 Good evening, everyone. My name is Maria Nicanor. I'm the director here at Cooper Hewitt. I'm really, really thrilled to welcome you all for one of our programs in conjunction with the Deconstructing Power WEB Du Bois at the 1900s Will It's Fair exhibition that I hope you all have had a chance to see in the galleries upstairs. It's had an extraordinary run here at the museum and it's in its last weeks. You can still catch it until May 29th and tonight's program celebrates the exhibition in a very, very special way. Before I introduce you to the speakers, I wanted to make sure to say that both the exhibition and tonight's program have many powerful underlying messages and you surely will have picked up on some of those if you've seen the show, but one of them is that data is important and that representation is important and visualizing it in the right ways to elevate the stories that count is crucial in the world that we live in and is even more important if we want to inspire any kind of change. Designers have a role to play in that part, which is why I'm so happy that we're having a conversation like tonight's in the National Museum of Design of the United States because we believe that designers have a very important role in that conversation. I also want to thank tonight the extraordinary learning team here at the museum and my colleague Kirsten McNally who did some magic to make all of this happen tonight and also have to recognize the fact that we couldn't be hosting this stellar lineup of speakers and scholars and designers if it wasn't for the generosity of our supporters. So I truly want to thank our friends at the Heartland Foundation as well as Denise and little Phil Sobel for the incredible support that they have provided for the exhibition and for the program tonight as well. So with that, let me introduce you to our speakers. As you know, we're hosting a conversation tonight with our guest of honor Mona Shalabi and two of the shows Curators, Devin Simmerman and Cristina de Leon. So it's your infrared treat. Let me start first with Cristina. Cristina is our own associate curator of Latino design here at Cooper Hewitt and currently also serves as the acting deputy director of Curatorial. Since 2017, she has grown our museum's collection of U.S. Latino and Latin American design and has also organized exhibitions and public programs and bilingual digital content for the museum. In 2021, Cristina produced Cooper Hewitt's first feature-length documentary film, which was titled Mud Frontier Architecture at the Borderlands, which has been screened widely internationally. You can watch it online on our website as well. Devin Simmerman is an associate curator of modern and contemporary art at the Ugunkit Museum of American Art in Maine. His research focuses on transatlantic networks that fueled modernism in art and design, and as well as being the curator for deconstructing power, he is also the curator of several forthcoming exhibitions, including spontaneous generation in the art of Liam Lee and Everbot went down the line. And finally, I am so, so pleased to introduce you also to Mona Shalabi who is of course an award winner, writer and illustrator. If you read The New York Times or The New Yorker or The Guardian where she's currently the data editor or if you get some of your content from Netflix or from BBC or from NPR, essentially if you don't live in a closet, you've heard and seen the work of Mona and you are as excited as I am personally to have her here tonight as someone who uses words and color and sound to help us all digest and understand and visualize this very complex and sometimes complicated and troubling world that we live in. Mona does all of that mixing beauty and irony and humor and kindness, but also empathy with an arresting strength and sense of urgency which I think is what makes her work so powerful. Her work has earned her a Pulitzer Prize, a fellowship at the British Science Association, an Emmy nomination and recognition from the Royal Statistical Society, this last one I find the most impressive actually. In recent years her work has been exhibited at the Tate, at the Brooklyn Museum, the Design Museum in London and also the House of Illustration. She is currently writing a book about the ways we talk about money which I understand will also be a documentary series and she's also the Executive Producer and Creative Director of an upcoming animated TV show with Rami Yusef, A24 and Amazon Studios. So with all of that I'm going to lead you to welcome and help me welcome our three speakers tonight, Mona, Christina and Devan. Thank you. Hi. How you doing? Hi. Welcome. Thank you all for being here. What a great way to get Cooper Hewitt Public Programs going a full room and all here to celebrate a great exhibition and Mona's work which we're so so excited to be able to talk about today. So thank you all for taking the time. Yeah. So I thought we would just start off for those who haven't made it upstairs. I do hope at some point, if not, I don't think you can this evening or at least you'll have to be quick and avoid a few guards to try to make it up before the end of the night. Deconstructing Power, W.E.B. Du Bois at the 1900 World's Fair was really a collaborative endeavor with myself, Leneesa Kitchener at the Library of Congress, Christina De Leon and Yao Feng Yu here at the Cooper Hewitt. And for those who haven't seen the show or who have, the exhibition looks at the 1900 World's Fair as an item, an object of design, something designed to communicate messages, to communicate ideas and ideologies about the world and to look at how designers navigated, explored, participated or deconstructed many of those messages that were communicated. And central to that narrative is the work of W.E.B. Du Bois and his students at Lenea University and the really remarkable data visualizations that they produced and exhibited at the 1900 World's Fair. This is the first time that the original data visualizations have left the Library of Congress. So it's been about 120 years or so and they're just really remarkable, remarkable items and objects and so important. And they are the launching point for our discussion tonight. And so this wonderful juxtaposition and kind of my first question was, Mona, how did you kind of first encounter the data visualizations and kind of responded to them? Yeah, this is not helpful, but I don't really remember. I don't remember the first time that I found Du Bois's work, but I remember seeing a Library of Congress link where you could scroll through each of the pieces. And what's incredible, as you know, obviously having put together the exhibition, is it wasn't just these data visualizations, but he really, really went out of his way to ensure that photographs were a key part of the World's Fair. And I think that when you look at it on the Library of Congress, it's kind of amazing. Everything isn't really in order, like the reference numbers. So it kind of goes photograph, incredible data visualization, photograph, photograph, photograph data visualization. So you're kind of forced to view them in conversation with one another. And I just remember thinking it was the best journalism I had ever, ever, ever seen. And I think that's still the case even now. I lost a day. But in a good way, in a good way, I was just fully immersed in it. Yeah. A good rabbit hole is always an amazing thing. What then led you to produce the series kind of responding to the data visualizations? Yeah. I had been talking to an editor about how much I loved his work. And I think he wanted to commission the illustrations. And actually, I worked with another organization who wanted to kind of film me making them. And it was a disaster because to make them is incredibly hard. And so as we were filming, I was just like, oh, I'd like fucked up this part. Like, do we have TIPX? Like, I don't know how to like fix it. And again, this is my first time just like literally 20 minutes ago seeing the work for the very first time in real life. And to view it in real life is phenomenal. We were just like, how did he do it without Photoshop? Like this has been Photoshopped to like fix my mistakes, you know, and the idea of doing this hand lettering at that scale is just mind blowing. It's amazing. Yeah. And even like, yeah, his is so much nicer than mine, which I shouldn't be surprised about. But yeah. What I was really intrigued and you know, walking through and seeing them in person and kind of getting a hint in the images and something that seemed what you innately gravitated to, whether intentionally or not, is how hand drawn, obviously because they are, but like, your hand is present in the data visualization sets that you produced. And one of the things that I always find so stunning, I've seen the ones in real life is the hands of all of the students. And we talked about this briefly, but how do you put humanity into data? And how did you think, or were you thinking about that when you were working on this or? I was. Yeah. I think again, the photographs are a key part of like bringing in some of that humanity. But I think actually the imprecision is actually really important. So even on his, it's like, it's phenomenal and it's perfect. But you can tell that that lettering hasn't been done with a typewriter. And I think that's really powerful and important. It's reminding the viewer that like humans are both responsible for the active collecting data, for the active selecting data, for the active visualizing data. And I never want to lose that in the work that I'm creating. So computer generated graphics, I do think can feel quite inhumane, quite clinical because you kind of lose sight sometimes of the humanity in it. Yeah. Yeah, that sort of technocratic precision. And do you ever think through that sense of authenticity or truthfulness that's embedded in something that's precisely drawn and then you go about, in hand drawing these things, it kind of subverts that sort of structure? Yeah. And I think part of it also requires like a little bit of swallowing your ego. Because when I first started to create this, right, the work wasn't really taken seriously, it was viewed as less precise. But what people don't realize, and I don't know if I actually did it with the Du Bois pieces, but I do in every other piece that I do because these were done on like a thicker kind of paper. But normally, I create something with ink, I mean, it depends a little bit that kind of style, but typically I'll hand draw something. I create a computer-generated graphic, and then I go into Photoshop and I line it up with exactly like pixel for pixel, whatever a computer-generated graphic would have been, so it's as precise as any bar chart, line chart that you're going to see. But I think I'm not telling you that I'm not putting a decimal place on it. I'm not implying the precision because I actually think the reality of the underlying data, even now, like none of these things, even despite us like progressing so much in terms of our data gathering techniques, none of these things deserve a decimal place on them. We still don't know them to a decimal place. And so to create the hand-drawn graphic, I think also means that what you walk away with is a sense of relative scale, and that's what I'm trying to show, actually. It's not about, you don't need to remember 25%. What you need to remember, I guess, is the relative size of the rate triangle relative to like the beige one. And that's the important thing to take away. Yeah. Could you, you know, talk about how you respond to the way Du Bois sort of structured his data visualizations? Because we were chatting about this a bit and it's sort of legibility. Yeah, it's really important. So I hate the word infographic. I think it's like a really, really bad word. And that's because when I look at an infographic, again, there's like a lot of ego. It's like, look at how much we can like cram in, rather than thinking about the viewer. But also I think when you look at infographics, sometimes you don't really know where to begin and where to end. Like, do I start in the top right hand corner? Do I start down here? And the thing that he does that's so phenomenal in his work is he's really, which is something I strive for as well. He's like diminished the number of words on the graphic, which is super important in terms of accessibility, people who might speak English as a second language, people who might have a different educational backgrounds. But also part of the legibility is that you understand when you look at this graphic very quickly, I need to read the top part first. Okay, I've done that. Now I need to read like, what's written next to the red circle, then what's written next to the blue circle and your eye can dart back and forth. And you don't have to spend an hour with this to get your head around it. And I think, for both, I mean, I really don't want to presume what Du Bois was thinking. But I assume that he also had the same preoccupation as I do, which is, I don't have your attention for long. I just assume people are going to lose interest. And so I feel this sense of urgency of like, before I lose you, I have to really quickly communicate something. It's quite an indulgent thing to assume that anyone gives a shit about what you're saying. Like, yeah, and I think he assumes probably he's working against a tide. Yeah, you know, I try to think about the World's Fair attracted something like 50 million people, you're in a convention center, like setting. Yeah. And how do you get someone to stop? Yeah, let alone how do you get a white European audience to stop? And how do you get them to bite into this data and actually sit with it and think about it? It's a tall, tall order. And it is the anonymous passerby. You don't have a known audience per se, as you reach out. Do you think about audience? Yeah. And I think actually maybe that's a way in which we potentially diverge. So I think actually when I started out in journalism, I was thinking about our modern equivalent of the white rich European wandering through, which is like, I guess, especially in the lead up to the 2016 election, I was very concerned working at the Guardian, that the people who read my work are people who already agree with everything I'm trying to say, right? Like I'm talking about gun control and people are reading these charts and they're fully on board already, like what am I really achieving here? And I think that 2016 was a really pivotal point in my career, where watching Trump win and watching things like the Muslim ban come in, I was like, okay, no, actually, educating my side is actually really, really critical. I'm not trying to convert anyone that's a waste of my time and energy. Helping somebody understand these are your legal rights as a migrant. These are your legal rights. This is a way to go about getting an abortion if you can't get an abortion. That is a really important part of journalism. And it isn't about changing anyone's mind. So I'm not at this point, if people aren't convinced about decarceration, if they're not convinced about the police state, I don't know. I'm not going to convince anyone. That's not part of my job. Yeah. Well, I think we spoke about this going through the show is, Du Bois was struggling with this hope, almost a utopian hope that data and science and rational argumentation could deconstruct some of these cultural myths and pressures. And that intensity is often over or superseding any kind of rationality or rational argument. People don't want to have a certain type of rational argument or internal biases, bases, wants for affirmation is sort of subsumed and missed with that. But then let me ask you, as Curia has spent so much time in this archive, do you think he changed people's minds in 1900? Yes and no. Exactly. Well, because again, you start thinking about reception too. And so, he, the exhibit got awarded a gold medal at the fair. It was triumphantly heralded in the black press and was largely suppressed in the white reviews of the exhibition. And then you kind of enter into this parallel discussion of what communities, what arguments are you trying to make. And I think you see that shift when he writes The Souls of Black Folk, three years later, something that's deeply embedded in his own personal experience and lived experience specifically, devolves. Yeah. And just to be clear, writing for your own community doesn't mean you fail in any way. Like that work is crucial. Yeah. Yeah. This is another data visualization that I just kind of was struck by as we were going through. I really didn't notice the parallel until I was sent the slides ahead of time and I was like, oh, wow. Yeah. So for those who haven't seen the show, this is a really, really stunning data visualization by Du Bois, which charts the exponential growth of black asset valuation after reconstruction. And what he does is embed within this simple linear data line cultural forces, including the rise and existence of the KKK during reconstruction, and then charts other events like political unrest. But then the dip occurs within the context of lynching, which he names, and prescriptive laws, Jim Crow laws at the end, and introduces this sort of data visualization, but also these external forces. And his willingness to sort of both chart these often suppressed or unspoken elements of American society are embedded within these other exponentially kind of hopeful visions. But what, both jumping off of that and thinking through this is one, I think KKK groups and membership and participation, especially as someone who experienced the 1990s and some of the mythologies about race and existence and politics within the 90s and late 80s. But the growth of KKK groups threw into the 2000s. What attracted you to that data set? I don't know about the tracks it is. Again, this is like a really, the visualization is as much about showing the thing as showing our understanding of the thing. So like this was data was collected by the Southern Poverty Law Center. That's the right discreet, that's, yeah, yeah. And again, like the data was actually pretty inaccessible. It was in a PDF. So I had to spend time like typing out one number at a time and the years to like create this. And then I just wanted to like, I just, you know, you have this series of numbers that are in a table. I quickly turn it into a line chart and I immediately saw like this shape. But I also think surely this is exaggerated, like surely I just, I view this as inherently imprecise. Like, how is it that we are tracking the number of KKK groups in this country? Like, how have those methods of measurement? Is this about tracking how we spent more resources in measuring it? And then the allocation of resources to measure this dropped off a cliff? Or is it that the number of active KKK groups dropped off a cliff? Or is it that they, they continue to proliferate in different ways? I don't know. And then it's like, what is my job as a journalist? Should I be figuring out what, should I know what I don't know about this data before I put it out? Or is this like the beginning point of a conversation? I don't know. I didn't answer your question. Did I? No, sorry. No, but how, how, so this is actually really interesting. But when you're collecting data and when you're thinking about data sets that you're approaching, there is always the risk of well, what am I marshaling data to? And when can that narrative that I'm laying out with the data that I'm visualizing potentially turn or get out of or lose the message that you're putting out or seeking to sort of skew the story that you're trying to tell? Scary. Yeah. Yeah. I mean, we talked about this just briefly. Before we came up, we were talking about the risks of misinformation, right? And how, again, it's something I think about all the time is like, how could this one visualization be interpreted in a different way? But again, the stakes of that change, depending on the subject that you're talking about. So like, I don't know, I did, I did an illustration I was looking at this morning for something else that was about, like, the percentage distribution of pubic hair grooming injuries. Like, if people misunderstand where they're going to injure themselves when they're grooming their pubic hair, it's not the end of the world. Like the stakes are relatively low. But then when COVID happened, I was like, if I'm miscommunicating data about the efficacy of vaccines or the efficacy of masks, I could literally, and I felt that on a human level anyway, just, I felt so overwhelmed by this idea of like the fragility of everything and this idea that I could cause harm just even to my neighbors. And then the idea of my that like really bled into the idea of my work and the recognition that I do have some kind of platform. And if I get things wrong, it's potentially disastrous. So I think about that all the time. And there's, you can take steps to try to minimize that, right? So I have a group text thread with friends who do nothing to do with data, nothing to do with journalism. And I don't provide them with any context. I say, here's an image. What do you understand from it? So you can do that. But obviously, it's still imprecise. It's still, you know, there are dynamics in all of our friendship groups that mean that we're still coalescing around certain, I don't know, certain communities that aren't representative of everyone who's going to see the work. So I don't know. For me, my goals have shifted from this idea of like doing good, which feels so lofty and arrogant, this idea of minimizing harm. So I'm always thinking of like, what are the ways in which this work could be harmful? Does this imply that the KKK is going away and no one really needs to worry anymore? I don't think that is what it's implying because there's that crazy second spike. But that's something that I think about before I post it. Yeah, I still didn't answer the question, did I? It's okay. We're just going to have a conversation. And, you know, I guess this comes to, again, this question of your thought process when not just maybe limiting or controlling because data has so much power of the types or where your stories are going, but then how you construct a narrative in the data that you were then illustrating. So I felt really compelled both because of the kind of correlation between DuPois work, but just how this average voter wait times and how you use individual voters and create these curving linear line. How do you go about thinking of, here, I'm going to set this story out and play with it? Yeah. So I'd never seen these both side by side. And I didn't even realize that I had plagiarized him. Was I thinking of him when I'm like, I genuinely thought I'd come up with a really innovative idea all of my own. And maybe he was right there in the back of my head. So when I saw this, I was like, oh, gosh, I wanted to imply this idea of like, it makes sense because often, obviously, when you're standing in line, the queue kind of wraps around like this. So much of the work is about trying to capture a feeling. And that idea of like, literally, I got off a plane like two days ago and you get off at Newark. And again, the immigration process, if you don't have a US passport, oh my God. And you're counting the number of times that the line weaves rounds and you're doing this mental math of like, okay, like, I've been here for 10 minutes. I saw this much of the queue go down how much like, I'm trying to capture that feeling of just frustration in the thing. And so the wrapping around, I actually think it affects the legibility of the chart. It actually makes it you have to do a little bit more work. But it does a better job of capturing the feeling. So it's succeeding, at least on that measure. And I think, again, you walk away with the main thing, which is the relative scale, it doesn't actually matter exactly. And to some extent, it doesn't actually matter exactly how many minutes African Americans are waiting in line, although obviously that does matter. It's the injustice of seeing that number set up against those groups up top that is supposed to invoke a feeling of like, wow, this system is intentionally and deeply broken. I do just want to say one last thing about this one, which is something I think about all the time, is how am I representing gender, and also how am I representing race, right? So like even Latinos, and again, the language that I'm using very often, I'm just grabbing exactly the language that is used in the census. Should I be mimicking that language? Should I be questioning it and pushing it? I don't know. But even Latinos, like, that is not a race. I could have depicted that character in any skin tone. And how I choose to do that is something that I'm always grappling with. Yeah, I don't know. I don't know. But I'm drawn to this, you know, again, because you use something like infographics or data, and it immediately dries the subject out. And your focus on intensity, this kind of emotional resonance, whether or not that that is a powerful tool in data in storytelling, because that is what you're doing, is telling a story through data that resonates. And I think that's something that we shy away from sometimes in journalism, this idea of like trying to elicit an emotion feels inherently manipulative or something. But I view the line between journalism and activism as being a bit more blurry, and I think you can't be depicting this stuff without having some hope of changing it. That feels ludicrous to me. And I think that anger is actually a really critical part of change. I think anger is really underrated. And I think you should feel angry when you look at some of these charts. So speaking of sentences, this was a data visualization that Devin and I, when we were looking through your portfolio, immediately thought, we have to talk about this. For many reasons, one, because I think a big underlining discussion or point in this discussion is the census. How do we use the census in this country? How is it mobilized? How is it disseminated? How is it visualized in many different ways? I think Du Bois is a great example of one person who worked with a group of people to create this incredible set of data visualizations that was talking about really difficult, difficult topics for the United States. And when I think about the census, I think about this part of the census a lot. I'm the curator of Latino design. What does that mean? I feel you in terms of the pressures that you have to represent a type of data. When I was first hired here, there was a lot of pressure to represent what is Latino design? What does it mean for a museum to collect objects that represent the history of a group of people from a very expansive region in the world? And so looking at this data set and thinking about who is represented in the categories of race or ethnicity in the census starting from the 18th century and even to today, the folders, which are so inane, right? That folder that says, that's you. But seeing it in black, white, American, Indian, Alaskan, Asian, other, Hispanic, Hawaii, Pacific Islander, it makes me think a lot also about the breadth of what makes up the United States. What is the United States? How do we understand the U.S. today, not just from a point of race and ethnicity, but also from a point of territory, right? Right when you see Pacific Islander, you think... Wait a second. Yeah. Yeah, yeah, yeah. Are Pacific Islanders Americans? I mean, that is a question that I'm sure many people have asked themselves. And when you look at it, you only get that category until 2020. So I wonder if you could talk a little bit about this data visualization, how you think about census data and how you think through some of the problematic points of interpretation when thinking through... In particular, I think this one is really interesting because you're looking at an expansive time of census record keeping. Yeah. So it's interesting that you even described it as a data visualization because I guess technically it isn't. There's no data there. I guess you're right. I guess I think about it as the data of data. Yeah. It's information design and it's interesting because I think the Census Bureau actually published this beautiful war chart kind of thing that explains the evolution of these different categories. And this is obviously massively simplified, but I think for me anyway, you walk away with a few different takeaways, right? You see at what point various categories appear. You see which categories we're left with. And fascinatingly, you see this thing of some of the categories disappearing and then reappearing, which is maybe one of the juiciest parts of this. So I think I'm inherently drawn to this because my ethnicity is Arab, right? And we are never, never, never, never, never on anything. I literally went to a dermatologist office where the skin form was, I think it was 200 categories and not one of them was so detailed. And I went to go and speak to her and she was Egyptian. And I was like, babe, why, why, what, what? And she was like, oh, no, we just downloaded off the internet. Like it just isn't. And the history of that is really, really fascinating, right? Like if I understand correctly, I should really figure out the, the precision of this story because I tell it a lot and I'm still not, I'm still a little bit vague on the details. But if I understand correctly, I believe that in this country, they had finally coalesced about the idea of adding Arab to the census. I think the category was actually going to be Middle Eastern, North African, which I want to circle back to. And they had agreed upon it in like the summer of 2001. And then September 11th happened and they were like, yeah, let's not bother adding this to the census because no one's going to want to tick this box. And they're right, right? Like all of my family members, anytime they get given a government form, they're like, no, thank you. I don't understand how this is going to be used. I don't believe for a second, this is going to, this is going to serve my community. And we have such a history of surveillance. Like, I do not trust this. So I would say, and let me circle back to Middle East, North African thing. It was fascinating. I don't, I don't know if she's here, but we bumped into an incredible geographer when we were just looking at the Du Bois exhibit. Her name was Elizabeth. I don't, I don't think she's down here, but she was talking, Michelle, sorry, Michelle, thank you. She was talking about radical cartography and again, like even the description Middle East, Middle East relative to who? Relative to what? Like, like, I don't think we're Middle East. And again, the grouping of those categories makes a very, of those countries makes varying degrees of sense. So I think a baked into is this idea of like, who are we counting? What are we counting? And the fluidity of these categories that feel neat, like the idea of a folder feels neat, but it's also bullshit. But, but these folders can serve us. And we will know that like, there are, there are rooms that I'm in where it serves me to connect with other people and say, Hey, we're both our, we're both here. We're going to set aside the fact that you're Yemeni, I'm Iraqi. It's fine. It's fine. We just need to like, you know, we need to get this thing done. And that's okay. And so like we, there's a strategy to surviving in this country. And there is a strategy to gathering data. And yeah, it's important to be strategic sometimes if you want to survive and thrive. Well said. This was also one of the data visualizations that I loved. For a lot of different reasons, because I think not only is it speaking to inherently how are we connected and also who hasn't thought of either doing a DNA test or hasn't done one already. And don't do them. Please don't do them. I'm really sorry. And I, I'd love for us to talk about it a little bit more, because I think when you look at this chart, you think, you know, oh, wow, this is really interesting, the hands and the way, you know, everyone's connected. But then you start to think about sort of the more darker points of what it means to share DNA, but also what does it mean to give your DNA out? Very freely to someone, which is immediately where my head went. What does it mean when I swab and then I send it off? And it goes to a bank. So much worse than that. I mean, talk about it. Okay, just really, just please, please, if you haven't already done it, just try to refrain. There are massive consent issues. If your sibling does it, you're in the database, regardless of whether or not you want it to opt in, how like it's madness, 23 amese long-term strategy was always to get into a pharmaceuticals. That was always the goal of the company. They have successfully done that now. So we are very, very close to a point if we're not there already where when you're inquiring about insurance, they already know on the phone with you that you're going to get Alzheimer's in 20 years, but you don't know it, and they can affect your premiums off the basis of that. It is terrifying. And also to go back to the earlier slide for a moment, this idea of being told exactly where you're from, I understand different communities who have had their history stolen from them, the desire to reclaim it. However, the data itself is not that precise. The idea of telling somebody, you are 23% Scottish is really dangerous, because actually the reality, and this is something I try to talk about all the time about, how do you communicate the numbers on either side of that? Because the reality is what we can tell from those DNA tests is actually you are probably somewhere between 5% and 40% Scottish, let's say. And those are two radically different numbers, but no one wants that. They want to be able to go to the bar with their friends and say, hey, I just found out I'm 23% Scottish. And actually, why do you want, again, setting aside communities who have had their history stolen from them, if you are somebody who just wants to find a white person going about your business in this country and you are interested in the fact that you're 23% Scottish, why? I'm curious why you want to know that, and what that means for you. And actually, I'm really concerned about the rise of eugenics, which again is something that Du Bois was thinking about all the time. How do you present this information about things like different racial and ethnic groups in this country without implying that any of those differences are innate? How do you show that all of these differences are systemic? All of them are the result of structural differences that exist in our lives, and nothing about this is biological. And that's something that I worry about all the time, whether I'm showing, I don't know, like even the voter wait times, you know, there'll be a racist person who looks at that who thinks that it means something different to what it actually means. Yeah, it's fascinating. I think a lot about something that we talked about upstairs, which is how do you express the blind spots in the data? And I wonder if you could talk about that a little bit, because it's an interesting discussion when you're thinking about responsibility as a journalist and as someone who works so closely with numbers, which as a society, we often equate as fact, right? Like, here's a number, it's been calculated in this, you know, mythical way, or maybe we know exactly how it's calculated, or maybe we're just taking the number because, you know, I saw it in the New York Times or I saw it in whatever communication strategy that I use. But, you know, there's a lot more steps that go into creating and that story, putting together those numbers and then sharing it with the public. And, you know, as curators, like, we have this idea of like, we want to do a show and we're in our bunker and we're writing and we're thinking and a lot of that work is done in a whole, you know, like we're in our own orbit. And then you put it out there to the public and you're like, did I get every single fact correct? Is someone going to say no, it wasn't that date, it was this or no, you're completely misinterpreting what this work was all about. So I wonder if you could talk a little bit about that because it's hard. So I guess to build on your parallel there, data collection just like curation is expensive, right? Like it costs a lot of money to do these surveys. And therefore, because of that, the data reflects the existing systems of power because it reflects where do we attribute value? Which communities do we think are worth counting? Which diseases do we think are worth counting? Are we measuring tap water, lead in tap water in this community or this community? And so part of my work is about trying to show the data that doesn't get collected or the data that is extra and precise. There's an artist whose name I always forget that is incredible. It's just it's a visual artist who doesn't have a background in statistics, who created a piece called like the Library of Missing Data Sets and it's a filing cabinet with labels on it that says things like black maternal mortality rates in city X. It's all the data sets that no one has collected and they're just empty files. It's beautiful. Anyway, so to give you a really specific example of the ways I'm trying to communicate uncertainty, there are a range of tools that we use in data visualization that we've used to do this all the time. Instead of a solid line, can we do a dashed line to communicate uncertainty? There was another mass shooting that was in Atlanta where it was a massage parlor that specifically targeted Asian women. So for that, I wanted to show actually the vulnerability of being a sex worker and how that puts you at risk. That data doesn't exist. The government doesn't collect data on sex workers much less. Also, I mean, actually, there's really difficult data to collect about how being transgender or gender non-conforming affects your probability of experiencing violence. And many people get misgendered when even on their death certificates. So for that, I believe what I did was some incredible resource had looked at sex worker deaths and had created upper and lower estimates. And I visualized it with a circle, but it's a circle with like an ombre where the circle kind of diffuses. So you don't really have a precise sense of where the circle exactly starts and ends. But you do have a sense by comparing this circle to that circle that sex workers are people who are at higher risk of being murdered. And again, it's all about like that story of scale of like, if you can understand, the one thing you can take away from the data without question, regardless of where you start on the upper or lower bounds, is this community is more at risk? That data is still served its purpose because it's saying something about the allocation of resources and what we need to do. And then the accuracy of the data, I mean, it still matters. We still should be collecting that data. It's still worth putting out that data even though it's emphasized. I still think it was worth creating that visualization. Yeah. And actually, that's a point that often gets brought up in Du Bois. How accurate was the data? And it's interesting because we did a talk with a few weeks ago with two scholars from the Du Bois Center. And one of them said, what does it matter? Why are we questioning whether or not the data is precise or is exactly what was the situation at that time? What was being done was providing a story, information, context, and it's so much bigger. And the story that is factually true. It's absolutely factually true regardless of the numbers. He was depicting injustice that was factually correct. Yeah. And she said, actually, I thought it was really interesting because she said, I hate that question. And I don't like to really fall into how hyper-accurate it was this data in 1900. And I would also say, who gets pulled up on their accuracy? What are the people questioning other scholars of the time in 1900 to be like, oh, was your data exactly accurate? Why are they only coming after Du Bois to pose those questions? And that's still the case now. I mean, my first job in this country was working for Nate Silver, a man whose data gathering was not questioned with the same rigor that mine is. And I would say that his efforts in journalism are not only imprecise. They are dangerous for democracy to tell people who is going to win before people go out and cast their vote. Why is that a journalistic endeavor? I don't understand. It's an excellent point. So we wanted to mix it up a little bit. But what I found very, I think, poignant, but also really excruciating. I felt a lot of anger when I first saw this video for a lot of reasons. One is that, who hasn't dealt with a bad landlord? It's living in New York City as a New Yorker. You're dealing with bad landlords. You're dealing every day with some issue that you're trying to get fixed. You're paying exorbitant rents. And it's really, really difficult. But then when you see that statistic on the worst landlord in New York City versus the New York City Housing Authority, NYCHA, who houses officially about 340,000 New Yorkers, but unofficially that number could be well over half a million. And to see all of those housing violations and also thinking about our context here in New York City, Cooper Hewitt is in an area that's one of the wealthiest zip codes in New York City, if not the actual wealthiest, right? And if you just go a little bit further uptown to East Harlem where I grew up, it's the second largest concentration of NYCHA housing. And so this juxtaposition of neighborhoods, of wealth, of access, of who gets to live in a dignified way, for me when I saw this video, it just immediately became so visceral to me. So I wonder if you could talk a little bit about thinking through this video and why you juxtaposed the landlord with NYCHA. So the starting point was that I had a very bad landlord at the time. I had lived in the same apartment in Fort Green next to my neighbor who came today who actually bought me a Du Bois book that I really haven't forgotten. It was so lovely of you. And he has a great coffee company as well if anyone's interested in coffee. I was living with a very, very bad landlord. I'd lived there for five years in that apartment in Fort Green. I mean, I don't think this should actually be said. It's totally besides the point, but was a good tenant? Like I paid my rent on time, every single winter without fail, the heat would go out. And I think I'm actually shocked retrospectively how willing I was to tolerate some of the absolute inappropriate. She had keys to my apartment and would sometimes just wander in. Yeah. Yeah. She lived downstairs. It was a three-floor brownstone. She asked me to feed her cats when she went away, which I dutifully did. I don't know. Anyway, basically, around about the time that I was making this, I got a little letter slipped under my door, which was an eviction notice. Even though we like share the building together, she could have told me. And it's because her daughter was coming home from college and she wanted to give her daughter my apartment. And luckily at the time there was more of a conversation happening actually about housing rights, thanks to COVID. I mean, because of COVID. So anyway, I was feeling very frustrated and very pissed off at the time that I made it. I am somebody who I think it's quite clear, like my politics is very left. And I think that NYCHA should exist. I think the government has a responsibility to house the city's residents, especially those who are in incredibly vulnerable situations. But I also think it's really important that just because I'm super lefty, it doesn't mean that government gets a free pass. And that like, yeah, Jason Corn is horrific. And this also has to be addressed. So I think, yeah, that was kind of what I was trying to say, I guess. Yeah, I don't know. Yeah, I for me, I guess I thought it was really striking because NYCHA is everywhere. Yeah, through these very large campuses. And they're actually, that's how they're referred to as these like large scale campuses, where they're dealing with so many issues, like sanitation, asbestos, crime. I mean, even just having lighting in some of those buildings and some of those walkthroughs is disability access for people who have different, yeah, different abilities to be able to enter their own homes and exit their homes. And the fact that these are microcosms of of a very large expense of people that are living in New York, aging population, children, families, single people, but oftentimes these apartments have multiple, multiple generations in a one bedroom or two bedroom apartment. But they have, it's almost like, well, if you want to, like, I think about this a lot when anyone of my friends is talking about like a rental is like, it doesn't check all the boxes, but like, I can afford the rent or like, you know, we're working it out and it's okay. And like, I think I'm happy. So like, I'll stay here even though my landlord's coming into my apartment whenever they want, right, you rationalize it. And in a way, this is sort of saying like, well, you have, you know, this subsidized housing. So you should shut up. Yeah. And you should be okay with the fact that you are living in conditions that are inhumane in some cases and undignified. Exactly. Yeah, it's horrific. And I think that we listen, like I said, anger is really, really important, but it's also really exhausting to be angry all the time. And the ways in which you can go about redressing this is so incredibly bureaucratic, even when I was fighting my landlord, I live in the same building with her, like, what am I going to do? Like, and again, people who speak English as a second language, who people who don't speak any English at all, going about trying to fight for your rights is exhausting. So we also think that actually, if I was to critique this work, was I really sharing in conjunction with this, what you can do about if you live in one of these nature buildings, where your housing rights are being violated by the people that are supposed to provide those very rights to you. So that's a good question, right? You didn't provide the, what can you do? Maybe I did. I don't think I did. Yeah. But when you, when perhaps you can't do it all, right? So what, what do you hope people do with all of the content that you put out? What are you hoping is engendered by, you know, that one person who's either scrolling through Instagram or Google something and like your image or one of your videos pops up in, in a perfect world where as you're so conscious of someone, you know, maybe just giving you five seconds or 10 seconds to view your work, what do you hope the takeaway is? So there's like two extremes. I think the least ambitious and like at a bare minimum, I hope that people feel like seen and they feel like their experience has been, has been captured and like has been recognized and has been respected in that depiction. And I think to feel seen is no small thing, especially if you're part of a community that isn't normally seen or represented. And then a more extreme level like policy change. I don't know. Obviously that's pretty ambitious, but I don't, I really don't know. I think we spend so much time like depicting over and over and over again what's wrong. And I don't really know if it's contributing to change, but do you feel like, I don't know, I feel like we're edging towards a revolution of the proletariat and everything's getting really, really bad. So I don't know. Yeah. Maybe things will change. I'm sorry. But because what both in terms of the way that this video operates in intensity, and it goes back to intensity, because I don't think it's anger. I mean, anger is the intensity that comes out of it, but it's like how you're modulating an intense, emotional response, which sent us collectively down a rabbit hole, which I think is an effective practice to come out of like intensity, which was the the the prestige or the turn in the data that you present, which is here's this person. And I feel like conceptualizing anger towards a single individual is easier to do. And then the way you switch it into something much more systematic and technocratic, that this is a person, then here's a system. And then that almost embodies a system to then be like, ah, you know, the unveritable anger of it that then is like, wait, I love that depiction. It's like part one, like really micro human level, part two systemic. But I think part three has to be some kind of articulation of like, where do we go from here? You know, I think a lot about it was a piece that was published years ago in the New York Times about labor rights violations in New York nail salons. I don't remember what was like huge piece and it was showing how the vast majority of those workers are women, they are systematically abused in those roles underpaid. And I remember everyone came into work that week being like, my nails are bare, I didn't do them this week. And I was like, I don't know if that's the solution. And I think that that journalist had a real responsibility to not only highlight what was going on. And of course, it requires a change in labor rights laws and the way that those institutions are like the kind of vigilance over workers rights. But it's also like, you know, even if it's as small as if you can afford to get your nails done, make sure that you are tipping them cash in hand, like at the end, make sure it's these amounts like, and if you can't afford to tip that, don't go. I don't know, but like to just abstain, I don't think that's the solution. And to just put out this huge body of work that was read by so many people who then walked away with a different kind of conclusion from it. Surely you've failed in some way as a journalist. Maybe. This is an audio chart. Oh, God, it's this one. During puberty, voice testicles get bigger. And as they get bigger, their voices deepen. Tom will now demonstrate. Hi, my name is Tom. This is how I sound with testicles that are one milliliter. This is how I sound with testicles that are five milliliters. This is how I sound with testicles that are 10 milliliters. This is how I sound with testicles that are 15 milliliters. This is how I sound with testicles that are 20 milliliters. This is how I sound with testicles that are 25 milliliters. This is how I sound with testicles that are 30 milliliters. Sound. Sound. Sound. Sound. Sound. Sound. I really like this. Being someone who works in the visual arts, it is constantly a conversation of you're in a medium that is about looking. And thinking about how do you communicate to an audience who may not be able to look, may not have that sight. And so I was just really compelled about the audio component about this. How do you think of data not purely just as data visualization but different audiences and ways of communicating those kind of data stories? Yeah. I'm really glad you asked this question because I think about it a lot and I still think that I'm not doing a good enough job at it. But yeah, obviously data visualization massively alienates people who are blind or visually impaired. And I think that there is like an ease of going back to those same techniques where data sonification is incredible. And it's just because it hasn't been explored to the same extent that there aren't those same kind of go-to quick results. But I mean, it makes sense in so many ways. Even I was like exploring different ways that data sonification has been used in the past. And if you think about even something as simple as like a lab setting, when you're looking through a million different results and you're trying to find who has the result that means that they're unwell, it makes far more sense to sound an alarm for that one result rather than having someone scroll through visually all of the results like data sonification. Like every method of accessibility, every time I build on sounds into a visual, it doesn't just benefit communities who are blind or visually impaired. Everyone is like, Oh my God, I understand it so much better now. And so all I can say is that it's still work in progress. I've created tactile work in the past to try and work on that. I've created data sonifications. I'd love to just like keep on experimenting. And again, part of the reason why I did voice is because so much of my work is trying to like make the medium feel correct to whatever the data is. So even if I were to communicate something like how the smell of New York City has changed over time, could I like do that with like a data sonification thing where like you could smell urine on a map or something? I don't know. And you could know you're in like, you know, you're in Soho just from like sniffing the map. That'd be cool, right? Smell urine? No, definitely. There has to be a heat component. All year round. Who are you kidding? All year round. Yeah. But it mixes, you know, you get the fragrance of the spring, flowers and bloom and urine in the background. But I guess that goes to like storytelling because, you know, in your introduction, you're doing a number of different types of projects across a number of different types of medium. And the format, the media becomes kind of the message in what types of stories you tell. So do you think as you're sort of working on a podcast or working on a television show and trying to craft narratives, does that kind of do your ideas and projects bounce off of one another? Does a data visualization or a research project that leads to a data visualization and lead to a podcast? Or how do you kind of work cross platform? I'm thinking a lot about just time and how little time we all have. And like, is there just a way to like make it more efficient that I can just like take apart this one chunk of what I've done and kind of reutilize it in a different space? But yeah, do you feel like TV is quite a different beast? And it's been exciting to try to understand that. Yeah, but also quite weird that the TV show is with Amazon. And like, Amazon is the devil. So can we just transition to the next slide? Yeah. Oh yeah. Amazon is the devil. And I love how you just want to pull it, sir, for this body of work. Congratulations. And so why don't you talk a little bit about that? Because I can imagine there must be a lot of feelings, right? You won this incredible award for the guy who's playing my Patriot. Yeah. And I love this one. I mean, it's a series of data visualizations that you did. But this one in particular is incredible, right? Because everyone understands what a snow globe is. And to say that a tiny flake in a snow globe and put that is the wealth of an average US household in comparison to the Statue of Liberty for Jeff Bezos. I mean, that's just, again, thinking about the way to communicate information that's just so easily digestible and also impactful. This is a really incredible example of that. But then right now you're now you're in the situation where you're working with Amazon. How do you reconcile with that? I mean, as as people who work in cultural institutions and museums in general, that's something that institutions like are constantly grappling with like, who are the wealthy people where it's okay to take their their funding or it's okay to collaborate with them? I mean, those are difficult conversations that you must be having with yourself and had. And we certainly within the museum sector are having much more. So maybe I'll just start by talking about the piece. So it was a weird commission, right? It was like, can we find ways to visualize his wealth? And I guess a lot of journalism is supposedly telling you something new. But in this, like, everybody in this room knows Bezos is filthy rich. And so the goal is actually not to communicate anything new, but maybe to resensitize you to something that feels now like mundane, or like it's like a natural fact of the way of the world that is this incredibly rich person. But no, no, like, this is so obscene. And how can I make you feel the obscenity of that again? It's kind of interesting as well, because it's a data visualization without a single number on it, which is again, something that I weirdly strive towards because people hate maths. And so I'm trying to like, you know, get rid of the numbers on the piece, which is weird. And yeah, I feel like it was relatively successful. But yeah, I'm currently collecting a paycheck from Amazon. Also, I'll just say, Smithsonian also has received funding from Jeff Bezos. Oh, wow. Okay, we're all in the same bucket. We're all, I don't want you to think you're out there, as too. Well, I mean, it's really interesting. It's a project of a guy. Technically, don't work for the Smithsonian. Okay, okay. Well, I would say that like, maybe this feels too radical a position. I don't really think once you get to a certain level of money, I don't think it can ever be clean. Like, all of that money is corrupt and filthy. And I'm not excusing it at all. I feel like part of my thinking, I guess, was that if I if I ever hesitated to make this piece, then I needed to quit the job. But like, I was like, more than willing to just be like, I'm going to make it. And if I lose the job, then then it is what it is. Unfortunately, I guess it's a bit of a testament to how ineffective journalism is that Bezos isn't aware of this. Bezos doesn't give a shit. Like, he doesn't even know that I work for him. And it is like empire, it doesn't make a difference. I don't know. I don't know. I don't know if it's like, Robin hooding it to like take his money, and then use it to like subvert his his I, I don't know. I think a lot of people under American capitalism are walking very difficult moral lines. And part of my work and including the book that was mentioned in the in the introduction, the thing I'm really trying now to do is to be really radically transparent about those moral questions and about where I'm navigating them. So even something as simple as part of the goal of the book is to have people talking in a much more lucid and articulate way about our personal individual wealth and our individual income and like how it would change the dynamic in this whole room and this whole conversation. If the three of us in addition to our like assumed race and our assumed gender being visible also had like labels above our head with our annual take home income. And what does that say about the power dynamic between the three of us and the way that you will view us? I don't know. All I can say is that honesty feels like the first step for me to say this is fucked up that I worked for this guy. And then I don't know. I don't know. Yeah. Well, I think that's a perfect point to open it up to questions. Yes. Oh, will someone be walking around with them? Oh, there we go. We'll have a mic. And thank you for coming out on like a Friday night. Yeah. You're gonna have to stand. I wasn't prepared for that. Hi, my name is Azar. First of all, for so many brilliant things you said, you kept saying I don't know. And I was like, you know, you've been telling us the whole night. I'm also a storyteller. And I struggle with the idea of do I tell a mainstream story that happens to happen to a brown man? Or do I tell a brown person's story and hope that somebody listens? And you kind of touched on that. And I just wanted to find out how did you make your decision? Yeah, what helped you what didn't help you that I could steal maybe. Thank you. It's a really good question. I think for anyone who has any kind of like, access to an identity that is marginalized, you know that that is formative of everything. But it's also not the only thing of who you are. And I think if you can try to like, just be honest about the times at which that identity is like, really front and center and the times at which it takes a backseat, the audience will find you like, I don't know, there is an audience for for that. So I don't know. I think it depends on what is what is like the subject that feels front and center for the story that you want to tell. And then from there, I think you're figuring out your identity's place in that story, which is really difficult to unpick. Yeah, I don't know. And again, I'm thinking about in the process of writing this book, like, like, I don't know how much did my identity shape my upbringing? And obviously it did. But it's a really difficult act to unpick some of that. Yeah. Yeah. I didn't really answer. I'm doing that. I don't know thing. I know I was a guy gave a great answer. Yeah, you're welcome. Yeah. Hello, hi. My name's Surya. I'm a qualitative researcher. I do UX research, but anthropology by training. And I wanted to ask a question about kind of interpretation. So I guess kind of to the point that you're just talking about about how, you know, if you're as a person situated, how you how others interpret you. But I think in when it comes to data visualization, especially your style of data visualization, where it is also very akin to art. And so in the art world, you have that kind of question of for the audience, their interpretation of your piece and for you, your data visualization plays a certain kind of role in how it's kind of actioned and internalized. How do you navigate the way that your art is or your visualizations are both, you know, is kind of taken in and and navigating that kind of space of I'm presenting this kind of nebulous data that may has a certain context to it because of the way that you do present it. And you know, in the case of US even this piece right here and and having the Statue of Liberty and very much situating it locally. But at the same time, not really having the breadth of context that is often missing from, you know, survey data and things like that or even data visualization is really, you know, built off of how that person kind of internalizes your work. Such a good question. Thank you. I would say that I mean, all artists, I guess, take a different stance on this of like whether, you know, I'm trying to tell you this one thing, and that's what you need to take away from it. I think it really does depend on the subject that I'm depicting. So really weird example. Again, I looked at this morning for some reason, I looked at like the percentage of adults that experience rectal bleeding. And however you interpret the causes of that, I don't think it's necessary because I'm not trying over time. It's just like one spot statistic. I think that being open to interpretation is kind of OK if it means you go off and like do some querying of your own. If I'm depicting data on the efficacy of masks during the peak of the pandemic, actually my main concern is the possibility of misinterpretation. And there becomes a really urgent need for you to walk away with not only a set of numbers, but a very clear understanding of cause and effect. Same thing with vaccination. And so I think for certain subjects, particularly when it comes to health, my concern is about eliminating certain types of misinterpretation that could do harm. And then whatever you're left with, feel free to interpret it kind of however, however you like, I guess. It really depends on the subject, basically. Yeah. Yeah. Thank you. Any other questions? Hi, I just want to whenever I go to doctor's office, I am asked to, for some reason, they always want to know if I'm a male or female. I always cross it out. I've been doing this for several years and I write in like, well, I write a screen, but you know, it was very interesting because I'm I'm sure we all experienced that also and I encourage you to do the same. But then I was registering for this tonight. And I was for some reason, I was asked about my gender. And there was a list of 12 different genders that I should identify with. Some of which I thought were overlapping. And I wonder where we get to a point where we're not we're just splitting too many penny. You know what I mean? I do. I do. And also, each of those categories for like data interpretation processes purposes, sometimes the addition of extra categories means that you're left with sample sizes that are so small that there's actually nothing that you can do with them. However, I would also say that the act of filling out forms is also a political act, right? Not just the information design, but the act of filling out. And I believe there are people that will come to those 12 genders and will see themselves for the very first time rendered on that page and will feel profoundly relieved and moved to see that. And I think that alone is a very good reason for keeping those 12 on there. And I think again, it depends, right? Like I would say that sometimes in the doctor's office, and this all comes down to like our relationship with the people who are gathering that data. Have you had positive experiences with medical professionals in the past? Or have you had negative experiences? Do you believe that that ticking that box will help them to provide better care for you or worse care? And like, will it mean that they dismiss your concerns? And so again, like, it's about your relationship with the figures of authority. And sometimes again, to produce those 12 genders actually can help people's relationship with both this institution and the people who run it. And it might alienate others. But I would say that maybe sometimes some of the people that get, some of the people who feel frustrated by some of those things are people who, and I'm not suggesting this is the case for you at all, but sometimes some of that act of some of why it bothers people is the discomfort of the change and the upset of what's familiar and what is known to us. And sometimes discomfort is a good thing. Here, I did it to identify my job. I'm sure there was a prefer not to say option, though. Yeah. Yeah. But why was that? Why are they even asking? Because I think I think institutions like this in order to better serve communities. I mean, I really don't want to speak on your behalf, but I'm going to go ahead and try. It's really important to know who your community is, right? And if, for example, one of the things that that yields is that there's a vast number of people who are in this event tonight who actually identify as non-binary, for example, but might not feel comfortable telling to their neighbor in the seat next to them and saying that, but they do feel comfortable saying in a private form. That could affect something as simple as the the bathroom designs in this building. And so that is really valuable information. It could affect the curation process of maybe we need to have more exhibits here that are done by artists who are non-binary to make sure that people are seeing work that's represented here. I think we do. I think we do sometimes because the reality is, and this goes back to I agree, but like should be do like so. I agree. However, to go back to like Arabness, I'm just going to speak from my personal experience because I'm a cis woman, so I don't want to speak to gender. As an Arab woman, we have no fucking clue how many Arabs there are in this country, right? So I can say, yes, of course this museum should have an exhibit that's done by an Arab person, but the museum's like, well, how many people are going to come to that? Is there really an audience for that? Is there really going to be demand for it? And a critical part of advocating, and this is where it gets really, really difficult, is that a critical part of advocating for yourself is the demonstration of need. However, I would also say that the over-reliance on data sometimes can be really problematic where I would say even if 0.5% of people in this room are non-binary, that shouldn't be a reason for not having an exhibition in here that's done by a non-binary person. However, the realities of fundraising and going to people sometimes requires saying, there is a community for this, please give us money for it. Like, do you want to say something? This is correct. Just as a museum person, this is correct. And oftentimes, we are forced, actually our director can speak a little bit, but we're forced to provide cold, hard numbers or cold, hard facts to be able to get support to do things that are maybe outside of what we've done normally or new for us, or even to just continue what we've been doing already that has been successful. We have to show why we need to do what we need to do. But our... Yeah, just to say, Mona's answer is spot-on. You're hired, you can come over with us anytime. But just to say that, I also agree with you. Do we really need to do all of this to prove that this is what we need to be doing? And I think this is the unfortunate reality that connects with the point that we were talking about before, that we live in an imperfect system. The system is profoundly fucked up, right? But we need ways of convincing others sometimes to do that, whether you agree or not that we need to go through those systems. But I need to use it sometimes to convince others that do not agree with maybe the fact that I don't think that I would need to be convincing them. So it's complex and it's imperfect, I would say. I have a complicated relationship with data myself. I hate when I have to fill that kind of stuff. And then add to that the level of the federal government, which is like a whole other other sort of like level of... So it's very complicated. So for me, as long as we're using it and to subvert it a little bit and to provide what I think we should be providing, it justifies its use as much as I sometimes wouldn't be my first choice. Because that's my most honest answer that I can give. Oh, now there's some hums, oh my gosh. Okay. Sorry, I want to reach the back of them. Hello, my name is Bill. I did. And now I can see you. Oh, sorry, I'm sorry. If I could just make a suggestion for the future, I think most of us came out to see you and the other guests. But it turned out to be like an audio cast. Oh, I'm so sorry. Because nobody could see you. But anyway, I was sort of drifting in and out when you brought up Fort Green. And I was a Fort Green resident for about 20 years. And I was a landlord, as a matter of fact. And so when you mentioned that, I started just thinking back, uh-oh. But I realized... No, he's okay. He was definitely not my landlord yet. No. But I realized chronologically it was impossible, so that was okay. But what I really wanted to mention was you talked about the Middle East and North Africa. And those are entirely bullshit, if I may say. And they go back to the last century, maybe even a little before, it was British colonial construction to make sure that it is separate from Africa. And if you look at the map, it's interesting because the Middle East is separated from Africa by the Red Sea, which is about 90 miles. And it's not even a complete separation, because if you go further north, you can actually go around. You know, it's like a land bridge. Okay, so the Middle East is not part of Africa, definitely, according to the mainstream. But Madagascar, 500 miles away from the coast of Africa, is African. So, think about that. I mean, it honestly brings us right back to the DNA data that we were discussing, of what on Earth does it mean to describe somebody as 23% black? Like, we have lost sight of the fact that races are construct, and we are treating it as if it is a scientific fact. Which is scary, yeah. Thank you, Bill, and I'm sad you wasn't my landlord. I'm sure you would have been a great landlord. I loved your idea. I do data all day, and most of what I do... Can I ask in what capacity? I'm a Supervisory International Trade Analyst, and you may have seen some of my work in the last couple years. I won an award for part of it, the Uyghur Force Labor Prevention Act. I created the three scenarios where we could actually try to do some of that law. Well, that was my initial report. So, I used lots of other people's data, everything else, but the initial saying was whether we could do that law and get that law through. That was my three scenarios. Though they didn't go with my big one, which I had learned from the private sector always throughout three scenarios. One really easy one, one that you can probably get, and one that's a dream. No trade with China was not going to fly. But that was my third one. So, you know, I was pretty happy we got A and B. So, you know, but that was all done with missing data. And, you know, there were some very good people who won a poll. It's also who really did some data, and they communicated where I could find some data that they had done. Other people did. Sheffield Hallam University, communicated with me, the anti-community people. When you're doing data, you go with anybody. You don't care whether great or not. You just care that their data is clean. Oh, I don't know about that. But yeah. You care that their data is clean. Yeah. The cleaner the data, the better it is. You know. Even that adjective, though, for numbers, feels deeply problematic. But keep going, keep going. So, what I love is that you had that one description where you were talking about the big circle for missing data. Yeah. Because most of it is missing data. So, for something like the Uighurs, it's going to be missing data. Because these are missing people. They're just not shown. And they're disappeared. And so, you have to be able to show missing data. And you have to find ways to visualize those things. So, I really love that. And I do that. I understand why people take large amounts of data. Because the data is how you get laws passed. How you get changes. You need that data constantly. Otherwise, because there's a limited pie of, you know, the government has a limited pie. I mean, it'll be a bigger pie if they text Basil's, but uh-huh. Yeah. But, you know, there's a pie. Yeah. And you have to divide that pie. And you have to figure out what your priorities are in that pie. And just see where that pie is going to see what's actually happening with it. So, those are all important things that data can do. And like I always say with the MENA issue, just go with the Arab League. I don't know. I don't know. Why they have a really nice thing, the Arab League. They just pick out their countries that belong in the Arab League. And they've got their own little thing. They've already- But then, how would you honor something like census data? The Arab League is a political body. The Arab League is a political body. Just put that in there. I wouldn't check Arab League on my census form for my agencies. Well, you just put Arab- Ethnicities. But there are people who are ethnically Arab without necessarily speaking Arabic, right? Hmm. I mean, like, you've got Sudan where, you know, they're ethnically, they're, you know, if you, most people consider them racially black, but they're Arab and they're in the Arab League. So, and you've got Moroccans, Indonesians. And I mean, it's really more of a cultural definition and- I don't know. I don't know. I don't know. I mean, who's in the Arab League? Who's in the Arab League who doesn't speak Arabic? I don't know if this particular night is the night of the time and the place. No, true. But a fun, but a fun one. Defining Arabness right this second. But a fun one. Yeah, yeah, yeah. It's a tough call because how do you define a cultural group that is spread from- I think a big part of it is letting those communities define it. I think, yeah. Like I said. Yeah. Anyway, thank you. Thank you. One more question over there. Okay. So, if you, okay, if you use a number of different data sources. So, sometimes you run into the issue that these different data sources do kind of lead to different conclusions. Yes. Also, a second question is that, like, if you're presenting data, how do you avoid the fact to not imply causality by a correlation? So, it's like a really important thing that people will, you know, basically see, like, increase in X that is caused by Y. So, I guess, how would you approach that problem? And, you know, are there any, like, data sets that you like working with or don't? And then how do you run into this issue that you look at something and then, actually, you would have a different conclusion if you looked at something from a different source. Yeah, yeah. So, on conflicting sources, it's a really good question. I think it's really important to look into who is collecting the data, what their goals might be. Dare I give an example that veers into Arabness potentially? I mean, it's actually an example about Muslims. So, there was a statistic that was cited that was something like, I don't know, 60% of Muslims believe in jihad and it was, like, posted all over the Daily Mail in the U.K., it was from the survey body. The survey, I, like, looked into the survey and the survey was many, many, many questions. The first question on the survey was how do you define jihad? And the vast majority of respondents selected my personal peaceful struggle to be closer to God. So, that automatically changes the definition of people supporting jihad. It was also an online form that anyone could opt in for. So, no understanding of whether or not those people are Muslim or what. Fact number three, crucial fact. The survey was done by Kellyanne Conway's polling company. So, there are steps that you can do to, like, figure out what are, what are the resources. And honestly, I would say the tricky thing is that, to give the example, again, of, like, sex workers or different marginalized communities, sometimes it's very specific bodies that are collecting that data where they are both far more accurate because they have built up the trust with that community to increase response rates. But it also means sometimes they're incentivized to sometimes inflate some of those numbers. Gun, gun, yep. Yes. Yes. Oh, sorry. You mentioned so many nuances. They're super important. But how do you distill that complexity to this kind of visualization? Yeah, yeah, yeah. Colors, images, and easy to digest. So, let me give a really concrete example that I've never done, but, like, just to respond. Like, tell me, I don't know, one thing that you're interested in. Anything. Could be, like, what you eat for breakfast, aren't I? I mean, like, economic inequality. That's really interesting. Okay, economic inequality. Or, like, social issues. Okay. So, let's say there was a survey that was done to measure economic inequality, and it was asking people, have you had trouble paying for your groceries in the past month? And there's two different data sets, right? One of the data sets says 20% of Americans are struggling to pay for groceries, and one of them says 2%. One potential technique for visualizing that is to show 20% and 2% as two different bars, right? Like, let's use this. So, there's a bar at the top that says 20% on the pink, and a bar at the top that says 2% that's much smaller. And then down from those bars is a dashed line that now shows, either with a circle or with a square, the number of respondents that were included in that survey. And maybe the 2% survey had 200 respondents, and the 20% survey had 10,000. And so, now I'm presenting both sets of statistics to the audience and also giving them the opportunity to incorporate for themselves which data set do you feel is more reliable. And imagine if on that same thing I'm also showing the breakdown we're out of time. We're also showing the breakdown of, like, how much money was put into that survey? Am I also being able to show, like, with little state outlines which were the communities that were interrogated? Was this truly across the country? Was it only certain places? So, in an ideal world, I would present people with more than one data set. The thing that me and two boys to, like, try and put a bow on it were struggling with, oh, god, were struggling with all the time is this idea of fatigue on the part of the audience. I just assume that if I'm going to give you two or three data sets, you're going to be like, what, you want me to figure out? That's on you. You need to tell me which is the data that I need to trust and just tell me what the information is saying. So the thing that I try to do that I also think two boys tried to do is to step out the information. So maybe I just create one visualization that shows you poverty rates in the U.S. And if you want, you can use the next slide, you can scroll down, you can turn the page on the book and look at those information resources, those various information sources if you want. But my starting point is going to be to assume you probably don't actually want to know that. Yeah. Thank you. Thank you so much. Thank you.