To voice these realities, however, is to commit a microaggression, according to University of California diversity enforcers. One handout inflicted on “Fostering Inclusive Excellence” attendees presents a long list of microaggressions, categorized by “Theme” and “Message.” The “Myth of Meritocracy” “theme” includes such statements as: “Of course he’ll get tenure, even though he hasn’t published much—he’s Black!” The “message” conveyed by this particular microaggression, according to UC’s “Recognizing Microaggressions Tool,” is that “people of color are given extra unfair benefits because of their race.” Now where would anyone get that idea? Well, you might ask any high school senior, steeped in his class’s SAT rankings, if it’s true that “people of color” are given “extra benefits” in college admissions. He will laugh at your naïveté. A 2004 study of three top-tier universities, published in Social Science Quarterly, found that blacks were favored over whites by a factor of 5.5 and that being black got students an extra 230 SAT points on a 1600-point scale. Such massive preferences are found at every selective college and graduate school. Every student knows this, and yet diversity protocol requires pretending that preferences don’t exist. The race (and gender) advantage continues into the academic workplace, as everyone who has sat on a hiring committee also knows.
Other alleged microaggressions include uttering such hurtful words as “I believe the most qualified person should get the job” or “America is the land of opportunity.” Someone who has been through the “Fostering Inclusive Excellence” seminar may call you out for giving voice to such ideas. Why, exactly, saying that the most qualified person should get the job is a microaggression is a puzzle. Either such a statement is regarded simply as code for alleged antiblack sentiment, or the diversocrats are secretly aware that meritocracy is incompatible with “diversity.”
Equally “hostile” and “derogatory,” according to the “Tool,” is the phrase “Everyone can succeed in this society, if they work hard enough.” Such a statement is obviously an insult to all those career victims whose primary occupation is proclaiming their own helplessness and inability to accomplish anything without government assistance.
Many purported microaggressions arise from the contradictions in diversity ideology. Authorities in a diversity regime are supposed to categorize people by race and ethnicity—until that unpredictable moment when they are not supposed to. Assigning a black graduate student to escort a black visiting professor, for example, is a microaggression, per the “Tool.” But wasn’t the alleged need for role models and a critical mass of “persons of color” a key justification for “diversity”? Describing a colleague as a “good Black scientist” is another microaggression. But such a categorization merely reflects the race-consciousness and bean counting that the campus diversity enforcers insist upon.
Color blindness constitutes an entire microaggression “Theme” in the “Tool,” pace Martin Luther King Jr. Beware of saying, “When I look at you, I don’t see color” or “There is only one race, the human race.” Doing so, according to the “Tool,” denies “the individual as a racial/cultural being.” Never mind that diversity ideologues reject the genetic basis of racial categories and proclaim that race is merely a “social construct.” The nondiverse world is under orders both to deny that race exists and to “acknowledge race,” in Tool-parlance, regarding Persons of Color.
Other microaggressions provided a glimpse into the future. It may have seemed like a stretch in 2015 to label as a microaggression “being forced to choose Male or Female when completing basic forms.” By 2018, however, the movement to discredit binary, biological sex distinctions had accelerated to the point that many institutions could expect media denunciation if they did not allow their members to choose from an array of “gender” possibilities and combinations.
The ultimate question raised by the “Fostering Inclusive Excellence” seminar was: Are there any grown-ups left on campus, at least in administrative offices? And the answer is: no. The most disturbing aspect of the exercise is that it was initiated by the president’s office without outside provocation. Had Napolitano not come up with these antibias trainings, no one would have noticed their absence. Instead, she promulgated sua sponte an initiative deeply ignorant about how seriously most professors—at least in the sciences—take their responsibilities to build up a faculty of accomplishment and research prowess. We have come to expect such ignorance from coddled, self-engrossed students. Now it turns out that those students may be the least of the university’s problems.
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5
ARE WE ALL UNCONSCIOUS RACISTS?
Few academic ideas have been as eagerly absorbed into public discourse in recent years as “implicit bias.” Embraced by a president, a would-be president, and the nation’s top law-enforcement official, the implicit-bias conceit has launched a movement to remove the concept of individual agency from the law and spawned a multimillion-dollar consulting industry. The statistical basis on which it rests is now crumbling, but don’t expect its influence to wane anytime soon.
Implicit bias purports to answer the question: Why do racial disparities persist in household income, job status, and incarceration rates, when explicit racism has, by all measures, greatly diminished over the last half-century? The reason, according to implicit-bias researchers, lies deep in our brains, outside the reach of conscious thought. We may consciously embrace racial equality, but almost all of us harbor unconscious biases favoring whites over blacks, the proponents claim. And those unconscious biases, which the implicit-bias project purports to measure scientifically, drive the discriminatory behavior that, in turn, results in racial inequality.
The need to plumb the unconscious to explain ongoing racial gaps arises for one reason: It is taboo in universities and mainstream society to acknowledge intergroup differences in interests, abilities, cultural values, or family structure that might produce socioeconomic disparities.
The implicit-bias idea burst onto the academic scene in 1998 with the rollout of a psychological instrument called the Implicit Association Test (IAT). Created by social psychologists Anthony Greenwald and Mahzarin Banaji, with funding from the National Science Foundation and National Institute of Mental Health, the IAT was announced as a breakthrough in prejudice studies: “The pervasiveness of prejudice, affecting 90 to 95 percent of people, was demonstrated today … by psychologists who developed a new tool that measures the unconscious roots of prejudice,” read the press release.
The race IAT (there are nonrace varieties) displays a series of black faces and white faces on a computer; the test subject must sort them quickly by race into two categories, represented by the “i” and “e” keys on the keyboard. Next, the subject sorts “good” or “positive” words like “pleasant,” and “bad” or “negative” words like “death,” into good and bad categories, represented by those same two computer keys. The sorting tasks are then intermingled: faces and words appear at random on the screen, and the test-taker has to sort them with the “i” and “e” keys. Next, the sorting protocol is reversed. If, before, a black face was to be sorted using the same key as the key for a “bad” word, now a black face is sorted with the same key as a “good” word and a white face sorted with the reverse key. If a subject takes longer sorting black faces using the computer key associated with a “good” word than he does sorting white faces using the computer key associated with a “good” word, the IAT deems the subject a bearer of implicit bias. The IAT ranks the subject’s degree of implicit bias based on the differences in milliseconds with which he accomplishes the different sorting tasks; at the end of the test, he finds out whether he has a strong, moderate, or weak “preference” for blacks or for whites. A majority of test-takers (including many blacks) are rated as showing a preference for white faces. Additional IATs sort pictures of women, the elderly, the disabled, and other purportedly disfavored groups.
Greenwald and Banaji did not pioneer such response-time stud
ies; psychologists already used response-time methodology to measure how closely concepts are associated in memory. And the idea that automatic cognitive processes and associations help us navigate daily life is also widely accepted in psychology. But Greenwald and Banaji, now at the University of Washington and Harvard University, respectively, pushed the response-time technique and the implicit-cognition idea into charged political territory. Not only did they confidently assert that any differences in sorting times for black and white faces flow from unconscious prejudice against blacks; they also claimed that such unconscious prejudice, as measured by the IAT, predicts discriminatory behavior. It is “clearly … established that automatic race preference predicts discrimination,” they wrote in their 2013 bestseller Blindspot, which popularized the IAT. And in the final link of their causal chain, they hypothesized that this unconscious predilection to discriminate is a cause of racial disparities: “It is reasonable to conclude not only that implicit bias is a cause of Black disadvantage but also that it plausibly plays a greater role than does explicit bias in explaining the discrimination that contributes to Black disadvantage.”
The implicit-bias conceit spread like wildfire. President Barack Obama denounced “unconscious” biases against minorities and females in science in 2016. NBC anchor Lester Holt asked Hillary Clinton during a September 2016 presidential debate whether “police are implicitly biased against black people.” Clinton answered: “Lester, I think implicit bias is a problem for everyone, not just police.” Then–FBI director James Comey claimed in a 2015 speech that “much research” points to the “widespread existence of unconscious bias.” “Many people in our white-majority culture,” Comey said, “react differently to a white face than a black face.” The Obama Justice Department packed off all federal law-enforcement agents to implicit-bias training. Clinton promised to help fund it for local police departments, many of which had already begun the training following the 2014 Ferguson, Missouri, police shooting of Michael Brown.
A parade of journalists confessed their IAT-revealed preferences, including Malcolm Gladwell in his acclaimed book Blink. Corporate diversity trainers retooled themselves as purveyors of the new “science of bias.” And the legal academy started building the case that the concept of intentionality in the law was scientifically obtuse. Leading the charge was Jerry Kang, a UCLA law professor in the school’s critical race studies program who became UCLA’s fantastically paid vice chancellor for equity, diversity, and inclusion in 2015 (starting salary: $354,900, now up to $444,000). “The law has an obligation to respond to changes in scientific knowledge,” Kang said in a 2015 lecture. “Federal anti-discrimination law has been fixated on, and obsessed with, conscious intent.” But the new “behavioral realism,” as the movement to incorporate IAT-inspired concepts into the law calls itself, shows that we “discriminate without the intent and awareness to discriminate.” If we look only for conscious intent, we will “necessarily be blind to a whole bunch of real harm that is painful and consequential,” he concluded. Kang has pitched behavioral realism to law firms, corporations, judges, and government agencies.1
A battle is underway regarding the admissibility of IAT research in employment-discrimination lawsuits: Plaintiffs’ attorneys regularly offer Anthony Greenwald as an expert witness; the defense tries to disqualify him. Greenwald has survived some defense challenges but has lost others. Kang is philosophical: “It might not matter if Tony’s expert testimony is kicked out now,” he said in his 2015 lecture—in ten years, everyone will know that our brains harbor hidden biases. And if that alleged knowledge becomes legally actionable, then every personnel decision can be challenged as the product of implicit bias. The only way to guarantee equality of opportunity would be to mandate equality of results through quotas, observes the University of Pennsylvania’s Philip Tetlock, a critic of the most sweeping IAT claims.
The potential reach of the behavioral-realism movement, which George Soros’s Open Society Foundation is underwriting, goes far beyond employment-discrimination litigation. Some employers are using the IAT to screen potential workers, diversity consultant Howard Ross says. More and more college administrations require members of faculty-search committees to take the IAT to confront their hidden biases against minority and female candidates. Promotion committees at many corporations undergo the IAT. UCLA’s law school strongly encourages incoming law students to take the test to confront their implicit prejudice against fellow students; the University of Virginia might incorporate the IAT into its curriculum. Kang has argued for FCC regulation of how the news media portray minorities, to lessen implicit prejudice. If threats to fair treatment “lie in every mind,” as Kang and Banaji argued in a 2006 California Law Review article, then the scope for government intervention in private transactions to overcome those threats is almost limitless.
But though proponents refer to IAT research as “science”—or, in Kang’s words, “remarkable,” “jaw-dropping” science—their claims about its social significance leapfrogged ahead of scientific validation. There is hardly an aspect of IAT doctrine that is not now under methodological challenge.
Any social-psychological instrument must pass two tests to be considered accurate: reliability and validity. A psychological instrument is reliable if the same test subject, taking the test at different times, achieves roughly the same score each time. But IAT bias scores have a lower rate of consistency than is deemed acceptable for use in the real world—a subject could be rated with a high degree of implicit bias on one taking of the IAT and a low or moderate degree the next time around. A recent estimate puts the reliability of the race IAT at half of what is considered usable. No evidence exists, in other words, that the IAT reliably measures anything stable in the test-taker.
But the fiercest disputes concern the IAT’s validity. A psychological instrument is deemed “valid” if it actually measures what it claims to be measuring—in this case, implicit bias and, by extension, discriminatory behavior. If the IAT were valid, a high implicit-bias score would predict discriminatory behavior, as Greenwald and Banaji asserted from the start. It turns out, however, that IAT scores have almost no connection to what ludicrously counts as “discriminatory behavior” in IAT research—trivial nuances of body language during a mock interview in a college psychology laboratory, say, or a hypothetical choice to donate to children in Colombian, rather than South African, slums. Oceans of ink have been spilled debating the statistical strength of the correlation between IAT scores and lab-induced “discriminatory behavior” on the part of college students paid to take the test. The actual content of those “discriminatory behaviors” gets mentioned only in passing, if at all, and no one notes how remote those behaviors are from the discrimination that we should be worried about.
Even if we accept at face value that the placement of one’s chair in a mock lab interview or decisions in a prisoner’s-dilemma game are significant “discriminatory behaviors,” the statistical connection between IAT scores and those actions is negligible. A 2009 meta-analysis of 122 IAT studies by Greenwald, Banaji, and two management professors found that IAT scores accounted for only 5.5 percent of the variation in laboratory-induced “discrimination.” Even that low score was arrived at by questionable methods, as Jesse Singal discussed in a masterful review of the IAT literature in New York magazine. A team of IAT skeptics—Fred Oswald of Rice University, Gregory Mitchell of the University of Virginia law school, Hart Blanton of the University of Connecticut, James Jaccard of New York University, and Philip Tetlock—noticed that Greenwald and his coauthors had counted opposite behaviors as validating the IAT. If test subjects scored high on implicit bias via the IAT but demonstrated better behavior toward out-group members (such as blacks) than toward in-group members, that was a validation of the IAT on the theory that the subjects were overcompensating for their implicit bias. But studies that found a correlation between a high implicit-bias score and discriminatory behavior toward out-group members also validated the IAT. In
other words: heads, I win; tails, I win.
Greenwald and Banaji now admit that the IAT does not predict biased behavior. The psychometric problems associated with the race IAT “render [it] problematic to use to classify persons as likely to engage in discrimination,” they wrote in 2015, just two years after their sweeping claims in Blindspot. The IAT should not be used, for example, to select a bias-free jury, maintains Greenwald. “We do not regard the IAT as diagnosing something that inevitably results in racist or prejudicial behavior,” he told The Chronicle of Higher Education in January 2017. Their fallback position: Though the IAT does not predict individual biased behavior, it predicts discrimination and disadvantage in the aggregate. “Statistically small effects” can have “societally large effects,” they have argued. If a society has higher levels of implicit bias against blacks as measured on the IAT, it will allegedly have higher levels of discriminatory behavior. Hart Blanton, one of the skeptics, dismisses this argument. If you don’t know what an instrument means on an individual level, you don’t know what it means in the aggregate, he told New York’s Singal. In fairness to Greenwald and Banaji, it is true that a cholesterol score, say, is more accurate at predicting heart attacks the larger the sample of subjects. But too much debate exists about what the IAT actually measures for much confidence about large-scale effects.
The Diversity Delusion Page 10
