Implicit bias: a fancy term for "you will get your mind right"

Okay, I got my objective-journalist job done.

As I've said here before, I recently picked up a new gig as a freelance news guy for a local media company that owns an array of radio stations. I cover the local-government beat.

Last night I attended the monthly local school-board meeting. The customary way the meetings are structured to begin is for one of the board members to offer a reflection on some aspect of the education enterprise. In this case, one guy talked about how a colleague had turned him on to an article in an education trade magazine on implicit bias and how it had spurred him to think about that more deeply. He encouraged all those present - fellow board members, school-corporation staff, and the general public in the audience - to examine how implicit bias shows up in their own lives.

Later in the proceedings - after a first reading of the 2019 budget, approval of claims and payroll and some other matters - another board member said he'd like to take up where the reflection guy had left off. He'd recently attended a workshop on implicit bias put on by the school corporation's diversity officer (I don't think I knew it had one, but I'm not at all surprised) and found the exercises she put attendees through to be eye-opening. He reiterated the first guy's desire to encourage everyone to look more deeply into it.

Of course, any normal person who sets store by his or her individual agency - that is, ownership of the inside of his or her noggin - is going to bristle at the very notion of such a concept, but let's not react from gut alone.

Let's go to a September 2017 City Journal article by Heather MacDonald for some history and insight:

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 non-race 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 studies; 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 Blind Spot, 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 fatal police shooting of Michael Brown in Ferguson, Missouri.

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.

She cites fields such as business and law enforcement, where this fad has taken hold.

But how does it hold up as science?

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. 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 Blind Spot. 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. 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.

Initially, most of the psychology profession accepted the startling claim that one’s predilection to discriminate in real life is revealed by the microsecond speed with which one sorts images. But possible alternative meanings of a “pro-white” IAT score are now beginning to emerge. Older test-takers may have cognitive difficulty with the shifting instructions of the IAT. Objective correlations between group membership and socioeconomic outcomes may lead to differences in sorting times, as could greater familiarity with one ethnic-racial group compared with another. These alternative meanings should have been ruled out before the world learned that a new “scientific” test had revealed the ubiquity of prejudice.

The most recent meta-analysis deals another blow to the conventional IAT narrative. This study, not yet formally published, looked at whether changes in implicit bias allegedly measured by the IAT led to changes in “discriminatory behavior”—defined as the usual artificial lab conduct. While small changes in IAT scores can be induced in a lab setting through various psychological priming techniques, they do not produce changes in behavior, the study found. The analyses’ seven authors propose a radical possibility that would halt the implicit-bias crusade in its tracks: “perhaps automatically retrieved associations really are causally inert”—that is, they have no relationship to how we act in the real world. Instead of “acting as a ‘cognitive monster’ that inevitably leads to bias-consistent thought and behavior,” the researchers propose, “automatically retrieved associations could reflect the residual ‘scar’ of concepts that are frequently paired together within the social environment.” If this is true, they write, there would need to be a “reevaluation of some of the central assumptions that drive implicit bias research.” That is an understatement.

Among the study’s authors are Brian Nosek of the University of Virginia and Calvin Lai of Washington University in St. Louis. Both have collaborated with Greenwald and Banaji in furthering the dominant IAT narrative; Nosek was Banaji’s student and helped put the IAT on the web. It is a testament to their scientific integrity that they have gone where the data have led them. (Greenwald warned me in advance about their meta-analysis: “There has been a recent rash of popular press critique based on a privately circulated ‘research report’ that has not been accepted by any journal, and has been heavily criticized by editor and reviewers of the one journal to which I know it was submitted,” he wrote in an e-mail. But the Nosek, Lai, et al. study was not “privately circulated”; it is available on the web, as part of the open-science initiative that Nosek helped found.)

T

he fractious debate around the IAT has been carried out exclusively at the micro-level, with hundreds of articles burrowing deep into complicated statistical models to assess minute differences in experimental reaction times. Meanwhile, outside the purview of these debates, two salient features of the world go unnoticed by the participants: the pervasiveness of racial preferences and the behavior that lies behind socioeconomic disparities.

And organizations that hire people had been puking all over themselves to achieve the "correct" demographic mix long before implicit bias became a thing:

One would have difficulty finding an elite institution today that does not pressure its managers to hire and promote as many blacks and Hispanics as possible. Nearly 90 percent of Fortune 500 companies have some sort of diversity infrastructure, according to Howard Ross. The federal Equal Employment Opportunity Commission requires every business with 100 or more employees to report the racial composition of its workforce. Employers know that empty boxes for blacks and other “underrepresented minorities” can trigger governmental review. Some companies tie manager compensation to the achievement of “diversity,” as Roger Clegg documented before the U.S. Civil Rights Commission in 2006. “If people miss their diversity and inclusion goals, it hurts their bonuses,” the CEO of Abbott Laboratories said in a 2002 interview. Since then, the diversity pressure has only intensified. Google’s “objectives and key results” for managers include increased diversity. Walmart and other big corporations require law firms to put minority attorneys on the legal teams that represent them. “We are terminating a firm right now strictly because of their inability to grasp our diversity expectations,” Walmart’s general counsel announced in 2005. Any reporter seeking a surefire story idea can propose tallying up the minorities in a particular firm or profession; Silicon Valley has become the favorite subject of bean-counting “exposés,” though Hollywood and the entertainment industry are also targets of choice. Organizations will do everything possible to avoid such negative publicity.

In colleges, the mandate to hire more minority (and female) candidates hangs over almost all faculty recruiting. (Asians don’t count as a “minority” or a “person of color” for academic diversity purposes, since they are academically competitive.) Deans have canceled faculty-search results and ordered the hiring committee to go back to the drawing board if the finalists are not sufficiently “diverse.” (See “Multiculti U,” Spring 2013.) Every selective college today admits black and Hispanic students with much weaker academic qualifications than white and Asian students, as any high school senior knows. At the University of Michigan, for example, an Asian with the same GPA and SAT scores as the median black admit had zero chance in 2005 of admission; a white with those same scores had a 1 percent chance of admission. At Arizona State University, a white with the same academic credentials as the average black admit had a 2 percent chance of admission in 2006; that average black had a 96 percent chance of admission. The preferences continue into graduate and professional schools. UCLA and UC Berkeley law schools admit blacks at a 400 percent higher rate than can be explained on race-neutral grounds, though California law in theory bans them from using racial preferences. From 2013 to 2016, medical schools nationally admitted 57 percent of black applicants with low MCATs of 24 to 26 but only 8 percent of whites and 6 percent of Asians with those same low scores, as Frederick Lynch reported in the New York Times. The reason for these racial preferences is administrators’ burning desire to engineer a campus with a “critical mass” of black and Hispanic faces.

Similar pressures exist in the government and nonprofit sectors. In the New York Police Department, blacks and Hispanics are promoted ahead of whites for every position to which promotion is discretionary, as opposed to being determined by an objective exam. In the 1990s, blacks and Hispanics became detectives almost five years earlier than whites and took half the time as whites did to be appointed to deputy inspector or deputy chief.

And yet, we are to believe that alleged millisecond associations between blacks and negative terms are a more powerful determinant of who gets admitted, hired, and promoted than these often explicit and heavy-handed preferences. 

I love the way she nails a diversity jackboot to the wall in this phone exchange:

PricewaterhouseCoopers has spearheaded an economy-wide diversity initiative, dubbed the CEO Action for Diversity & Inclusion™. Nearly 200 CEOs have signed a pledge to send their employees to implicit-bias training; in the case of PricewaterhouseCoopers, that means packing off 50,000 employees to the trainers. Any organization spending a large sum of money on a problem would presumably have a firm evidentiary basis that the problem exists. Megan DiSciullo is a spokesman for the CEO Action for Diversity & Inclusion and a member of PricewaterhouseCoopers’s human resources department. I asked her if she was aware of candidates who should have been hired at PwC but weren’t because of implicit bias. Our telephone exchange went as follows:

DiSciullo: I’m not aware of someone not getting a job because of bias.

Me: But are your managers making suboptimal decisions because of bias?

DiSciullo: The coalition as a group recognizes that everyone has unconscious bias; we are committed to training our managers to be better.

Me: Your managers are not making optimal decisions because of bias?

DiSciullo: Everyone has unconscious bias. I’m not saying that anyone is not being hired or promoted, but it’s part of the workplace.

Me: In what way? People are being treated differently?

DiSciullo: People have bias, but it manifests itself differently. I think you have an agenda which I am trying to unpack. The facts are clear that people have biases and that they could bring them to the workplace. Corporations recognize that fact and want to build the most inclusive workplace.

Me: You base the statement that everyone has biases on what?

DiSciullo: On science and on the Harvard Business Review.

Her deep dive goes on for a while, looking into performance results in a variety of fields.

Her concluding paragraph ties it all together:

A thought experiment is in order: if American blacks acted en masse like Asian-Americans for ten years in all things relevant to economic success—if they had similar rates of school attendance, paying attention in class, doing homework and studying for exams, staying away from crime, persisting in a job, and avoiding out-of-wedlock childbearing—and we still saw racial differences in income, professional status, and incarceration rates, then it would be well justified to seek an explanation in unconscious prejudice. But as long as the behavioral disparities remain so great, the minute distinctions of the IAT are a sideshow. America has an appalling history of racism and brutal subjugation, and we should always be vigilant against any recurrence of that history. But the most influential sectors of our economy today practice preferences in favor of blacks. The main obstacles to racial equality at present lie not in implicit bias but in culture and behavior.

But here in a small city in the heart of flyover country, tax dollars are going to this stuff.

You know what's next: explicit talk about privilege.