Racial Bias and Implicit Associations

One of the speakers I was excited to see at the Society for Neuroscience conference this year was Dr. Mahzarin Banaji.  I have been curious, and had strong feelings, about her work for a long time.

Banaji is one of the researchers who has developed the idea that memory retrieval can be affected by our unconscious bias, and she is part of the team that runs Project Implicit, which uses the Implicit-Association Test (IAT) to examine unconscious racial bias (among other things).

The IAT asks subjects to assign words to one of two categories: for example, is the name ‘Aaliyah’ the name of someone ‘Black’ or a ‘White’?  These categories are on either the left or right side of the screen; the subject signals choice by picking the side of the category.

IAT - Task 1

The test then introduces another category: for example, does the word ‘Suffering’ belong in the category ‘Pleasant’ or ‘Unpleasant’?

IAT - Task 2

Finally, the test layers the two category choices, and asks the subject to pick the side of the screen which contains the correct category, of the four.

IAT - Task 3

The testers hypothesized that subjects would perform this task more quickly when the two paired categories on either side of the screen were also cognitively paired for the subjects, i.e. ‘White’ + ‘Pleasant’, or ‘Black’ + ‘Unpleasant’, in the case of racial bias.  So far, there is a fair amount of data which supports their hypothesis.

However, the IAT is problematic.  First of all, it is very easy to manipulate the test – the mechanism is simple, and once you’ve grasped it, you can easily delay your responses along whatever axis for which you do not wish to express a bias.

Second, and more importantly, the IAT assumes that a slower sorting time in a cognitive retrieval task meaningfully reveals something as complicated as racial bias.  There are many reasons why this might not be the case: language sorting and recall are governed by different parts of the brain than those which govern social parsing.  The two processes might be unrelated.

Which is why studies showing that implicit biases as revealed by the IAT actually predict bias in behavior are valuable.  For example, Banaji and her team published a paper in 2007 (Green et al., 2007, ‘Implicit Bias among Physicians and its Prediction of Thrombolysis Decisions for Black and White Patients’) showing that physicians who scored on high on the IAT for implicit racial bias were less likely to prescribe thrombolysis for black patients than for white patients with identical case files, despite the fact that the physicians reported (and probably believed) that they had no explicit racial bias.

In fact, white patients are much more likely than black patients to receive thrombolysis treatment, and there are a number of well-documented racial disparities in the treatment of cardiac patients in the United States.  Papers like Green et al., are important not only because they support the usefulness of the IAT, but also because they help explain how subtle and pernicious racial bias can be, and because they propose a quantitative, though imperfect, mechanism for finding it out in ourselves.

Sample IAT images borrowed from Wikipedia.

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