[00:00:05] SO: One theme that runs through 2020 and 2021 is our relationship with bias. We know that unconscious bias is powerful, and in recent years we’ve collectively had that discussion more. We know that bias can impact both innovation and culture. But we also know that bias properly addressed can be a competitive advantage. The past 18 months have laid much better about our relationships with work, friends, culture, politics, privilege, and much more. For professionals interested in brain science, habit, activation, and human behavior, as we are here at the NeuroLeadership Institute, it’s been an insanely interesting time.
Our three panelists for this episode discussed bias in a variety of ways; how it’s an impediment to innovation, its cultural relevance, and ways of overcoming the inherent challenges behind said biases. It’s very action-heavy, diversity-heavy, race-heavy, and bias-heavy. If these are current challenges for you, we hope you embrace them. I’m Shadé Olasimbo, and you’re listening to Your Brain at Work from the NeuroLeadership Institute. We continue to draw our episodes from a weekly webinar series that NLI has been hosting every Friday. For this one, there’s lots of different visuals referenced. So if you’d like to view the video version of this episode, you can find it on our YouTube channel, youtube.com/neuroleadershipinstitute. Look for Episode 50, and keep in mind you can view all of our previous episodes there as well.
But for today’s episode, our panel consists of NLI’s Senior Client Strategist, Janet Stovall, NYU’s inaugural Senior Vice President for Global Inclusion and Strategic Innovation, Dr. Lisa Coleman, and Dr. Natalie Byfield, a professor in the Department of Sociology and Anthropology at St. John’s University. Enjoy.
[00:02:06] JS: For my title, we’re going to talk about innovation and communication, and we’re going to talk about what world bias plays in that. So we’re going to start with talking about how we got to where we are, we’re going to go from there to where we are, and then we’re going to talk about the future. Let’s start off saying, first of all, what do we mean by bias.
At NLI, we start and we end all of our definitions of bias with these eight words. If you have a brain, you have bias. It’s that simple. Bias, cognitive bias, is in the brain. It’s hardwired. It’s evolutionary. It’s designed to keep us alive. It’s prime directive is don’t die. You might think that we have evolved to the point where we can be smart enough not to be biased. Not true. Intelligence won’t make you less biased. Experience or expertise might change the biases that you deal with, but it will not make you less biased. It’s normal to have bias and it’s also normal to have no idea that you have it, to be completely unaware of it, which is why we call it an unconscious.
Cognitive biases are really our mind’s playing tricks on us and trying to convince us that we’re right, without any good reason. That’s why we call it unconscious, and there are actually more than 150 different unconscious biases out there. So what we’ve done in NLI is we’ve corralled that 150 plus into something we call the SEEDS model. It’s a way to think about, to understand, and to talk about the types of biases that affect the decisions we make, and the biases do affect our decisions. The acronym SEEDS stands for the five categories that we’ve put those biases into; similarity, expedience, experience, distance, and safety. But these biases are in your brain. They’re working all the time. They affect everything we say, do, and create. They always have been for all of us. So it stands to reason that most of rather all of the things that we communicate and create and innovate have some bias baked in. If humans existed in isolation, if we never evolved beyond hunter gatherers, that might not be a problem. We would just have great tools to keep ourselves alive, the don’t die prerogative.
But we did evolve, and that put us on a continuum from biology to bigotry, from unconscious bias to conscious bias. As we evolved, conscious bias has become a part of those same systems, those solutions, those statements. It’s baked into. It’s normalized. We have subjective assumptions that we are holding as objective truths, and that’s what we’re going to talk about today. We’re going to talk about how that happened. Let’s start by finding out how we got here. Let’s do that by asking two questions. What are the established systems where bias lives and grows, and what have those systems produced that have furthered the misperceptions that we accept as truths? For that, I’m going to turn to Dr. Lisa Coleman, who has lots of research on this. She’s going to tell us a little bit about it.
[00:05:08] LC: Yes. Thank you so much. I’m going to talk a little bit about the history and thinking about the history of where we have been, where we might think we have come from. So today, I’m going to talk a little bit about a brief history of the systems of racialized legacies. I’m going to begin by talking about a sort of the selected mapping of the beginnings of racialized science. So when we’re thinking about racialized science, what I’m going to talk today about is sort of the history of how science, it was married with racialized patterns of understanding and an inquiry. So we go back in time, what we can. One of the things I want to say from the very early points of this is I’m going to map this across Europe and the United States to make connections so that you understand that this was not just about the US. In fact, it’s very much about the formation of US.
I used to teach a course called The Making of a Nation, 1790 to 1924. For those of you who are familiar, 1790 is when whiteness is conflated with citizenry and 1924 is, again, with the National Origins Act. But what happens in the midst of this is a pattern of science. So let’s start by looking at some of the scientists who contributed to this. So we think about Carlos, Petrus, Johann, Samuel, Francis, and J. Marion Sims. I’m going to just talk a little bit about them. So what we have in the beginning is this delineation of what begins in the 18th century from the 17th century of categorizations of racial categories; European, American, Asiatic, and African.
As we move throughout the 18th century, what we begin to have is the establishment of things like the standards of beauty and also things like the idea of an original race that’s going to later inform the 1790 decision. If you think of the work of Blumenbach, the term Caucasian in 1795 to describe mankind, and that’s the Mt. Caucasus, and then the determination that this is the original race and therefore the most beautiful, all standards to that will be measured against this Caucasian term from 1794. Then we come to Samuel Morton, who theorized in the mid-1800s around intelligence, right? This has to do with brain sizes and what that meant in terms of thinking about racist skull superiority and inferiority and the measurements. Why this is important is this is really where you begin the scientific databases and those kinds of things that are going to be recorded throughout time. Those measurements are going to be used again and again and again. We can think about the 1994 publication of The Bell Curve, and I will come to that later.
We get to Francis Galton, who then coined the term eugenics, to encompass the idea of modification and selection through selective breeding. What this really meant was eugenics was the time to really say that there was a superior race, and we needed to really double down on that idea, and I’m going to talk a little bit more about the specificity of eugenics and phenomenology. J. Marion Sims, the father of American gynecology, we cannot leave him out because he was so important to understanding the ways in which gender and race, and we think about the intersectionality. J. Marion Sims is known as the father of American gynecology. He is the one who actually began to think about these repairs for complications in relation to childbirth. As you know, during the 18th and 19th centuries, childbirth, many women were dying from childbirth complications. But what he did, and he also was one of the first people to create the idea of a speculum, it was called the Sims’ speculum. That has been modified over time. However, what is important to note about Sims’ work is that he operated on and performed his procedures and experiments on black women without using anesthesia. These were enslaved black women, and this would provide a model for the kind of when we get to things like Tuskegee later, without consent, those kinds of experimentations that are done particularly on black and indigenous bodies.
So eugenics, the practice of controlled selective breeding. I already told you a little bit about that. But what I would like to say here is that eugenics continues on and in terms of the science of eugenics, right? Then there’s this idea of a superior race. This idea of natural selection is something that, of course, Darwin picks up and other scholars to isolate and say that those who will survive are the breed of the fittest. Then, of course, the idea of who gets to survive is based on resource allocation, etc. Then we’re going to talk a little bit about the study of the heads. So we think about the eugenics and the categorizations. On the left hand side, you can see these categorizations of idiot, imbecile, high-grade imbecile, and moron. How that was made real was through these scientific measurements of skulls. So on the right hand side, you will see different images of skulls.
There are also measurements of skulls across different racial categories and women. What was then decided was that the skulls of Africans were more related to those of animals such as gorillas, etc. Therefore, their brain sizes were smaller. This was depicted in pictures again and again and again. Secondly, that was also true for women and women’s brains. Particularly then for black women, they were often depicted as both, right? Sort of gorilla-ish, and also there’s the Hottentot image we’re going to talk about in a little bit. I want to talk about this because also it’s important to think about the relationship between racialized science and art. This is, of course, one of the original drawings by Da Vinci called the original man, right?
One of the reasons I depict this is because when we think about the work of the Vinci, Da Vinci during his time period and like many others, there is the intersections. When we talk about intersectionality, it’s not just about intersections of race, etc. It’s about the intersections of disciplines in the systems that Janet brought up earlier. These systems are interrelated, so art, science, government, education, history. That’s what I’m going to map out now. So we’re thinking about science and art, they are in communication with one another, and this idea of the original man becomes an image. This image is reproduced throughout art and the idea of the original man. Da Vinci also did a lot of skeletal drawings, and his skeletal drawings provided a map for how we would think of skeletal drawings later.
Then we come back to the skeletal drawings that were taking place during the 18th and early 19th century, and you can see the modeling of these kinds of skeletal images from thinking about we have the image of man, all the way right to the orangutan and the chimpanzee. These images were used to depict black people and then, of course, the image of the original man and the skeletal image as the perfect man. The Venus Hottentot, some of you know Saartjie Baartman and, of course, the work related to her. But the Hottentot image was much bigger than Sarah Baartman, and these were images that were used throughout Europe and the United States to depict black women’s sexuality and the over and hypersexualization of black women. There was also the hypersexualization of black men in the images of buck, etc. These images circulated and, of course, body parts, and it leads to also the dissection of black women. The Venus Hottentot and Sarah Baartman’s body parts, until the ‘90s, were on public view in France in a museum. As a result of public outrage were eventually taken down. But I mentioned this because, of course, the legacies continue today.
Thomas Jefferson, one of our greatest presidents and one of our greatest people who had a lot to say about race. So when we think about racism, science, bias, and governmental policies, this is a quote from Thomas Jefferson. “I advance it therefore, as a suspicion only, that blacks, whether originally a distinct race or made distinct by time and circumstance are inferior to the whites in the endowments, both of body and mind.” Jefferson enacted policies over and over again related to this kind of philosophy. He was, of course, one of the fathers of American democracy. So when we think about the systems again that Janet brought up and how this kind of racist ideology is embedded within our system and pulled out of science, body and mind to go back to those categorizations and delineations.
Then we move to racism sciences and psychiatry and mental health. The advent of psychiatry is brought into the United States through Europe, places like Argentina. Then, of course, we saw great migratory patterns. The formation of psychiatry in the United States, many of have traced this through the original sort of Freud psychology movement to ego psychology, which takes formation in the United States. That particular brand of psychology institutionalizes through psychiatric institutions a kind of superiority complex, superiority around the brain, and what is manifested as normal. What we know if we look at this book from 1840 to 1880 and then, of course, the administrations of lunacy, racism, and the haunting of American psychiatry. What we know is that the legacies of this permeate even today, misdiagnoses. More black people are misdiagnosed with schizophrenia than any other group in the United States. The long legacies of mental health and disparities are there, built on those earlier racist discourses.
This goes to racism, science, bias, and research, and we’re still here; The Bell Curve. We have there Charles Murray, who continues to write today. Then, of course, we have some of the work to debunk this. But basically, The Bell Curve is a measure of intelligence, the idea of a measure of intelligence pseudoscience, and that blacks are on the lower end. As you can see, whites are on the higher end in terms of IQ measurement. These were very standardized tests, and we know the biases and standardized tests. I don’t have time to go through all of that either. Racism, science, bias, and research, Mankind Quarterly, which still exists today. So as we’re moving through time, now we’re getting up through the 1940s. After, of course, Nazism and eugenics, we get to the 1960s, the beginning of this journal, The Mankind Quarterly, which is still in publication today. All I can say is that it was started in Edinburgh, Scotland, again showing this connection between Europe and the United States. It is a white supremacist journal. That’s how it has been described, and it continues to this day to serve as a mouthpiece to promote eugenics and scientific racism and the superiority of the white race.
Legacies of the racial science, and this is Superior: The Return of Science, a new book by Saini, a London-based journalist who’s written this book. What she argues, of course, is that in the 70 years and, of course, the 270 years of biological sciences, we have seen that the social meaning has continued in many ways. While it has been repressed, it still is part of the legacies of what we have to deal with. To go back to what Janet said, the legacies of racial science, we have conscious and unconscious cognitive biases. We go to the work of Claude Steele, stereotype threat. Of course, Mahzarin Banaji and Anthony Greenwald and the unconscious and cognitive biases. I have to give a shout out to Mahzarin, my former colleague from Harvard. I miss you, Mahzarin. I miss you too, Claude. We used to work together.
Anyway, discrimination, violence, and differential treatment and impact. Lots of this. We saw this during COVID, right? Health disparities, misdiagnoses related to mental health. I just mentioned that. We can think about melanoma. Black people can’t tan, so they can’t get skin cancer, right? All kinds of misnomers around the black body, right? We also know that black people and black women in particular are less likely to receive pain medications because of this idea that we are impervious to pain, going back to the Venus Hottentot. Environmental and climate impacts, what we know if we think about asthma and all of these kinds of things and reports that have come out that blacks are more asthmatic than whites. That is not true. But blacks live in climates and environments where there’s more pollution often.
If we think about right here in New Jersey and New York, one of the black indigenous populations, the oldest black and indigenous population has the most pollution in the United States right outside of their region, asthma and all kinds of other health complications. Uneven emerging technologies, whether that’s the digital divide, and we know. We meet people on their smartphones. We know 42% of low-income Americans are using smartphones, and we know the intersections with race and poverty. Algorithms and bias, we’ve seen it through Google. We’ve seen the people who’ve had to leave Google and all of those kinds of things. There is so much more. Again, I’m just looking at the time. I’m definitely out of my time. So I would just like to say when we think about this, we have to, again, think about these legacies, the systems, and how they inform where we are today. We’re going to talk about some solutions as we go forward.
[00:18:20] JS: Wow, Dr. Coleman. My head’s blowing off. I’m sure a lot of people’s are. I think the thing we want to think about and we want to take from this is we got here for a reason. What we believe to be, what we think is objectively true is really subjective. If you think about it from everything Dr. Coleman said, the very first biased reality that we deal with is race. That’s a social construct. It doesn’t even exist. So you think about it. Just start with that and think about all the things that we’ve built and made and determined and do based on that. So that’s how we got here. But then the question is where are we now? Dr. Coleman started off by saying – She ended actually by saying, and it’s taken to the next section, what are we doing right now that is setting the stage for where we go from here. For that, I’m going to turn to Dr. Byfield because a lot of her work has been in some of the things that Dr. Coleman was talking about, which is like AI and that. So I’m going to turn it over to you, Dr. Byfield.
[00:19:18] NB: Okay. Thank you very much, Janet. Dr. Coleman, thank you for that wonderful presentation. I want to focus some of what I discussed right now on the work that I’ve been doing, the more or less empirical work that I’ve been doing, which exists really at the nexus between media and policing. I’m specifically pointing to that connection between media and policing because of how my work has evolved from the studies about the Central Park case to eventually studying police surveillance in New York City. My initial studies around the Central Park Five case led me to the company illusion that we have not discussed enough and understand enough about the collusion that actually takes place between the media in their reporting of events and policing and the police themselves as an institution.
In terms of thinking about the ways in which some of these biases have permeated our institutions, I want to focus our attention on media and police and this collusion relationship between media and police. The Central Park Five case revolves around a series of incidents that occurred on the night of April 19th, 1989, and there are four major features to that incident. One, a relatively large group of black and Latinx male teenagers were seen gathering at the entrance of Central Park on 110th Street. A smaller group of black and Latinx male teams were seeing running through Central Park in areas by the reservoir and beyond, smaller groups. Three, some people predominantly whites who were running recreationally and involved in other types of leisure-related activities in the park were assaulted allegedly by some of these smaller groups of black and Latinx teens. Four, a lone female jogger was raped and badly beaten later that evening in relatively close proximity to where the events transpired.
Those are the things that we know happened that night and those are the things, I should say, we knew happened that night from the initial unfolding of the incident back in April 19th of 1989. How the press covered that incident though turned it into a whole other affair. Some of the information that you heard about bias and about the history of science actually is implicated in what happened in terms of the coverage. So first and foremost, the coverage that we got from that incident, but the media coverage is, in essence, based on a type of cognitive processing that all media institutions go through to process the phenomena of race. That processing, the relevant features of that processing, are centered on language and what I call media language. So they pay attention to story topics, the structure of the story, quotations, style, semantics, things like that.
But what those general topics translate into when it comes to presenting the information is that it turns around and draws on this history of race science that was presented earlier and this history and understanding of the relationship between the races that is based on a structure really, an institutionalized structure, of white supremacy. So the ideas that were highlighted in the coverage of the story focused on words and terms like wilding, focused on words and terms like cunt, focused on words an terms like feral to describe the young people who were initially accused. So the outcome of what is essentially was presented to the public here is that we watch the media go through a type of cognitive processing that led the public because of the history of what has been presented in the stereotypical representations of black people in the United States, people of color in the United States, leads to a type of public acceptance of what the media is presenting to them.
This outcome leads to the wrongful conviction of primarily black and Latinx youth, and their convictions are not particularly challenged because of the public expectations that existed and by the environment also during that period. So we have the historical associations between race and crime, and we have a particular environment where there’s the step up of the drug wars and things like that. We get public acceptance of stereotypical representations that paint the kids who were accused in this case. Paint them as guilt.
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[00:25:01] NB: Some of the work that I’ve done studying this has led me to certain conclusions. One is a lack of self-awareness that there was implicit bias in media structures, in the internal organizing of the media, the institutional relationships that exist between media and police, for example, and definite lack of self-awareness of the bias that’s being presented in the media products like the stores that are coming out. Another conclusion that I essentially came to was that the actions of the media, meaning the media products that they created, led various elements of the public to a logical conclusion befitting their preconceived ideas about not just the teen’s guilt but the fact that those preconceived ideas came from this long history of a type of race science that’s been practiced and developed in a way and permeates institutions today with the stereotypes that came out of the race science. That allows people to continue seeing these ideas as somehow objective, somehow making sense.
In terms of the coverage of the Central Park case, some of the things that we have not talked about and that has become important for me as we look at the implications of this type of cognitive bias, how it permeates institutions like media and how it advances in the world, particularly in the institutional relationships between policing and media, those things become particularly important when you think about a case like the Central Park Five case. In the context of that case, what we saw in the wake of that case was a stepping up of broken windows policing and a stepping up of the practice of stop and frisk. From my work on that case, it made it very clear to me that I needed to pay closer attention to how stop and frisk operated in the city. First and foremost, it was stop and frisk practices that allowed for the police to detain the night of the attack on the jogger. The stop and frisk practices allowed police to detain Raymond Santana, for example. They didn’t find him in the park but they were able to detain him on suspicion.
We can talk a little bit later in the course of the conversation about how stop and frisk actually works and the laws that it’s based on. But the laws that it’s based on allow for the police to stop and detain people based on suspicious activity and allows them to do that without, the courts have ruled, violating Fourth Amendment rights. So we have a situation where in the wake of the Central Park case, we recognize, I noticed, that stop and frisk escalated tremendously in the city. The stop and frisk practices that existed in the city existed as a type of surveillance. But what is oftentimes not discussed about the way in which stop and frisk has been practiced in New York City, particularly under the Bloomberg administration between 2002 and 2013, when the practice and the way it was done was ruled unconstitutional, that a key element of the way stop and frisk was practiced, it was that it was served as a major data gathering point for people who were stopped. For every stop that was made, police were required to collect data on a UF-250 form.
On this UF-250 form, there are something like 126 data points that end up being assessed. The data that was collected were collected on things like birthmarks, tattoos, not just the precinct where the stop occurred, the actual physical location of the stop if you were stopped with other people in the group and if you were stopped and not just stopped with other people in the group. But then what we saw also on this new UF-250 form were the reasons for the stop, reasons such as wearing clothing that was out of season for the time in which the stop was made. For example, another highly used reason for stops during this height of the stop and frisk practice under the Bloomberg administration was furtive glances. The only thing I can conclude from this discussion of furtive glances is that people seem shifty in their appearance.
We know we can trace some of that to historically how black people have been stereotyped. So part of what we see on the UF-250 form in terms of the data that was gathered on the form is that data was now being gathered on black people, Latinx people, that met some kind of cultural stereotype also about who these people are and what they represent. Of course, if this data is being gathered, then you know it’s being gathered for a purpose. So one of the purposes for gathering this data, of course, was the police’s argument is to bring crime rates down. But what we also know is that this data that has been gathered was used to help develop now the algorithms for assessing and used to develop the algorithms that are being used and have been developed to do data analytics of crime data in the city.
One of the things you have to keep in mind about the data that was gathered during stop and frisk, particularly under the Bloomberg administration now, is that this data was gathered based on somewhere in the vicinity of five million stops. Approximately 90% were of black and Latinx people. Of those people stopped, 90% were released because they were innocent. There was no real reason for the stop. Their stop didn’t even meet the level of this suspicion, so they couldn’t be detained. So we have algorithms being developed based on data gathered from people who were not really engaged in any illegal activity whatsoever, not even activity that met the level of suspicious behavior.
[00:31:36] JS: So does that system now exist? I’d like to ask Dr. Coleman to step in on this too. How does all that history – You mentioned the AI that we use right now. Let’s talk very quickly about how all this factors into even the systems that all of us use every day because I think it’s really easy for us to say, “Well, yeah. Police do that, whatever.” But I believe and I think your research shows us too and some of the work you’ve done too, Dr. Coleman, is that these systems are also generating data and fueling the data and the systems that we use normally. I mean, I may not get caught up in today’s search but I remember reading an article several years ago about how AI and face recognition, the systems don’t read skin color. AI that generates systems like resumes, they kick out women’s resumes. When the Amazon made it a couple of years ago, they created AI that kicked out the resumes of women if they have women’s colleges on it.
In the world that I live in, in communications and marketing, you see it in targeted advertising. Now, we have something called web lining, which is analogous to redlining in the housing industry, where you get shown ads, those kinds of things, so ads playing into all those things. The question is what are we going to do about this? I mean, we’re here now. We got here. We know we got here, and it’s built in. It’s baked into the system. It’s the gremlin in the machine. What can we possibly do about this? That’s something I’m interested in because when we talk about it in terms of business and in those spaces too, what are we going to do? How do we address this? I know that from the standpoint of bias at NLI, we say you mitigate the bias. You have to consciously mitigate it.
[00:33:24] LC: Let me jump in just quickly because I think that one of the things that you’re bringing up is about bad data in is bad data out.
[00:33:31] NB: Bad data out.
[00:33:33] LC: So when Dr. Byfield is talking about the ways in which we think about policing and systems, the bad data in means bad data about the criminality of blackness. Then there’s bad output, right? That’s part of the Central Park Five and what happens and these kinds of in the systems of policing. Similarly, my first major was computer science. So when we put data in, our data is fooled with the biases of who we are, right? When people talk about diversity of thought, I would say this. It’s a great concept, but what we know our thoughts are formed by who we are, our backgrounds, our experiences, etc. So are our biases, right? So if you have a brain, you have bias. That’s about our cognition. As a result, the way in which we formulate that data often mean that then the way we create algorithms, etc., to go back to what you said earlier, Janet, is built in subjectivity and built in subjectivity that is biased based on racial science.
There is work by Safiya Noble and by Virginia Eubanks, Algorithms of Oppression and Automating Inequality. I cannot say enough about these two books. I did an interview with Safiya Noble, just really talking about an outlining how this happens. Now, how do we address this? I want to make sure that I talk about Ruha Benjamin Ruha Benjamin is a sociologist and Associate Professor of African American Studies at Princeton. She’s called this development the New Jim Code, as opposed to the New Jim Crow, which I have to say I just absolutely love and really thinking about that idea, right? Because the Jim Crow, of course, was about segregation, digital. I mean, divide, divisions, etc. What we know now is this is way beyond a digital divide. We’re talking about innovation divide. We’re talking about the future of work and lack of access. That’s what we’re talking about now.
What Dr. Benjamin has done is she has founded something called the Just Data Lab at Princeton to bring together students, educators, activists, and artists to develop critical and new ways of approaching data. She, like Safiya Noble and Virginia Eubanks and others, and we have something called our Public Institute of Knowledge At NYU, where we too are rethinking this creation of knowledge and how knowledge gets created for this public dissemination. The last thing I’ll say, and turn it back to Dr. Byfield, is we really also have to think about the ways in which these biases are also impacting, because I have to say this, the future of work. This is the most diverse generations we will ever have. We’ve ever had, not will ever have. Have ever had. What we know is if we want our companies to be successful, etc., I always say this, nobody wants to be Blockbuster, Netscape, or AltaVista. For the people who are too young, who don’t even know what those companies are, they don’t exist anymore.
[00:36:30] JS: There’s a reason why.
[00:36:31] LC: Right. Part of the reason they don’t exist is they did not look at the markets and emerging markets. In particular, if we look at Blockbuster, middle and lower income families, and then paying $50 versus $10.99 at Redbox. But my point is, is that we really do have to think about innovations and emerging innovations and how we debunk racial science. Stereotype threat and mitigating bias, and mitigating bias is hard work, and you have to have systems and strategies to go back to what Dr. Byfield said.
[00:37:01] NB: Part of what I think we also need to think about is that we’re looking at, and this is why I think it’s so – I’m just going to backtrack for a moment to bring in some of what Dr. Coleman brought up earlier. We are looking at a system of science that’s grounded in categorizations and classifications. They operate well in the context of biases because it’s the categories that people have in their minds as they approach the world that sort of instigate them to lean into the biases. “Oh, I know what that represents, that category of thing represents.” But our very world of science, everything about science, so the first person on the list of scientists that Dr. Coleman mentioned. Linnaeus, the Swedish Linnaeus. This is – He’s a naturalist, right?
[00:37:53] LC: That’s right.
[00:37:53] NB: What he’s doing is he’s putting together categories of plants. This is a rose because it has these types of petals. As we go through life, and that gets incorporated into racial classifications, the meanings that are assigned to racial groups and categories now get built into all of the technologies that we develop because the technologies are nothing but ideas initially. They’re concepts that end up getting –
[00:38:23] LC: Reified.
[00:38:24] NB: Right, exactly.
[00:38:25] LC: Reified and materialized in things like policing.
[00:38:29] NB: So what we have, for example, now in the world of policing, which, for me, is something that we’ve got to pay really close attention to, is the fact that we’re dealing with a global platform. This digital platform that we’re living on that allows for these technologies to take off is a global platform. This is a very international global development. That means that the ways in which we understand race here in America and the meanings that we are assigning to them are going to be multiplied and built in and fed into the system because we know that different cultures have dealt with the issue of race and the meanings assigned to race and things like that differently.
But the more and more we globalize the system and the more and more it allows us to, one, change meanings quickly. So that goes back to your question, Janet, about so what does this mean. It means that we have to start dealing with the fact that the science that’s at the foundation of all our work is part of the problem because of the ways it manages classifications and categorizations, one.
[00:39:41] LC: Exactly. Part of that is if we’re going to deal with that, then we actually have to get – One of the things that I say a lot is when people – I’m a chief diversity officer sort of in innovation and that, but what I say is that in South Africa, they call me a chief transformation officer. That’s the title. Transformation it’s South Africa starts with truth and reconciliation, but you have to have the truth to reconcile, right? Truth is we have to talk about these foundations, how they’re embedded into pharmacology, into the various practices of every scientific, right? Then not only that, to go back to what I also was trying to point out, science is embedded in art. Science is embedded in government. Science is embedded in policy decisions, in incarceration, in mental health, in all of the fabric. We use science as the arbiter of proof and to make things valid. In that way, we really have to an unearth how science –
Some of the work I’ve been doing right now is all around diversity and belonging and inclusion, right? I just wrote this article on belonging because everybody keeps talking about belonging like Kumbaya. We’re all supposed to get along. But we can’t. That’s not what it’s about. You have to have contestation and debate. What we know from really good science is if you have contestation and debate, you can get to better answers, better innovation. Often, we have not included that contestation and debate, right? We’ve we stifled that and only use what you’ve talked about before, Janet, confirmation bias and other kinds of things, right?
[00:41:16] JS: As we talk about at NLI, we talk about inclusion and we talk about how it starts with agreeing that you understand that conflict is a part of it. We talk about diverse teams, and everybody believes diverse teams should work well. By all logic, they don’t immediately. They’re not comfortable. Diversity is not comfortable because it is creative conflict. We talk about innovation. We cannot talk about any logical innovation that just came up because somebody didn’t have a creative idea that had some – It was conflict with something else. That’s how innovation happens. But the issue is we have to know that a lot of what’s baked into this started off bias, a lot of things that we accept is true.
I believe she’s at NYU actually, Dr. Coleman, Meredith Whittaker. I mentioned to her –
[00:42:04] LC: I work with her.
[00:42:06] JS: Wonderful. I love what she said. She says that biases and things and algorithms that we have today replicate historical marginalization. That’s what they do.
[00:42:15] LC: We have to know.
[00:42:16] LC: When these algorithms are going out and doing things, we’re depending on them and we lean in them. We market by them. We police by them. We get jobs by them. Every day, we got to realize what’s at the basis of them. So in order to fix that, we’re talking about what we could do. One of the things I think we have to do is we have to, as voters, as concerned citizens, we have to push out policymakers to hold the people who create these things to transparency. We have to push for that. As communicators, we have to be conscious communicators. We have to recognize that when we say things, what we believe to be true may not actually be true that we are dealing with biases, and we have to start that. We have to question ourselves.
One of the things we also say at NLI is that you can’t catch yourself being biased. So that’s the importance of –
[00:43:04] LC: You need a team for that.
[00:43:07] JS: You need people to help you. That’s why we have to have languages and that’s why we use our SEEDS model, which is great for that. But that’s what we believe when we have to have common languages that are not pointing fingers and calling names because, I mean, it feels good to call names sometimes. Sometimes, calling names is warranted. I won’t argue that. But it doesn’t get you anywhere, so we got to find ways to talk about this and not get as triggered as we do because it’s understandable. But we got to realize that what we know to be true is that it’s untrue. So we got to start there.
[00:43:36] NB: I think that because we know people are operating with these biases, one of the things that we have to accept is that, as you were mentioning, people are not going to see that they are operating with these biases, one. Two, they actually don’t necessarily have the tools to step outside of it too. The third point I wanted to make is that just giving them additional information does not necessarily help because part of what they’re trapped in is the frameworks that they’re using that these categorizations and classifications are based on. So if they don’t have an alternative framework, they’re not able really to penetrate the trap that they’re in, sort of the discourse that they are in, the if-then proposition that they’re in, this proposition that gets fed into our technologies even because the technologies organize our world in such a way that leads to the outcomes that reinforce what the biases are telling them. It’s really, really hard for them to step outside of this. So part of what this work has to include is a push to transform the science. I keep –
[00:44:49] LC: Transformation and embedded within this conversation are systems of power, right? No one wants to give up power, right? But they have to have intentional strategies to redirect power. One of my examples that I use, which is just really small, during the Obama administration, the men were talking over the women. This happens sometimes. Men think their ideas are better than women because their brains are bigger, because it was proved during eugenics. That’s a joke. So as a result, one of the things that the women did during the Obama administration is they bonded with each other and created a reference structure. So when a woman would speak, they would say, “Janet, if a man would steal Janet’s idea.” Like Bob would start saying, “Oh, I had this great idea,” and Janet just said the idea, then one of the women would say, “Oh, no. Janet, that was a great idea that you said about 10 minutes ago. Would you like to explore that further? And, Natalie, would you like to follow up on that, right?” So they developed an intentional strategy to redirect references and power, right? Power in the room.
So we have to think about those tactical strategies that redirect power because the disruption of power is not easy. One of the things that I write about a lot and have been talking about a lot is we cannot return to the new normal. We need a new different. We need different ways. I have a whole article titled new different, right? Really thinking about strategy, intentionality, sustainability, and transformation around the truth. This is where we need to be thinking about.
[00:46:29] JS: I don’t think I need to make any other closing statements any better than that. I think that you said it. It’s about intentionality, no matter what we’re doing. Intentionality starts with admitting and understanding that the basis of what you believe may not be objectively true. You start there and then you get intentional about, to your points, both of your points, finding a new system, a new structure, a new way of looking at things. That’s how we got to change this and that’s hard. That is really hard because we are asking ourselves not to believe what we truly believe. I think the question you ask yourself always is why do I believe this objectively to reach for it right? If you can’t answer that question, then you got some work to do.
[END OF INTERVIEW]
[00:47:16] SO: Your Brain at Work is produced by the NeuroLeadership Institute. You can help us in making organizations more human by rating, reviewing, and subscribing wherever you get your podcasts. As mentioned on the top of the episode, feel free to look out for any of our webinars on our YouTube channel at youtube.com/neuroleadershipinstitute. Our producers for today are Matt Holodak, Danielle Kirshenblat, Ted Bauer, and me, Shadé Olasimbo. Original music is by Grant Subritsky and logo design is by Catch Wear. We’ll see you here next time.