Blindspot: Hidden Biases of Good People

The chapter title is a reference to solving a problem that arose when an original copy of a book that Greenwald had requested was transferred to a digital copy. In the digital copy, the words were continuously cut off on the right side of the even-numbered pages. The issue was that the margins of the right and left pages were different, so a paper copy with margins that were similar had to be made first. The administrator in charge of making the digital copy told Greenwald that to fix the problem, the “trick” had been “to outsmart the machine” (146). The authors connect this anecdote directly to the issue of mindbugs—arguing that the trick is for the brain to outsmart the hidden biases within it. In other words, the reflective side of our minds needs to be able to override the automatic. But the question remains as to whether this can be done. Are we able to “outsmart the machinery of our own hidden biases?” (146).

The authors explain that there are, in fact, strategies for outsmarting mindbugs. The first example they provide is of the audition process for symphony orchestra applicants. At one time, orchestra musicians were predominantly male. A cheap, easy adjustment to the audition process, however, altered the gender dynamic forever. After a curtain was placed in front of the person auditioning—making the audition a blind one—the number of female orchestra musicians increased dramatically. The association between “male” and “virtuoso” (147) was seriously challenged. The “blinding strategy” (148) can be used in other contexts, like student test or essay grading. However, in most cases—such as interviews, court cases, medical exams, and real estate transactions—it simply isn’t feasible. Not only is face-to-face communication essential but demographic information is “trivially easy” (148) to obtain online.

In the 1990s, researcher Nilanjana Dasgupta looked into whether mindbugs could be weakened or even destroyed. After showing test subjects images of admirable African Americans interspersed with images of white American criminals, the subjects took the Race IAT. They showed a “weaker” (150) association between “White” and “good” (150) than those who were shown images of admirable white Americans. The same held true when subjects took an age IAT after seeing images of admirable older people. These results, along with those from other similar studies, showed that stereotypes could be changed “with relatively simple procedures” (151). Mindbugs could be undermined more easily than the authors initially believed. However, it is not clear from these studies that the attitude change displayed by test subjects is lasting. More likely, the changes are “elastic” (152), meaning attitudes are briefly altered or stretched, then return to their initial state. The mindbug is temporarily suppressed, rather than eliminated. The persistence of mindbugs means that the most promising strategy is to outsmart them.

However, the idea of trying to outsmart a mindbug may be met with resistance. The authors use the example of attitudes toward pit bulls, which generally have a negative reputation and are reportedly euthanized more than other dogs. They ask if you should change your views toward pit bulls if you think they are more viscous than other dogs. The answer is a difficult one, and the authors suggest labeling the view of pit bulls as viscous as a “phobia” (156) rather than a stereotype. They explain that there is no easy way to outsmart such a mindbug. Pit bulls could be tested for aggressiveness, but this would be hard to implement everywhere. And while a test does supply numerical data that appears objective, this is not always the case. Tests can also be problematic and may carry bias in them as well. The authors cite the example of the 2002 Winter Olympics in Salt Lake City to demonstrate the issue with numerical data. The International figure-skating competition was compromised when it came out that judges had colluded to support certain skaters over others. A new scoring system was put in place as a result, but it still holds the potential for bias and collusion.

The authors return to the “GOOD PEOPLE” (158) of the book’s title, explaining that there are white Americans who consciously consider themselves to be “egalitarian” (158) but still display bias and “subtle forms of race discrimination” (158). Research by psychologists Samuel Gaertner and Jack Dovidio led to labeling this “subset” (158) of Americans as “aversive racists” (158). The authors find the label potentially problematic since showing a white preference on the Race IAT does not necessarily make one racist. They offer the label “uncomfortable egalitarians” (159) instead. The authors estimate that this group consists of about 40%of white and Asian Americans as well as a smaller number of Latinx and African Americans. Their racial preference may be a contributing factor to the disadvantages faced by Black Americans and may go completely unnoticed.

Uncomfortable egalitarians may view themselves as good, helpful people, but their helpful behavior is selective—potentially because they find interacting with other racial groups anxiety-provoking. They mainly assist those who are similar to themselves, like the doctor who gave Carla extra help with her hand when he found out she was also at Yale. While describing such preferential treatment as “discrimination” (160) may seem extreme, it often has the unintended result of perpetuating advantages of already advantaged groups. As other people act similarly, the cumulate effect is potentially significant. “In-group favoritism” (162), the term used to describe preferential behavior, is perhaps the biggest factor in perpetuating disadvantages to Black Americans and other groups. Although people with blatant prejudices definitely play a role, they continue to be a small minority of the population. One way to neutralize this mindbug is to help groups equally, if possible. The authors use the example of a friend who donated the same amount of money to both her college and the United Negro College Fund.

The authors then ask if self-defeating mindbugs can be reversed. They recall the mindbug that many women hold associating “female” with “family” and “male” with “career” (163) as well as ones held by the elderly that “old” means “infirm” (163). They also bring up the idea that academic achievement is often associated with white or Asian Americans. One possible way to modify these types of mindbugs is through positive role models. However, the problem of overcoming self-defeating mindbugs remains a challenge. Although in such cases we essentially see ourselves through the eyes of others, we are not conscious of it and therefore unable to benefit from it and make necessary adjustments.

In the book’s conclusion, the authors compare the Race IAT to a new device that helps indicate to a driver when a vehicle is in their blind spot. The IAT is similarly a warning device to help people “avoid unintended discrimination” (166). Perhaps one day it will operate in real time, alerting people to their hidden biases as they navigate their daily lives. Other strategies exist for tackling blindspots. They include the blinding method—used for the symphony orchestra auditions—as well as the implementation of guidelines based on evidence. While the authors do not know if mindbugs can be completely eradicated yet, they believe methods can eventually be developed to outsmart them.