Revealing Your Unconscious: Part 2

This is Hidden Brain. I'm Shankar Vedantam. All of us know what prejudice looks like. We've seen news stories about swastikas spray-painted on synagogues or nooses drawn on classroom walls to terrorize Black students. We have heard xenophobic speeches from politicians and watched in horror as ethnic groups around the world have exterminated their enemies. In the late 1990s, Harvard psychologist Mahzarin Banaji and her former PhD advisor, Tony Greenwald of the University of Washington, developed a test of hidden bias called the Implicit Association Test, or IAT. Unlike the very public spectacle of a burning cross on someone's front lawn, the picture of bias painted by this test was rather subtle. By measuring the speed of people's associations, the tests showed that large numbers of Americans found it easier to associate white faces with positive concepts than to associate black faces with positive concepts. Many people were similarly quick to associate men with professional activities and slow to associate women with such activities. Lots and lots of Americans appear to have negative associations about the elderly, the overweight, and the disabled. Crucially, large numbers of the people taking the tests didn't think of themselves as being prejudiced. Many prided themselves on having egalitarian beliefs. Their test results often came as a shock.

The feeling Was a feeling of dread, I would say. A feeling of having had the rug pulled out from under you. You have to start from scratch to now rebuild a view of yourself that will forever be a different view of yourself.

In part 1 of the story, which I strongly recommend you listen to before you listen to this episode, we explored the origins of the psychological test. Today on the show, we explore what it means that so many people have subtle biases when it comes to their mental associations. Are these biases benign and just inside people's heads, or do they cause people to act in biased ways? Is there anything we can do to fight our biases? The surprising connection between our biases and our behavior, this week on Hidden Brain. Support for Hidden Brain comes from Planet Visionaries. Professional climber and environmental advocate Alex Honnold hosts Planet Visionaries, a podcast spotlighting the people tackling climate challenges with real, hopeful solutions. Throughout season 6, Alex brings together extraordinary changemakers for powerful conversations about grit, determination, and the power of hope to fuel our fight for a better future. In partnership with the Rolex Perpetual Planet Initiative, this is Planet Visionaries. Listen or watch wherever you get your podcasts.

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Support for Hidden Brain comes from Progressive. Whoever says saving money is hard doesn't know that new customers who bundle and save with Progressive save hundreds. Because switching your home and auto insurance is better than cutting down on those iced lattes you love. Switch to Progressive and see if you could save. Get your quote today at progressive.com. Progressive Casualty Insurance Company, affiliates, and other insurers. Not available in all states. In George Orwell's dystopian novel 1984, There were lots of ways to get in trouble with a totalitarian state. Protesting in the street was a quick way to get seized by the authorities, but you could also get in trouble for subtler things, like reading the wrong book or having the wrong opinions. The eyes and ears of the state were everywhere, and subjects were expected to not just do the right things, but to think the right thoughts. After Harvard University put the Implicit Association Test on its website— you can find it at implicit.harvard.edu— interest in the test surged. Many companies began mandating their employees take the test during diversity training exercises. As we saw in part 1 of the story, early studies that Mahzarin and others conducted suggested a connection between implicit biases and the behavior of individuals taking the test. In one study she conducted with physician Alexander Green, physicians with higher bias scores on the IAT were less likely to prescribe clot-busting treatments to a Black patient relative to a white patient. But soon other studies started to come out that showed no association between the two. People showing higher levels of bias on the test did not act in biased ways. Critics of the test, including Phil Tetlock at the University of Pennsylvania, started to argue that the test was measuring the equivalent of Orwellian thought crimes instead of judging people on their words and actions. I think the IAT is grounded in a reductionist view of human nature, that there is a I conducted this interview with Phil some years ago, in 2017, as we featured him on another episode of Hidden Brain.

It depicts people somewhat as association-driven automatons. I'm not temperamentally all that comfortable with that form of reductionism, but I wouldn't reject the test on that basis. And I don't reject the test. It's a little bit different. I mean, it's a test that is enormously intuitively appealing. I mean, I've never seen a psychological test take off the way the IAT has and has gripped the popular imagination the way it has because it just seems on its surface to be measuring something like prejudice. You've got the differential reaction times, right, between the black and white stimuli, say, and this just seems to be a bullseye. And anybody who denies it is engaging in some kind of scholastic quibbling. But there is the question of whether or not people who score as prejudiced on the IAT actually act in discriminatory ways toward other human beings in real-world situations. And if they don't, if there is a very close to zero relationship between those two things, What exactly is the IAT measuring? Meta-analyses—

studies tracking a body of research, not just an individual experiment— are one way to tell if something is a fad or a fact. If you have a number of studies linking a medication with positive patient outcomes, for example, you become more certain that the medication actually works. Meta-analyses of the IAT test data found mixed results. Some studies showed a connection between implicit bias and real-world discrimination. But plenty of others did not. For many critics of the test, this was vindication that the test was useless and that the hype over the test was unfounded. Challenges to the usefulness of Mahzarin's research were published in peer-reviewed academic journals. Soon, Mahzarin and her colleagues decided to launch a comprehensive meta-analysis of their own. I asked her if she found there was a correlation between implicit biases and real-world behavior.

The strength of the correlation is small.

When scientists measure whether 2 things are correlated to each other, they use a scale from 0 To 1. Taller people tend to be heavier people, so height and weight are correlated. It doesn't mean every tall person is going to weigh more than every short person. All it says is that there is a relationship between the two. As height goes up, you're likely to weigh more. But there are lots of things that are not correlated. Taller people, for example, are not better at math. The closer a correlation between 2 things is to 0, the less likely the 2 things are correlated. And when it came to Mahzarin's analysis of the IAT—

We published a paper that probably places the correlation at the smallest it has ever been reported to be, around 0.1. And that's because we decided to include every single study, no matter how poorly it was conducted. So we decided we will not throw any study out, there are studies in there that really don't belong. Somebody did a study looking at whether race bias would predict their degree of smoking. There should be no correlation, but that study is in there and it counts as a lack of correlation. So we have gone, you know, overboard in making sure that no matter what the study, we will include it. And so we show that even when you do that, the correlation is about 0.1. Within that set, if you begin to use 5 criteria for what is a good study that any scientist would agree, you know, the correlation jumps up to 3 times that size.

I first read about Mahzarin's work and the IAT around 20 years ago. It's fair to say I initially thought it would show that individuals with implicit biases always act in biased ways. I wrote about the test in my 2010 book, The Hidden Brain. As mixed data started to emerge in the last decade, I found myself having to question my beliefs. There was no doubt that large numbers of people around the world showed fast or slow associations on the tests. And the results of the tests generally matched our intuitions about the nature of prejudice. Across many countries, people more swiftly associated men rather than women with concepts related to science or leadership. Groups that were in the majority often had negative biases about minorities. The data coming in were voluminous. This wasn't about a couple of studies and a few dozen subjects. There were hundreds of studies and millions of test takers. But if people's test results were only weakly correlated with real-world behavior, what did these results mean? Did it really make sense for companies around the world to mandate their employees take these tests? Some were even using test results to figure out who should be involved with HR and hiring or rising to leadership positions. As critics and supporters used the studies as ammunition for their pet beliefs, Mahzarin and her colleagues kept doing research. The volume of data rolling in meant that they could do something that most social scientists working in other areas could only dream about. They could start to analyze relationships between test results and real-world behavior, not at the level of individuals or even at the level of companies, but at the level of cities, regions, and nations. In 2009, They reported preliminary results on an unusual finding.

We had data from a few countries, not as many as we have today. So we are in the process of replicating that to see if with so much more data, because we did this a long time ago, in which we knew that there was a standardized test that was taken across countries in 8th grade, and we could get the data on the gender difference in performance on that mathematics test. Taken in 8th grade across many countries. For each country, you compute the gender difference. What is the gender difference in Country A? It could be big. If boys score a lot higher than the girls, that's a big difference. There might be countries where boys and girls score about the same. You have a lot of countries with a bunch of variation in gender. What you're looking to see is whether the countries that show the largest difference in math performance between girls and boys are also the countries whose people are carrying in their heads a stronger association of boys with math rather than girls with math. And we reported that there is a robust positive correlation. Countries with higher gender bias are also the countries where the girls are underperforming compared to boys to a greater extent.

I want to take a moment to sit with this finding. At the time she did this study, Mahzarin didn't think much of it. But what she was finding was that when you give people an implicit association test measuring how quickly they associate concepts in mathematics with men rather than with women, there were regional differences, national differences, and test scores. When you averaged implicit bias scores across entire countries, People in some countries were faster than others when it came to associating men with math and slower in associating women with math. Now, if you turn to how 8th grade students were doing in the standardized math test, girls in countries with high implicit bias were doing worse than girls in countries with low implicit bias. The interesting thing is that if you look not at a nation But at an individual student or an individual school, you might see little to no correlation between implicit bias test results and student performance on the standardized mathematics test. Or you might find it in one school, but not in the next. It's only when you stepped back and looked at the big picture that you saw a robust correlation. It was like one of those pixelated images or a painting that uses the style known as pointillism. Up close, you just see a lot of dots. It's only when you step back you realize, oh, there's a picture here.

That paper, while I always thought that was an interesting result, I think I wasn't smart enough to realize that the reason we were getting a fairly substantial correlation is because we were wiping out lots of individual-level error, we were collapsing across many different people to come up with a much more stable score of what is going on in the larger system, in the larger environment in which people are sitting. And when you take the average of that, the average of a whole bunch of people, you're likely to pick up the actual or true correlation in a much better way.

When someone gives you a test, You feel the test is saying something about you. That is true, but also not completely true when it comes to the Implicit Association Test. Yes, at one level, the tests are telling you about something that is inside your head. But the tests might be telling you something much more important about the culture in which you are living. When we come back, what happens when we look at the implicit association test not at the level of individuals, but at the level of cities, counties, and nations? You're listening to Hidden Brain. I'm Shankar Vedantam. Support for Hidden Brain comes from Progressive. Whoever says saving money is hard doesn't know that new customers who bundle and Save with Progressive, save hundreds. Because switching your home and auto insurance is better than cutting down on those iced lattes you love. Switch to Progressive and see if you could save. Get your quote today at progressive.com. Progressive Casualty Insurance Company, affiliates, and other insurers. Not available in all states. Support for Hidden Brain comes from Quince. We all have those pieces in our closet that we can count on. The versatile jacket, the perfect t-shirt. These are the pieces that make getting dressed easy, and that's what Quince does best. Quince makes elevated everyday essentials using premium materials in designs that are timeless, thoughtfully crafted, and made to be worn again and again. And when the temperatures start to drop, Quince makes it easy to build on these everyday staples with versatile layers designed to keep you warm and comfortable All season long. Find your next fall favorites at Quince. Download the Quince app for app-exclusive offers or go to quince.com. Get free shipping on your order and 365-day returns. Now available in Canada and the UK too. And when Quince asks where you heard about them, let them know it was Hidden Brain. This is Hidden Brain. I'm Shankar Vedantam. The Implicit Association Test became very popular after psychologist Mahzarin Banaji and her colleagues placed the test on the Harvard website. Millions of people took the test, hoping it would give them a glimpse into their own minds.

I took the race one, and to be honest with you, I was really surprised at how insightful it was.

Does that score mean that I do not like Black people? Do I not like European Americans?

No.

Is my subconscious aware of the condition that African Americans are in in this country at this particular point?

Is it because I can't come to say that I'm bad? And is it just in our nature that there has to be an us and a them, and them is going to be the bad guy?

But as researchers evaluated whether test results revealed real-world behavior, they found mixed results. Sometimes people who showed high bias on the implicit association test acted in ways that were biased, but at other times they didn't. When you looked at all the studies together, the correlation between individual implicit bias test results and individual real-world outcomes was small. But the torrent of data from IAT test-takers around the United States and around the world meant researchers could now start to analyze not just links between test results and individual behavior, but the correlations between average scores in an area and real-world outcomes. Early on, Mahzarin and her colleagues discovered a curious result. The performance of boys and girls in an 8th grade standardized math test appeared to be linked to average implicit bias scores in those nations. Countries where people were quicker to associate men with science showed a wider gap in test scores. Girls did worse on the standardized test. In time, other research along these lines started to emerge.

So Raj Chetty, for example, my colleague in economics, is interested in not only why upward mobility is so slow these days compared to what it was, why the American dream has vanished, but he's also interested in for whom is the American dream more likely and less likely. He took our data and said, well, we can look county by county at IAT. Now forget the individual, instead identify the county and take the average IAT of all the people in that county and give it just one score. You collapse across all the people in the county and you come up with one IAT number and you do that county by county by by county, you line up the counties from the most anti-Black to the least anti-Black counties, and you look to see if that predicts upward mobility for Black Americans. In other words, he shows that the higher the race bias, average race bias in a county, the harder it is for Black people in that county to be upwardly mobile. This is just one example. Now that we've understood what the correlation is, I can just rattle off for you. Now I think we're up to about 17 independent studies that have been published that show that higher race bias in a county will predict greater lethal use of force by police against Black Americans. The most recent study shows greater militarization of police departments in those counties, greater threats to maternal health and infant health in the counties that have greater bias. School disciplining differences between white and black kids that are greater in counties that have greater race bias, traffic stops, tickets, etc. These 17 studies that I'm just mentioning that look at average IAT scores by county or by state or by metropolitan region or by country, they're all averages by region. They are just predicting up and down the spectrum in a way in which I would never have predicted, but is really exciting to see because these dependent variables are not simple little things. It's not even how well you do on a math test. This is about whether you live or die. This is about whether you will get disciplined in school and get kicked out. This is about whether you will live as a baby.

Can you talk a little bit about why it is we would see a stronger correlation at this aggregate level? So in other words, if you're analyzing my brain and saying, here's your implicit bias, and then you're evaluating me to see, do I hire a Black person or a white person? Do I hire a man or a woman? Why is it that we would see a lower correlation at me and an individual, but when you step out and look at the aggregate, you have a higher correlation? How would that be the case?

That's the power of aggregation. Any individual score is going to vary based on lots of things jittering around in that moment. And more importantly, whether you behave in a way that is biased or less biased is going to be multiply determined by little things in the local environment. For example, my score on race bias may be quite high. I may be quite anti-Black, but it may be that in the moment in which you're testing my behavior, a smiling person appeared in front of me who wiped out my bias and I responded positively to that person. Little things like that in the environment can make the behavior move around and not allow the particular measure in which you're interested in to show itself. So as soon as you aggregate it, for every person like me, somebody else's similar behavior will counter it. And so all you are doing— the best way to understand it is that when we aggregate, we are removing individual-level noise in the data.

One analogy to this idea comes from the realm of polling. In the United States, lots of polling is done by groups that have either a conservative or a liberal bias. Unsurprisingly, polls that lean conservative are likely to predict conservative victories. Liberal polls are likely to predict victories for Democrats. But something interesting happens when you average out the polls. The poll that leans too far right gets balanced out by the poll that leans left. When you average polls, you are likely to get answers that are much more accurate than individual polls. The same thing happens if you ask people to make estimates of something. say the size of the U.S. economy. Low estimates and high estimates cancel each other out when you average the answers, leaving you with a better approximation of the correct answer. This phenomenon is sometimes known as the wisdom of the crowd, meaning the average answer across a group of people is often more accurate than individual answers.

Now, there are 2 views on this. The 2 views are— my view is That as we remove noise, we will see higher and higher levels of correlation.

In other words, as the tests get better, as studies are conducted more carefully, Mahzarin is saying she expects the correlations to get better at the individual level, not just at the level of nations.

But she also cites a second possibility, that the IAT is really capturing a reflection in people's minds Somebody I admire greatly and agree with in many ways, and I'm not opposed to this, but this is Keith Payne. He argues that these things don't operate at the level of the individual. You will never, no matter how much error variance you remove, it will never get better at the individual level because we become of a certain place when we go into a certain area. So he did this remarkable study that if I can just describe really quickly, I will tell you about it. Keith obtained a map that had been produced by Abraham Lincoln in the 1860s in which Lincoln had his people plot county by county the proportion of enslaved to free people in every county in the Southern states. He did this because obviously he was this Smart guy, a scientist almost, because he thought, if I know that, if I know the proportion of enslaved people in a county, I will be able to make better military predictions about which counties are going to fall faster than other counties. The simple idea was the greater the proportion of enslaved people, the harder they will fight and the more they will resist giving up slavery. This map exists even to this day. You can look at the map, you can see the counties and little numbers that tell us what the proportions are. Keith, in the 21st century, goes back to this map and he says, let's correlate these 2 things, IAT race bias in that county today and the number of enslaved to free people proportions or ratios in 1860. Lo and behold, The correlation is quite substantial and high. And he will say, exactly as we've been discussing, he will say, well, how can it be? These are not the same people.

Notice how surprising this is. Keith Payne, a psychologist at the University of North Carolina Chapel Hill, looked at implicit bias test results for people in the 21st century. Why would these results be connected to policies that existed more than 150 years ago? Everyone who lived in those counties in the 1860s is now very dead.

The proportions of enslaved to free people are not the people whose minds we're measuring. In fact, we can't. They're gone. And it's not even the case that the descendants of those very people live there today. In the United States, enough migrations have happened that that is not the case. So Keith's point of view, which is very interesting, is that your mind reflects what is right around you. And that if you were somebody who lived in Seattle and Microsoft Corporation sent you to someplace in Georgia and you you arrive there, you become of that place. If there are many Confederate statues in the town in which you live, your mind will move in the direction of more anti-Black bias. Your children will hear certain things in school and they'll bring them to your home, and as you talk about them, you too will start to acquire that. And these are the things that ultimately create these remarkable correlations, almost unbelievable. And what it tells us is just the long shadow of history and how psychology is able to pick up this incredibly long shadow of history, that we can look at data from 1860 and we can predict today what that county's race bias is. Or we can look at the race bias today and we can predict who those people were. I find this absolutely fascinating.

In some ways, I think I'm hearing 3 different models. One says bias is produced by active animosity and hostility. People who act in biased ways mean to be biased. The second says, no, our minds are mirrors And when we go to different places, we are going to reflect what is out there. But I think there's also a third model, and I might call this the hypertension model. So if you were to measure my blood pressure right now and measure my blood pressure 2 hours from now or 2 days from now, it's going to fluctuate because blood pressure is not super stable. It depends on what's going on, what's happening to me physically, my mental state.

Mm-hmm.

But if you find that I do have high blood pressure, It doesn't necessarily tell you I'm going to have a heart attack next week, or I'm going to have a stroke next month. So in other words, it's a useful measure, but at an individual level, it's a somewhat crude measure of determining short-term risk. But if you were to step back and say, what's the average hypertension of all the people in California, or what's the average hypertension of all the people living in New York, and let's say the average hypertension in California was significantly higher than the average hypertension in New York, You could very confidently say you will have many more heart attacks and strokes in California than in New York, even though you can't predict which individuals are going to be affected. You can say something meaningful about the group, even if you can't be very precise about individuals. I mean, the same thing goes for smokers and nonsmokers. I might not be able to tell you which individuals are going to develop cancer in any given week, but I know the group of smokers is going to have more cancers than the group of nonsmokers. So both the mirror model and the hypertension model suggest that if you want to understand how unconscious biases caused biased actions, you need to look beyond the individual mind and look at larger systems and structures.

I think you very much have it right. And I like the example of hypertension for many reasons, one of which is that the way we measure hypertension shows that the machine is not terribly reliable for all the reasons that you said. If my arm is up or down, if I've just eaten, if I've walked, it will vary and sometimes substantially vary. There is error variance in that measure. The measure is not as good as it could be. Same with the IAT. The IAT is not as perfect a measure as we would think or like it to be. So there's error variance there. However, Your blood pressure does fluctuate. My brain is not the same brain as it was 2 hours ago. You know, having talked to you, a bunch of connections have now been made, and my bias on some topic, because we've been talking about it, could be higher or lower than it was. In other words, the IAT is actually picking up the real state of your brain now, which was different than it was yesterday. Therefore, what we'll say is reliability is low. I think when we put it all together, so this is one strand of why I like your hypertension example. The other reason I like the hypertension example is when I teach, when I say hypertension is called the silent killer because you don't feel it. It's not like osteoarthritis or something where the pain tells you that something is going on in your body. But wouldn't we want to know that we have it? And don't we want to invent gizmos that are not very reliable but can still save our lives? I think of this attempt, and I'm not speaking about the IAT here, I'm speaking about any attempt to try to get at this kind of implicit cognition. I think it's exactly the same thing that we're trying to do for our mind as we do for our body. We're trying to invent a measure that may not be very reliable but could give us enough evidence that we would say, you know what, knowing this, I will change my behavior. I will do things in a different way. And so I just love the hypertension example.

You know, one question it does raise though is that to the extent that these measures are in fact telling us something more useful at the aggregate level than at the individual level, whether that's the Keith Payne idea that our minds are really reflecting what's happening around us. In other words, what you're picking up in the measure of me as an individual is really It's really my reflection of what's in the society and the culture around me. Or in the case of the hypertension example, my hypertension is actually more relevant as a clue when you aggregate it with the hypertension of all the other people around me in terms of predicting where the heart attacks and strokes are going to be. In both those cases, does it not raise questions about the model of fixing these biases that seems to have become very popular, where so much of the efforts to fix implicit bias has been about trying to eradicate bias from individual brains. When you think about DEI efforts at various companies and corporations, so much of that is, we'll give you a test. We'll show you through the test that you have bias. And then we're going to try and train this bias out of you. If, in fact, the bias comes from the society, if, in fact, it's a reflection of the society, or, in fact, it's part of the larger systems and structures in which we're part of, isn't it a fool's errand to try and say we can actually just fix individual minds and hope to solve the problem?

In one sense, I couldn't agree with you more. I would say that it is a fool's errand to think that we can go into a corporation, especially as it is currently done, come and give a talk on implicit this or that, and then assume that we've checked off our box and now we don't have implicit bias. And in go Frank Dobbin and others and say, ooh, the people who did D&I training, nothing happened there. Okay, so—

Mahzarin is referring here to work by the sociologists Frank Dobbin and Alexandra Kalev, who find that mandatory diversity training, as practiced by many corporations today, is not only ineffective but frequently counterproductive.

So that's not a surprise because the intervention is not up to the task of actually changing anything real. I do believe, though, that that that education is necessary. And it's necessary not because it will change an individual person's bias, but it will make them open to structural changes their organization will want to make. So if I work with any group that comes to me and says, what shall we do? I will say, I will teach them about implicit bias in a scientific way. You can't do this if it's mandatory. I will only come if it's voluntary. And what I think we will achieve is that when you then go to them and you say, you know what, the way we run interviews is really bad. Interviews are a terrible way to make decisions. We are going to start to do something differently. We are going to get resumes with much harder good evidence. We're not going to let people write their hobbies on their resumes. We're going to do these screenings. We're going to bring interns in for 6 months and we're going to pick from that instead of these silly ways in which we did. I believe that if that education has been done well, that you will be able to make all these institutional-level changes that will ultimately change the level of bias because you will have fixed it by intervening in the right moments. So I'm very clear with organizations, if you want to change people, I'm not the right person. for you, I will in fact agree very much with you, Shankar, in saying that that would be a fool's errand. But I don't say don't educate them because I do believe that the education plays the role of making individuals feel secure as to why we're going about changing our organization. If police officers in Cambridge, who I've worked with, haven't been in a session with me and haven't learned why what I'm saying is in their interest, they will resist every little step of the way, wearing a body camera, wearing a bulletproof vest, and everything. But after a session like this, and we have a paper in which we're going to summarize the massive shift in attitudes that we've seen in police officers prior to an educational seminar and post an educational seminar, now they're saying, yeah, I see I see why this is good for me. So I believe in teaching, and I believe it's necessary, but nowhere sufficient.

When we come back, how change happens. You're listening to Hidden Brain. I'm Shankar Vedantam. Support for Hidden Brain comes from Instagram. Instagram Teen Accounts' automatic protections limit who can contact teens, the content they can see, and the time they spend on the app. Teens are automatically placed into Teen Accounts. Teens under 16 will need a parent's permission to change any of those settings to be less strict. Learn more about Teen Accounts and Instagram's ongoing work to protect teens online Working as a dedicated Fidelity advisor, I always ask, what's important to you?

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Support for Hidden Brain comes from Air France. Air France Business Cabin offers comfort throughout the journey with priority check-in, boarding, and lounge access. Onboard, enjoy menus from renowned French chefs, wines and champagne selected by a sommelier, and free high-speed Wi-Fi. When it's time to rest, settle into a fully adjustable lie-flat bed with added mattress comfort. The art of travel elevated, because Air France Business Cabin goes further in every detail to make your journey exceptional. This is Hidden Brain. I'm Shankar Vedantam. Psychologist Mahzarin Banaji had a formative experience growing up in India. She was a member of a minority group that was all but invisible.

So the short version is that Zoroastrianism is known today as the oldest monotheistic religion in the world. Its origins were in Central Asia, in particular in what was then the Persian Empire. And Zoroastrianism was the state, state religion. It was a very successful religion. It spread far and wide. Until about the 8th century when Islamic invasions of that part of the world began. And over 2 centuries, somewhere between the 8th and 10th century, Zoroastrians who did not wish to be converted took off in little boats looking for asylum, religious asylum. And I guess the first country that allowed them that religious asylum was India. It was on the west coast of India that they landed in Gujarat, and the story goes that the local king met them and said to them, we are full, there are many of us here, we can't take you in. And at least the apocryphal story is that the captain of this little boat asked for some milk and sugar because they couldn't speak the same language, and used this, put the sugar into the milk, stirred it, and explained that we would just blend in and that we would sweeten the milk. And the king was so happy with this demo that he apparently let us in and said, you can practice your religion, you can have whatever beliefs you wish, you have to speak our language and wear our dress.

Growing up, Mahzarin often felt pulled in 2 directions. Ever the observer, she noticed when this happened and what it meant.

I'll just give you one example. In my own community of Zoroastrians, but particularly in my family, I was considered dark-skinned. I was. People would say things to me like, oh, you know, kari che. Kari che means she's black. But when I would step out of the house, I would be considered pale-skinned in South India. So what was I? Very early, it was a conundrum. Am I dark-skinned or am I light-skinned? I think it taught me that there was clearly nothing inherent in the physical aspect of my skin color that made me light or dark. It's the context that made it so.

In India, the Zoroastrian community is known as the Parsi community, likely because the first Zoroastrians were associated with travelers from Pars, a region of Iran. Mazareen said she always sensed the Parsi community had to stand apart from the larger Indian society.

It was communicated in a very sort of sideways way. Nobody said to us, you cannot do X. But we just knew we couldn't. If you needed something fixed, you would call the Parsi neighbor who would call, you know, the Parsi friend who would come and fix it. fix it. You didn't participate in society. And I only learned this when I married somebody from the dominant group and I watched how his family operated. And I thought, wow, that's what it means to have access. It's just normal stuff, you know? My father wouldn't even collect the reimbursements of health insurance that would have come to him because he was a government servant. I mean, he just wouldn't participate in those sorts of things. We wouldn't feel we had access. But we knew that we would be safe. Nobody was going to, you know, come kill us or anything. But we had to just stay outside the mainstream in some way.

Mahzarin told me that while she was deeply enmeshed in Parsi culture while living in India, it was only when she came to the United States that she really started to understand her family's faith.

I learned a lot about my own religion when I came to Yale as an assistant professor and I met Stanley Insler, who was a scholar of Zoroastrianism and particularly of the holy book, which I can, you know, I can rattle off many thousands of lines of code in a language called Avestan, but I don't understand it. And what I learned by reading Stanley's books is that Zoroastrianism takes as its core principle, the recognition that the world is constructed of good and evil, and that the job of every Zoroastrian every day is to ask, on which side am I going to be? And that when you review your life, that's what you look at. How many times was I on the side of good or not? And I think of that as both quite profound but also somewhat ironic. I only noticed it years after we had, you know, worked on implicit attitudes that the fundamental dimension I study is the dimension of good and bad.

Some time ago, one of Mahzarin's students came to her. With a research idea that had direct bearing on this question of good and bad. Was it possible, Tessa Charlesworth asked, that implicit biases were actually receding, that the United States was becoming less biased?

I was completely confident, and I even said to her, yeah, you're not going to see any change, not in our lifetime. I mean, change will happen, but if you look at the IAT from 2007 to today, My prediction, no change. It'll be a flat line over time because implicit bias changes but not fast. So Tessa does these lovely analyses and what the data show is something quite stunning. On the sexuality test, the anti-gay bias test, bias was quite high. Anti-gay bias was quite high in 2007, but with every day Every month since then, it has slowly been coming down so that in 2020, that bias has come down close to neutrality.

What Mahzarin and Tessa found was that between 2007 and 2020, anti-gay bias decreased by nearly 2/3.

It's not yet neutral, but we're predicting— our model predicts that in 1.5 Americans will be at neutrality on that.

There are also encouraging signs when it comes to biases based on race.

Race bias has also come down on 2 kinds of measures: the Black-white test and also the dark skin/light skin test, which is not race but could be seen as another proxy for something akin to race. Both tests show exactly the same drop in bias by 25%. So it is not nothing, but it is not 64%, okay, which we know it could be if we were doing things the way we're doing for sexuality bias.

To recap, Mahzarin and Tessa's data found that race bias has come down by 25% and anti-gay bias by a remarkable 64%. That's the good news. The bad news? There are 3 other types of bias where the data haven't budged at all.

Anti-elderly bias, disability bias, body weight bias. These stigmas, I think, are going to be much harder to change. They are visible, they're on the body, and We don't talk about them nearly enough. We are not arguing about age bias or disability or body weight. In fact, body weight bias people express quite explicitly. That may be a part of it, but also these are gonna be harder to change. Right now, if we do nothing, those biases are with us for at least 200 years. That's what our model predicts. Let me go back to sexuality and just say one thing about it that I think is incredibly interesting. We thought, okay, it's changing, but it must be young people only, or a certain part, a certain demographic group, gay people only, things like that. And it turns out, no, everybody is changing. Conservatives are changing and liberals are changing. Elderly are changing and young are changing. Educated and less educated are changing. Rich are changing and poor are changing. So I think these results are together really exciting to us. It tells us that change is possible at the societal level.

As we put this episode together, Republicans and Democrats in the Senate came together to pass landmark legislation enshrining the right to gay marriage. I find it difficult to imagine this would have happened if it were not for a sea change in public attitudes, a sea change that the implicit bias test seems to have picked up. Mahzarin points to the forces driving the change. There was change at the individual level, as grandparents reconciled themselves with their grandchildren's sexuality, changes at an institutional level as companies began offering same-sex benefits to workers, and change at the level of national policy in terms of laws and Supreme Court decisions.

All 3 happened within a tight period of time. And when you have change at this many different levels of society, from the individual human to the Supreme Court, that's when you can get a 64% drop in implicit bias, anti-gay bias.

To be clear, the fact that there has been a dramatic drop in anti-gay bias does not mean the pendulum cannot swing back. There are jurisdictions across the U.S. and around the world that are actively trying to curtail LGBTQ rights. There is a two-way street between what happens in our minds and public policy. Laws can change because of the biases in our heads, But our biases can also change as a result of laws and cultural shifts. I know that you don't think of yourself as being a religious person, but again, I'm struck by something you told me about what it means to be a good Parsi. You know, the good Parsi recognizes that the world has good and evil and has to try and make a choice every day which side they're on. Do you feel like you do that? In your own life?

I mean, consciously, yes. Almost like writing in a book. One of the things I teach about is that our ancestors had very clear evidence every day about the harm that they did to people who were not like them. They would get on a horse, they would go into some neighboring village and loot it and bring their stuff back to theirs. So at the end of the day, If you asked, you know, our ancestors on the tundra, did you harm somebody who was not like you? They would say, damn right I did. They would have direct evidence. You and I live in such a protected and privileged world that we don't have to do that every day. We don't have the experience of harming people who are different from us. So how is it that we discriminate? And we do. We do it in a very paradoxical way. We do it by who we help. And I think that this is where, have I been a good person, is no longer a simple question. Because if I help people from my own tribe, which I'm sure I do, I should not count that in the good column until I've done a compensating behavior in the other column. Which is very hard to do.

do.

And which is why institutions and governments have to enter. It is because you and I as individuals will help. If my friend calls me up and says, my son is not doing well, you know, can he come and spend a summer in your lab? I will say yes. And I don't think I want to be the kind of person who doesn't do that. But if that's happening, then my institution needs to have a program by which people who are not the children of my friend can visit my lab. And this is why I think helping can often be the way in which we keep the world unequal, and yet we don't count it as something that we've done that is not something we should be proud of. So you see how it's complicated, and Yet, every day, when I do the kind of work and see the data that come in, I am being transformed in what I think is to go into the good and bad column of the Zoroastrian ledger.

Mahzarin Banaji is a psychologist at Harvard University. Along with Tony Greenwald, she's the author of Blindspot: Hidden Biases of Good People. Mahzarin, thank you for joining me today on Hidden Brain.

Thank you for having me. Always a pleasure.

Some weeks ago, we ran an experiment, and we would like to do it again. We're exploring the possibility of regular follow-up conversations in which our listeners can pose their questions to our guests. If you have questions or thoughts about our series with Mahzarin Banaji and are willing to have those questions shared with a larger Hidden Brain audience, please record a voice memo on your phone and email it to us at ideas@hiddenbrain.org. 60 seconds is plenty. Please remember to include your name and a phone number where we can reach you. Again, email the questions to us at ideas@hiddenbrain.org and use the subject line Implicit Bias Episodes. Hidden Brain is produced by Hidden Brain Media. Our audio production team includes Bridget McCarthy, Annie Murphy-Paul, Kristin Wong, Laura Querelle, Ryan Katz, Autumn Barnes, and Andrew Chadwick. Tara Boyle is our executive producer. I'm Hidden Brain's executive editor. Special thanks for this episode to sound designer Nick Woodbury. Our unsung hero today is listener and Hidden Brain supporter Brendan Smith of Oakland, California. Brendan says he likes to listen to Hidden Brain while walking with his golden retriever, Max. We're really glad to be part of your walks with Max, Brendan. Thanks so much for your support. If you found this episode thought-provoking and you would like to join Brendan in helping us to make more episodes like this, please do your part to keep us thriving. Help us build more shows for new listeners. Visit support.hiddenbrain.org. I'm Shankar Vedantam. See you soon. Having lots of choices sounds like it would be great. Who doesn't want more flavors of ice cream? It turns out the answer is us. Too many choices can make us anxious, overwhelmed. I'm Shankar Vedantam, host of the Hidden Brain podcast and YouTube channel. Each week, Hidden Brain brings you fascinating, science-backed insights about human behavior. So the next time you have to choose, choose Hidden Brain on Spotify, Apple Podcasts, YouTube, or hiddenbrain.org.

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