Revealing Your Unconscious: Part 2 - Transcripts

March 14, 2023

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In the second part of our series on implicit bias, we explore the relationship between beliefs and behaviors. We also talk with psychologist Mahzarin Banaji about whether research on implicit bias tells us more about groups than it does about individuals.


For Hidden Brain, I'm Shankar Vedantu. All of us know what prejudice looks like. We've seen news stories about swastika 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 Mazarine 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 test 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 one of the story, which I strongly recommend you listen to before you listen to this episode, we explore 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. 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

Interest in the test surged. Many companies began mandating their employees take the test during diversity training exercises. As we saw in part one of the story, early studies that Mazarin 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.

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. It's a test that is enormously intuitively appealing. 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 saying that 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 prejudice 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 Mazarin's research were published in peer-reviewed academic journals. Soon, Mazarin 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 two things are correlated to each other, they use a scale from zero to one. 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 are 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 two things is to zero, the less likely the two things are correlated.

And when it came to Mazarin'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 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. In that set, if you begin to use five criteria for what is a good study that any scientist

would agree, the correlation jumps up to three times that size. I first read about Mazarin'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. Results 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, Mazarine 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 eighth grade, and we could get the data on the gender difference in performance on that mathematics test, taken in eighth grade across many countries. And so 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, so you have a lot of countries with a bunch of variation in gender and what you're looking to see is whether the countries that showed 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, Mazarin didn't think much of it but what she was finding was that when you give people an implicit association test measuring how quickly the associate concepts in mathematics with men rather than with women they were regional differences, national differences, in 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 eighth 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 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 step back and look 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. When you step 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. This is Hidden Brain, I'm Shankar Vedantam. The implicit association test became very popular after psychologist Mazarin 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 win, and to be honest with you, I was really surprised at how insightful

it was. Does that score mean that I do not like European Americans? No. Am I 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 I. A. T. 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, Mazarin and her colleagues discovered a curious result. The performance of boys and girls in an eighth 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. And so he took our data and said, well, we can look county by county at IAT. So 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. Okay, so 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 county. You'll 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. You know, 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, et cetera. And 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 are 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 two views on this. The two 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, Mazarin 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

of something that is in the larger culture. 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. 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. And he did this because, obviously, he was a 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. And 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. So 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. So Keith, in the 21st century, goes back to this map and he says, let's correlate these two things, IAT race bias in that county today and the number of enslaved to free people proportions or ratios in 1860. And 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 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 three 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 two hours from now or two 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. 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 bias is 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 two 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. And therefore, what we will say is that reliability is low. So I think when we put it all together, so this is one strand of why I like your hypertension example. The reason I like the hypertension example is when I teach, when I say, you know, hypertension is called the silent killer because you don't feel it. You know, 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, you know, 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, you know, 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 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 is 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. In go Frank Dobbin and others and say, ooh, the people who did

DEI training, nothing happened there. Masrin 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 in effective 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 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 six 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. Both 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 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 Vedanta. This is Hidden Brain. I'm Shankar Vedanta. Psychologist Mazarine 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 eighth century when Islamic invasions of that part of the world began and over two centuries, somewhere between the eighth and tenth 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 use 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. Coming up, Mazarin often felt pulled in two 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, Kariche, Kariche 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 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. Mazarin 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. 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. 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 come kill us or anything. But

we had to just stay outside the mainstream in some way. Nazarene 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 Avastan, 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 Maserine 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 Maserine and Tessa found was that between 2007 and 2020, anti-gay bias decreased by

nearly two thirds. It's not yet neutral. But we're predicting, our model predicts that

in one and a half years, 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 two kinds of measures, the black-white test, and also the dark-skinned, light-skinned 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%, which

we know it could be if we were doing things the way we're doing for sexuality bias. To recap, Maserine 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 three 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're 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 going to 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 demographic group. Gay people only, things like that.

And it turns out, no. Everybody's 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 this 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. Mazarin 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 three 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 US 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 there. So at the end of the day, if you asked 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 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 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. Maserine Banaji is a psychologist at Harvard University. Along with Toni Greenwald, she's the author of Blind Spot, Hidden Biases of Good People.

Maserine, thank you for joining me today on Hidden Bright. 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 Maserine 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 at 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 at 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, Kristen Wong, Laura Quirell, 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.

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