test at the web site linked above,
88 percent of white people had a pro-white or anti-black implicit bias; nearly 83 percent of heterosexuals showed implicit biases for straight people over gays and lesbians; and more than two-thirds of non-Arab, non-Muslim volunteers displayed implicit biases against Arab Muslims.
The article begins — and you can get a good sense of the test from this beginning — by describing the test experience of a young woman in Washington who recently took the test.
Her office decor attested to her passion for civil rights — as a senior activist at a national gay rights organization, and as a lesbian herself, fighting bias and discrimination is what gets her out of bed every morning. A rainbow flag rested in a mug on her desk.
The woman brought up a test on her computer from a Harvard University Web site. It was really very simple: All it asked her to do was distinguish between a series of black and white faces. When she saw a black face she was to hit a key on the left, when she saw a white face she was to hit a key on the right. Next, she was asked to distinguish between a series of positive and negative words. Words such as “glorious” and “wonderful” required a left key, words such as “nasty” and “awful” required a right key. The test remained simple when two categories were combined: The activist hit the left key if she saw either a white face or a positive word, and hit the right key if she saw either a black face or a negative word.
Then the groupings were reversed. The woman’s index fingers hovered over her keyboard. The test now required her to group black faces with positive words, and white faces with negative words. She leaned forward intently. She made no mistakes, but it took her longer to correctly sort the words and images.
Her result appeared on the screen, and the activist became very silent. The test found she had a bias for whites over blacks.
“It surprises me I have any preferences at all,” she said. “By the work I do, by my education, my background. I’m progressive, and I think I have no bias. Being a minority myself, I don’t feel I should or would have biases.”
Although the activist had initially agreed to be identified, she and a male colleague who volunteered to take the tests requested anonymity after seeing their results. The man, who also is gay, did not show a race bias. But a second test found that both activists held biases against homosexuals — they more quickly associated words such as “humiliate” and “painful” with gays and words such as “beautiful” and “glorious” with heterosexuals.
If anything, both activists reasoned, they ought to have shown a bias in favor of gay people. The man’s social life, his professional circle and his work revolve around gay culture. His home, he said, is in Washington’s “gayborhood.”
“I’m surprised,” the woman said. She bit her lip. “And disappointed.”
One more example: Saj-Nicole Joni was the first woman hired in applied mathematics at MIT. She was a very successful pioneer in the struggle against bias against women in science.
[Her] test came up on the screen. Joni’s fingers, trained for many years on the piano, flew as she classified a number of words such as “husband,” “father,” “mother” and “wife” between “male” and “female” groups. She then grouped words such as “chemistry,” “history,” “astronomy” and “music” under “science” or “liberal arts.” The computer then asked her to group “male” with “science” and “female” with “liberal arts.”
When the groupings were reversed, Joni had to group “male” words with “liberal arts,” and “female” words with various disciplines in science. She made a mistake in classifying “uncle.” She hesitated over “astronomy” and made a second mistake in classifying “physics.”
The results popped up: “Your data show a strong association between science and Male relative to Female.”
Joni’s fingers tapped the table in frustration. “I fought for women to be scientists all my life,” she said, incredulous
I will leave it to others more versed in psychology and testing than I to comment on the validity, if any, of this test. I certainly would not say that its apparent findings are meaningless or even uninteresting. But I will say that it seems to me these findings have less to do with “bias,” at least as usually understood, than most of those who comment on it, like Milloy, obviously believe.
Women now constitute nearly half the labor force (46.8 percent in 2003), but they represent only 9 percent of civil engineers, 11 percent of aerospace engineers, 6 percent of mechanical engineers, and 8 percent of physicists and astronomers.
Given this reality, does associating scientific careers with men really reflect bias against women? I don’t think so.
Even more far-fetched are the policy implications that are commonly drawn from this implausible notion of pervasive bias. Listen, for example, to Courtland Milloy today:
Last year, U.S. Supreme Court Justice Sandra Day O’Connor predicted that affirmative action for minorities would no longer be needed in 25 years. Judging from the MIT study [of alleged discrimination against job applicants with black-sounding names], the only affirmative action currently being practiced is for whites.
The Implicit Association Test exposes bias that is deep-seated.
Preferences, in short, need to remain in effect until all our “implicit” bias has been eradicated. But if that is so, then all organizations where people like Milloy make the employment or admissions decisions should be extending preferences to whites.