Implicit Versus Explicit Prejudice

This post is based largely on the groundbreaking book by Seth Stephens-Davidowitz “Everybody Lies: Big Data, New Data, and What the Internet Reveals About Who we Really Are.” Any theory of racism has to explain the following puzzle in America: On the one hand, the overwhelming majority of black Americans think they suffer from prejudice—and they have ample evidence of discrimination in police stops, job interviews, and jury decisions. On the other hand, very few white Americans will admit to being racist. The dominant explanation has been that this is due, in large part, to widespread implicit prejudice. According to this theory white Americans may mean well, but they have a subconscious bias, which influences their treatment of black Americans. There is an implicit-association test for such a bias. These tests have consistently shown that it takes most people milliseconds more to associate black faces with positive words such as “good,” than with negative words such as “awful.” For white faces, the pattern is reversed. The small extra time it takes is interpreted as evidence of someone’s implicit prejudice—a prejudice the person may not even be aware of.

There is an alternative explanation for the discrimination that African-Americans feel and whites deny: hidden explicit racism. People might be aware of widespread conscious racism but to which they do not want to confess—especially in a survey. This is what the search data seems to be saying. There is nothing implicit about searching for “n_____ jokes.” It’s hard to imagine that Americans are Googling the word “n_____“ with the same frequency as “migraine and economist” without explicit racism having a major impact on African-Americans. There was no convincing measure of this bias prior to the Google data. Seth uses this measure to see what it explains.

It explains, as was discussed in a previous post, why Obama’s vote totals in 2008 and 2012 were depressed in many regions. It also correlates with the black-white wage gap, as a team of economists recently reported. In other words, the areas Seth found that make the most racist searches underpay black people. When the polling guru Nate Silver looked for the geographic variable that correlated most strongly with support in the 2016 Republican primary for Trump, he found it in the map of racism Seth had developed. That variable was searches for “n_____.”

Scholars have recently put together a state-by-state measure of implicit prejudice agains black people, which enabled Seth to compare the effects of explicit racism, as measured by Google searches, and implicit bias. Using regression analysis, Seth found that, to predict where Obama underperformed, an area’s racist Google searches explained a lot. An area’s performance on implicit-association tests added little.

Seth has found subconscious prejudice may have a more fundamental impact for other groups. He was able to use Google searches to find evidence of implicit prejudice against another segment of the population: young girls.

So, who would be harboring bias against girls? Their parents. Of all Google searches starting “Is my 2-year-old, the most common next word is “gifted.” But this question is not asked equally about young boys and young girls. Parents are two and a half times more likely to ask “Is my son gifted?” than “Is my daughter gifted?” Parents overriding concerns regarding their daughters is anything related to appearance.

https://implicit.harvard.edu/implicit/

The URL above will take you to a number of options for taking and learning about the implicit association test.

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