The Situationist

Implicit Bias and Strawmen

Posted by Jerry Kang on March 2, 2007

By now, many folks are familiar with the implicit social cognition work of Anthony Greenwald and Situationist contributors Mahzarin Banaji and Brian Nosek. The concept of “implicit bias”, which can be measured by reaction time instruments such as the implicit association test (IAT), has already had a substantial impact on the way that we think about race, racism, and race relations. The data reveal that we generally have more biases than we think we do.

Predictably, a backlash of sorts is forming against this work — after all, it’s always disturbing to think that we are not so colorblind or gender-blind after all. But certain complaints are slightly surprising, especially given where they are coming from.


Ford and Thought Police

To take one recent example, Stanford law professor Richard Ford wrote recently in a Slate article titled “The Irrelevance of Soft Bigotry” that:

Joe Biden got his presidential campaign off on the wrong foot this week when he called one of his competitors for the Democratic nomination, Barack Obama, “the first mainstream African-American who is articulate and bright and clean and a nice-looking guy.” . . .

So, is Biden a bigot, and should we care in the context of his presidential candidacy (assuming it survives)? Federal laws offer a rationale for concluding that the answer is, not much.

Civil rights law doesn’t prohibit racism by employers. It prohibits discrimination on the basis of race—tangible actions taken by employers, not their bad attitudes. . . .

Yet there are plenty of new tools for the thought police. For instance, the Implicit Association Test, developed by psychologists Mahzarin Banaji and Anthony Greenwald, seeks to identify not just hidden biases, but even unconscious biases.

There’s a lot to respond to here. But for now, I want to focus on only one point– the suggestion that implicit bias researchers are interested in thought control. That is nonsense, a strawman. It is Psychology 101 to separate out mental states from action. For lawyers not familiar with the standard typology, psychologists tend to distinguish attitudes from beliefs, sometimes calling negative attitudes “prejudice” and negative beliefs “stereotypes.” More important, they distinguish both attitudes and beliefs from action based on those mental constructs, e.g., “discrimination.” So, when Ford writes that we should be focusing on behavior as most important, no one is in disagreement. It is misleading to suggest otherwise. This is precisely why so many scientific resources are being poured into predictive validty and malleability studies.


Patterson and Authenticity

Here’s another example from Orlando Patterson, sociology professor at Harvard. In an article titled “Our Overrated Inner Self,” in the New York Times, he suggests that we are too absorbed with an “authentic” self. And that implicit bias scientists are pursuing a “gotcha psychology” that contends that implicit measures are the only “authentic” measures that should matter.

Again, a strawman. No responsible implicit bias scholar contends that implicit bias measures are somehow the only real, true attitudes and beliefs. (This interest in “true” or “authentic” also sounds a bit dispositionist, which is another issue.) For example, predictive validity studies suggest that explicit self-reports better predict behavior generally, but in domains influenced by negative attitudes and beliefs, implicit bias measures outperform as predictors.

At bottom, this strawman may be an odd sort of projection. This criticism might come from those who believe that explicit self-reports alone measure “true” attitudes and beliefs, that these reports are the only “true” predictors of behavior, and that these reports are the only “true” bases for moral, social, and legal evaluation. I doubt any serious psychologist or legal scholar thinks this, and the best science suggests that our lives, minds, and behaviors are much too complicated for such a simple diagnosis. Privileging explicit self-reports–even if entirely sincere–as the only thing that matters is not warranted by the best evidence we have. We need to be more behaviorally realistic (I hope to post soon about “behavioral realism”).


A Call for Care and Self-Critical Engagement

Talking about race and justice are difficult enough without strawmen being built, then torn down. I have always been leery of blogs because they can encourage soundbites and oversimplifications. I’m trying to do just the opposite here. On matters of implicit bias, there’s lots to learn and think through on both empirical and normative fronts. Further, most of the interesting questions are genuinely difficult, without the strawmen. My request among all scientists, legal academics, lawyers, and policy makers working in this domain is to earnestly avoid strawmen.

And if I’ve been guilty of the same in my work (see, e.g., Trojan Horses of Race, Harvard 2005; Fair Measures, California 2006 (with M. Banaji))—and given what Yale psychologist David Armor has shown us about the illusion of objectivity, I’m sure I have—point it out, and I’ll own up to it, as well as commit to avoiding it in the future.


4 Responses to “Implicit Bias and Strawmen”

  1. […] guilty” on several occasions (e.g., Guilty or Not Guilty?: Law & Mind Meets Hamlet; Implicit Bias and Strawmen). We now bring you news of a new study by New York University psychologist David M. Amodio and his […]

  2. Consequentialist said

    “And if I’ve been guilty of the same in my work (see, e.g., Trojan Horses of Race, Harvard 2005; Fair Measures, California 2006 (with M. Banaji))—and given what Yale psychologist David Armor has shown us about the illusion of objectivity, I’m sure I have—point it out, and I’ll own up to it, as well as commit to avoiding it in the future.”

    Here are some problems for the four points in the Fair Measures article:

    1. Racial discrimination laws create an effective presumption of guilt in the presence of group disparities in hiring or admissions, which results in firms and institutions creating unofficial quota systems to match employee or student proportions to population proportions. Implicit bias effects are nowhere near large enough for completely eliminating them to come close to eliminating those disparities, and so they cannot justify affirmative action as it is practiced.

    2. Your second point, that more accurate measurements of ‘merit’ should be adopted, is irrelevant for affirmative action as we know it, which is applied to prevent the effects of scientifically-informed personnel selection conducted without knowledge of group membership. There are big differences in work performance across groups:

    An organization allowed to use IQ tests and other predictively valid measures of work performance to select the employees who will do the best jobs will wind up with big racial disparities, end of story. The Supreme Court cases were about deviating from measures (grades, standardized tests) that *accurately predicted or over-predicted* performance (graduation rates, grades, medical malpractice for the Bakke medical school case) for favored minorities. If we wanted to minimize the number of patients killed by medical malpractice, maximize the performance of life-saving firefighters, etc, the data suggest that predictively valid psychometrics and physical tests should be used, measurements that would result in forbidden group disparities.

    3. Debiasing techniques are handy if you’re going for accuracy, but with numerical quotas they’re unneccessary unless implicit associations cause mistakes in rank-ordering candidates within groups.

    4. There will be a long wait indeed for the elimination of implicit bias in the presence of real differences in ability and performance. Anyone who accurately observes their environment or is familiar with the relevant data will risk forming the forbidden implicit associations.

  3. Consequentialist said

    Regarding “The Trojan Horse of Race” and the ‘accuracy objection,’ you present several arguments that general knowledge of the facts about group statistics will be interpreted in a negative light and that people will overreact to the information, especially viscerally charged discussion of individual cases of violence. For local news I can see the benefits of reducing the salience of race (although it may be needed as identifying information for violent criminals at large), but the taboo extends to the national press and often to policymakers and academia.

    Consider President-Elect Obama’s attribution of high rates of African-American incarceration to bias in the prosecution, conviction, and sentencing processes within the justice system. In fact, criminologists have worked very hard for decades to find evidence of such bias and failed: differences in incarceration are best explained by differences in crimes. Changing what counts as a crime, e.g. ending drug prohibition, or changing the root causes of crime might help, but Obama’s claim was wrong and distracted from the actual problems. Yet broadcast and print media almost completely failed to communicate this fact, and indeed almost never mention the relevant statistics.

    This sort of misconception can result in bad policy, e.g. police departments that allocate policing resources and arrests based on levels of criminal activity are accused of racism, and redistribute their efforts in a way that is less efficient at preventing murders. When police stop African-American drivers more often than other groups, but those stops result in more (proportionally) crimes discovered (e.g. drug or illegal weapon possession), they are directed to change their stopping patterns to less effective ones. When ‘white flight’ took the tax base out of urban cores, neglect of the importance of crime in favor of racism-based explanations mislead about what could reverse the flight (and the decline of crime has led to renewal of urban areas).

    Thus, even if we would be wise to restrict emotional images of individual criminals, it is important that dry and sober discussion of crime statistics take place. That does not happen on CBS or Fox News, in the New York Times or the Wall Street Journal, which is why the DOJ statistics are so shocking to anyone drawing solely on our national mass media for information:

    The most reliable crime statistics are homicides, since deaths and bodies have to be reported and most homicides are solved, and racial disparities are indeed huge. A 33:1 disparity between African-American and Asian-American homicide rates, 8:1 between whites (including Hispanic whites, which are aggregated for these statistics, but not most other federal statistics, reducing the apparent disparity) and African-Americans, probably 10:1 between non-Hispanic whites and African-Americans. About half of all homicides are committed by African Americans, and probably less than a third by non-Hispanic whites. Because demographics vary across the country, in regions where African-Americans or Latinos are much more than 10% of the population either (and often do) group can easily form a supermajority of homicide offenders. Race predicts criminal activity much better than SES, education, or any other such variables, and policy analysis that does not take such large effects into account will be systematically misguided.

    Similar issues arise elsewhere, e.g. the idea of ‘equal pay for equal work’ is often presented as though income differences primarily reflected discrimination in hiring and irrational discrimination by employers in pay levels (with no explanation of why sex-indifferent companies don’t make enormous profits by hiring women at 80 cents on the dollar instead of 70). Hilary Clinton did this and proposed legislative intervention in wage-setting, a return of ‘comparable worth.’

    In fact, the differences in income can be explained by reduced work hours and continuity of employment due to the unequal distribution of childrearing in our society, differences in education focus, physical labor and risk involved in jobs, preferences (for work involving interaction with people and animals versus inanimate objects) and non-cash benefits (short commutes, a clean office environment, etc). There are various things that could be done to address these factors, e.g. subsidized daycare, changes in the tax system, etc, but a legislative intervention setting wages in numerous industry based on factors other than supply and demand is a horribly wasteful and inefficient way to do things, which is why economists oppose it. But if media self-censorship prevents acknowledgment of any causes other than employer discrimination, then we are more likely to get the bad policy.

  4. tmaxPA said

    He had me right up until the end:

    “Anyone who accurately observes their environment or is familiar with the relevant data will risk forming the forbidden implicit associations.”

    Until then, I only suspected that he was just an ideologue attacking good science.

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