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As California debates bills to address implicit bias, we re-examine the science and research behind it




Obama administration officials at an Implicit Bias Training in 2016.
Obama administration officials at an Implicit Bias Training in 2016.
SAUL LOEB/AFP/Getty Images

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In California, several bills currently making their way through the California legislature seek to tackle implicit bias among medical professionals, police officers and judges.

One from Los Angeles Democratic Senator Holly Mitchell would set up an unconscious bias training program for perinatal healthcare providers, require the state public health department to collect stronger data on maternal mortality rates, and require hospitals to give patients more information on how to file discrimination complaints. A package of bills from Los Angeles Democratic Assemblywoman Sydney Kamlager-Dove would require medical professionals like doctors and nurses assistants, police officers and court officers to undergo at least eight hours of implicit bias training after getting their licenses and every two years afterward.

An April 2018 incident at a Philadelphia Starbucks in which a store employee called the police on two black men who were waiting for a friend to show up for a business meeting put a spotlight on implicit bias, and the training designed to try and identify it. It also raised questions about the science behind implicit bias and the accuracy and utility of the Implicit Association Test (IAT), the industry standard for measuring implicit bias which takes the speed at which the test-taker links words or images to things that are seen as good or bad and uses it to create a metric for hidden biases.

Starbucks ultimately closed more than 8,000 stores in May 2018 to conduct unconscious bias training, which advocates say is designed to help people identify their own implicit biases so that they might better keep them in check in social situations. But others who have studied it argue the IAT doesn’t achieve traditional scientific standards for validating a particular measure of something, and say that other factors like explicit bias and structural racism contribute more to inequality than implicit bias.

In light of these bills, AirTalk will revisit the discussion over the science behind implicit bias and the evidence for and against whether or not the test used to gauge it is a good predictor of how people will behave in real-life social situations.

Correction: A previous version of this article identified Sydney Kamlager-Dove as a California State Senator. She is a member of the California Assembly. We have updated the article to reflect this change and apologize for the error.

Guests:

Kelly Capatosto, senior research associate at The Ohio State University’s Kirwan Institute for the Study of Race and Ethnicity, where her work focuses on implicit bias

Michèle Lamont, professor of sociology, African-American Studies and European Studies at Harvard University; she tweets @mlamont6